How to use Python in Penetration Testing and Cybersecurity.
You need basic Python coding skills and basic knowledge in cybersecurity and penetration testing to successfully complete it. You are welcome to take the course even if you do not meet the criteria, provided that you can get yourself on track on-the-go.
You’re a programmer and you want to get into cybersecurity. You’re in the right place because this course will teach you how to combine or use Python programming to greatly expand your skills as a cybersecurity professional.
Penetration testers and cybersecurity analysts often get into the situation of having to do repetitive work that takes away precious time from their active focus. Examples of such situations can be scanning targets, doing intensive enumeration, subdomain discovery, reconnaissance, and more.
This is where programming languages like Python become extremely useful. Mastering Python allows you to take your hands off these repetitive and mundane tasks, automate them through code (and make them faster) so that you can focus on the really mentally challenging aspects of your penetration testing and cybersecurity projects.
This course is the second in a two-course series, that together will develop your Python skills and enable you to apply them in cybersecurity and penetration testing.
Who this course is for:
Developers who wants to break into cybersecurity and penetration testing.
Cybersecurity professionals with basic Python skills who want to get their work done more efficiently.
Anyone with basic coding skills who wants to learn Python for penetration testing.
1 section • 11 lectures • 1h 56m total length
Lessons11 lectures • 1hr 57min
Gathering Information – Grabbing Banners, Hostname and IP Lookup11:36
Building a Basic Port Scanner using NMAP in Python10:51
Grabbing Screenshots with Python11:21
The Socket Module for Network Communication – A TCP Server-Client10:02
The Scapy Module for Network Traffic Sniffing and Manipulation15:54
Attacking Web Forms with requests and BeautifulSoup in Python16:34
2 sections • 9 lectures • 42m total lengthExpand all sections
Introduction3 lectures • 7min
Introduction to the MetasploitPreview01:16
Metasploit6 lectures • 36min
Attack simulation: EternalBlue07:23
Metasploit database feature05:04
Receiving a reverse shell with msfvenom12:10
Upgrade normal shell to a meterpreter shell03:24
Basic Linux skills.
Learn ethical hacking using the world’s most used penetration testing framework. MetasploitFramework is a must if you plan a career in cybersecurity. It’s even pre-installed in the Linux distrubutions like Kali Linux or Parrot OS.
This course is different then most of the courses on Udemy. I strive for my courses to be no longer than one hour and to provide you only with important information, the ones that you will most often use in practice. If you like this approach, I invite you to enroll for the course.
This tool is frequently utilized by different types of hackers. Malicious intruders and skilled, ethical hackers. Keep in mind to use it ethically and legally.
Metasploit is a very powerful framework if you know how to use it. You will learn how to set up your environment and connect to target machines via a VPN (Virtual Private Network). You will learn about different versions of Metasploit and msfconsole – the main interface of the Metasploit. You will simulate an attack on Windows system using one of the most famous exploits – EternalBlue. You will know the difference between vulnerability, exploit and payload. I will teach you how to generate payloads for various operating system and configruations with msfvenom. You will run simple scans using Nmap, connect to a remote host via SSH service, create a HTTP server in python and change permissions of files among others. I will show you how to receive a reverse shell connection, execute commands remotely on the target system and upgrade a standard shell to the meterpreter shell which offers much more options. I hope you will enjoy this course and learn something useful. Working with Metasploit often requires knowledge about other tools, but in this course Metasploit is always in the center of atterntion.
Note: This course is created for educational purposes only. You shall not misuse the information to gain unauthorised access.
Who this course is for:
Anybody interested in learning ethical hacking / penetration testing.
Learn how to identify and avoid phishing scams and other forms of online fraud.
Understand the risks of social engineering and how to protect yourself from these tactics.
Understand the potential dangers of sharing personal information online and the importance of maintaining privacy.
Learn how to create strong, secure passwords and how to manage them effectively.
Learn how to recognize and avoid online predators and how to report suspicious activity.
Understand the risks of sharing sensitive information online and how to protect against data breaches.
Learn how to use privacy settings on social media and other online platforms to control who has access to your information.
Learn how to stay safe while using online banking and shopping sites, and understand the importance of keeping your device free from malware.
This is an introduction course. No prior knowledge is needed.
Welcome to Staying Safe Online: A Beginner’s Guide to Cybersecurity. This course was completely written and created using AI technologies, and will teach students about the different types of cyber threats and how to protect yourself from them.
This course will cover the basics of online security, including the different types of threats that exist, such as malware, phishing, and social engineering, and how to protect against them. Throughout the course, learners will gain a deeper understanding of the potential dangers of sharing personal information online and the importance of maintaining privacy. They will learn how to identify and avoid phishing scams and other forms of online fraud, and understand the risks of social engineering and how to protect themselves from these tactics.
The course will also cover best practices for creating strong, secure passwords and managing them effectively. Learners will also learn how to recognize and avoid online predators and how to report suspicious activity. In addition the course will also cover the risks of sharing sensitive information online and how to protect against data breaches. The course will cover the use of privacy settings on social media and other online platforms to control who has access to your information. Finally, the course will cover how to stay safe while using online banking and shopping sites. The course will be delivered in an interactive format, including videos, quizzes, and hands-on exercises, to ensure that the material is engaging and easy to understand.
This online security course is intended for individuals of all ages and backgrounds who want to learn how to protect themselves and their personal information while using the internet. The course is designed to be accessible to learners with varying levels of technical knowledge, and no prior experience in online security is required. The course is suitable for anyone who uses the internet for personal or professional purposes, such as browsing, social media, online banking, or e-commerce. This course is intended to be a resource for anyone who wants to stay safe while navigating the digital world and want to learn best practices for maintaining online security.
AI is speeding up discoveries about the universe and helping to hone the search for life within it.
Why it matters: Many astronomers spend a large amount of their time combing through data collected by telescopes.
AI and machine learning can be used to quickly pick out intriguing parts of a dataset, making it less likely that astronomers will miss something important.
“With astronomy and these huge datasets, there’s always a concern that you missed something, or that you didn’t know enough about the objects you’re trying to study,” astronomer Chris Impey of the University of Arizona tells Axios.
What’s happening: Last month, scientists revealed an image of the black hole at the center of the galaxy M87 that was reprocessed using a machine learning algorithm. The sharpened image should allow scientists to more accurately estimate the black hole’s mass.
Scientists have also used artificial intelligence to make it easier to analyze vast amounts of data gathered by gravitational wave detectors like LIGO, picking out the ripples in space and time created by collisions between black holes or other dense objects.
Now, researchers are using algorithms to figure out characteristics of the objects that produced the gravitational waves in the first place.
AI is also being used to pinpoint newly forming planets around young stars.
The intrigue: SETI (the Search for Extraterrestrial Intelligence) efforts could behelped by AI and machine learning, which are particularly adept at picking out patterns in large sets of data.
SETI — which focuses on trying to pick up radio signals emitted by technically advanced societies — generates huge amounts of data.
“The datasets for SETI endeavors are really massive,” SETI Institute CEO Bill Diamond tells Axios. “We generate many tens of terabytes a day.”
The institute and other organizations announced last week that the Very Large Array in New Mexico will join a new experiment to search for radio signals emitted by advanced alien societies, which will rely on machine learning tools.
“When the compute is completely outfitted for that project — which is right now about halfway — we’ll be generating seven terabytes per second of data, so extraordinarily huge amounts of data,” Diamond said. “And the only way we’re going to be able to parse that data to look for interesting phenomena is with machine learning techniques.”
Yes, but: Some worry these tools could spit out false positives that wouldn’t otherwise be an issue if humans were analyzing the data.
“It’s possible [with] these huge datasets that it could throw so many candidate anomalies at you that you just couldn’t keep up and you end up being buried in the candidate anomalies rather than finding new phenomena,” Impey said.
NASA has also been taking a close look at AI, but a report published last week by the Office of Inspector General stresses there are risks to wide adoption of these tools, including possible cybersecurity threats.
What to watch: Scientists are already training an AI algorithm to help create sharper photos when the Vera Rubin Observatory — tasked with learning more about the nature of dark matter and other science goals — comes online in the coming years.
Machine learning has been used to find streaks created by satellites streaking overheadin Hubble Space Telescope images, and in the future, algorithms could be used to remove satellite streaks from telescope photos as well.
Many people are suspicious of artificial intelligence. They don’t understand how computers can ‘learn’ and make intelligent decisions. Yet, the concept of AI can be understood by anyone.
Machine learning and deep learning are the two most important concepts in making AI possible. The two terms are often conflated, but they describe two fundamentally different methods with their own areas of application.
Deep learning vs. machine learning: What are the differences?
Different areas of application
Both machine learning and deep learning are subsections of artificial intelligence. Both approaches result in computers being able to make intelligent decisions. Deep learning, however, is a subtype of machine learning, as it’s based on unsupervised learning.
In both cases, this intelligence is limited to individual areas of application. We speak of so-called “weak artificial intelligence,” as opposed to “strong artificial intelligence,” which would have a human-like capacity to make intelligent decisions across many areas and situations.
Both technologies rely on large quantities of data being available for systems to learn from. That’s where the similarities end, though.
Deep learning vs. machine learning: What are the differences?
Historically speaking, machine learning is the older and simpler technology. It works with an algorithm that adapts when it receives human feedback. One requirement for making use of this technology is the availability of structured data. First, the system is fed structured and categorized data, and in this way, it understands how to classify new data of the same type. Depending on the classification, the system then carries out programmed activities. For example, it can distinguish whether a photo features a dog or a cat, and allots the files to their respective folders.
An initial application phase is followed by the optimization of the algorithm using human feedback – for this, the system is informed about any incorrect classifications and the correct categorizations.
With deep learning, structured data isn’t necessary. The system works with multi-layer neural networks that combine different algorithms that are modeled on the human brain. That’s why the system can also process unstructured data.
The approach is most suitable for complex tasks where not all aspects of objects can be categorized beforehand.
Important: In deep learning, the system finds suitable differentiation characteristics in the files by itself, with no need for any external categorization. In other words: training by the developer isn’t necessary. The system itself considers whether to change classifications or produce new categories based on new input.
While machine learning can already work with a manageable data pool, deep learning requires much more data. For the system to produce reliable results, more than 100 million data points should be available.
The technology for deep learning is also more costly to implement. It takes more IT resources and is significantly more expensive than machine learning, meaning that – for now, at least – it isn’t an option for mainstream businesses.
An overview of the differences between machine learning and deep learning
Manageable data pool
More than a million data points
Requires human trainers
A changeable algorithm
Neural network made of algorithms
Field of application
Simple routine activities
Different areas of application
Machine learning could be seen as a precursor to deep learning. In fact, all tasks that can be carried out by machine learning can also be processed by deep learning. It shouldn’t even be necessary to weigh up deep learning vs. machine learning.
Since deep learning requires significantly more resources, though, it isn’t an efficient procedure. The areas of application for both technologies are therefore clearly separated, and if machine learning can be used then machine learning will be used.
Using both technologies provides an enormous competitive advantage to companies, as both machine learning and deep learning are far from standard in the day-to-day business environment.
Areas of application: Machine learning
Online marketing: What marketing measures create results? Humans are generally not very good at surveying large quantities of data and delivering reliable estimations. This is where marketing analytics tools, based on machine learning, come in. These can evaluate existing data and make reliable forecasts as to the kind of content that would lead to conversions; what content customers want to read; and which marketing channels primarily result in a purchase.
Customer support: Chatbots can be based on machine learning. They are oriented towards keywords included in the user’s query, and can guide customers to the information they are looking for through queries and yes/no questions in the dialog.
Sales: If it works for Netflix and Amazon, it can also be used in sales. Thanks to machine learning, systems can successfully predict which products and services existing customers might also be interested in. Here, the systems are able to provide very detailed recommendations which, in the case of large product ranges and highly customizable products, simplify sales.
Business intelligence: Machine learning can also be used to visualize important business data and to make forecasts easier to understand for the human decision-maker.
Areas of application: Deep learning
IT security: Unlike with machine learning, IT and cybersecurity systems that are based on deep learning not only recognize pre-defined dangers, but also new, hitherto unknown threats, as these are picked up as anomalies by the neural network’s pattern recognition. The effectiveness of security measures can be dramatically increased with the help of deep learning.
Customer support: Chatbots that are based on deep learning understand human language, and don’t rely on certain keywords being used. The dialog is much more efficient and the solution offered is more accurate.
Content creation: With deep learning, content creation can be automated. If enough content is available as a data pool, the system can create new content from it and perform translations autonomously.
Speech assistants: Digital assistants like Siri, Alexa, and Google are based on deep learning. In business contexts, too, the first speech assistants are now being used. For example, users can ask them in a natural way to place orders, send emails, create reports, or carry out research.
Beyond the areas of application listed here, both technologies can also be used in many more areas, such as in medicine, science, or mobility.
Machine learning involves enabling computers to learn without someone having to program them. In this way, the machine does the learning, gathering its own pertinent data instead of someone else having to do it.
Machine learning plays a central role in the development of artificial intelligence (AI), deep learning, and neural networks—all of which involve machine learning’s pattern- recognition capabilities.
How Machine Learning Evolved
Modern machine learning has its roots in Boolean logic. George Boole came up with a kind of algebra in which all values could be reduced to binary values. As a result, the binary systems modern computing is based on can be applied to complex, nuanced things.
Then, in 1952, Arthur Samuel made a program that enabled an IBM computer to improve at checkers as it plays more. Fast forward to 1985 where Terry Sejnowski and Charles Rosenberg created a neural network that could teach itself how to pronounce words properly—20,000 in a single week. In 2016, LipNet, a visual speech recognition AI, was able to read lips in video accurately 93.4% of the time.
Machine learning has come a long way, and its applications impact the daily lives of nearly everyone, especially those concerned with cybersecurity.
Machine Learning Definition: Important Terminologies in Machine Learning
All types of machine learning depend on a common set of terminology, including machine learning in cybersecurity. Machine learning, as discussed in this article, will refer to the following terms.
Model is also referred to as a hypothesis. This is the real-world process that is represented as an algorithm.
A feature is a parameter or property within the data-set that can be measured.
This refers to a set of more than one numerical feature. It is used as an input, entered into the machine-learning model to generate predictions and to train the system.
When an algorithm examines a set of data and finds patterns, the system is being “trained” and the resulting output is the machine-learning model.
After the machine-learning model has been trained, it can receive an input and then provide a prediction regarding the output.
The target is the value the machine-learning model is charged with predicting.
When a machine-learning model is provided with a huge amount of data, it can learn incorrectly due to inaccuracies in the data. This is called “overfitting” the system.
In an underfitting situation, the machine-learning model is not able to find the underlying trend of the input data. This makes the machine learning model inaccurate.
Machine Learning Meaning: Types of Machine Learning
There are a few different types of machine learning, including supervised, unsupervised, semi-supervised, and reinforcement learning.
With supervised learning, the datasets are labeled, and the labels train the algorithms, enabling them to classify the data they come across accurately and predict outcomes better. In this way, the model can avoid overfitting or underfitting because the datasets have already been categorized.
In unsupervised learning, the algorithms cluster and analyze datasets without labels. They then use this clustering to discover patterns in the data without any human help.
In semi-supervised learning, a smaller set of labeled data is input into the system, and the algorithms then use these to find patterns in a larger dataset. This is useful when there is not enough labeled data because even a reduced amount of data can still be used to train the system.
In reinforcement machine learning, the algorithm learns as it goes using trial and error. The system is provided with input regarding whether an outcome was successful or unsuccessful.
Machine Learning Explained: How Machine Learning Works
Machine learning is based on the discovery of patterns and makes use of the following processes:
The decision process involves the machine-learning model making a classification or prediction based on input data. These then produce estimates regarding patterns found in the data.
With error determination, an error function is able to assess how accurate the model is. The error function makes a comparison with known examples and it can thus judge whether the algorithms are coming up with the right patterns.
Model Optimization Process
In the model optimization process, the model is compared to the points in a dataset. The model’s predictive abilities are honed by weighting factors of the algorithm based on how closely the output matched with the data-set.
Role of Machine Learning in Cybersecurity
Machine learning is already playing an important role in cybersecurity. Its predictive and pattern-recognition capabilities make it ideal for addressing several cybersecurity challenges. It can collect, structure, and organize data and then find patterns that can be used to better inform decisions.
For example, a machine-learning model can take a stream of data from a factory floor and use it to predict when assembly line components may fail. It can also predict the likelihood of certain errors happening in the finished product. An engineer can then use this information to adjust the settings of the machines on the factory floor to enhance the likelihood the finished product will come out as desired.
Machine learning can also help decision-makers figure out which questions to ask as they seek to improve processes. For example, sales managers may be investing time in figuring out what sales reps should be saying to potential customers. However, machine learning may identify a completely different parameter, such as the color scheme of an item or its position within a display, that has a greater impact on the rates of sales. Given the right datasets, a machine-learning model can make these and other predictions that may escape human notice.
Real-world Applications of Machine Learning
Machine learning is already playing a significant role in the lives of everyday people. In many ways, some of its capabilities are still relatively untapped.
Speech recognition is used when a computer transcribes speech into text or tries to understand verbal inputs by users. Speech recognition analyzes speech patterns and uses feedback as to whether or not the output is accurate. In this way, a speech recognition machine-learning model can tell the difference between similar sounds, such as those associated with “f” and “s.”
For example, when someone asks Siri a question, Siri uses speech recognition to decipher their query. In many cases, you can use words like “sell” and “fell” and Siri can tell the difference, thanks to her speech recognition machine learning. Speech recognition also plays a role in the development of natural language processing (NLP) models, which help computers interact with humans.
Customer service bots have become increasingly common, and these depend on machine learning. For example, even if you do not type in a query perfectly accurately when asking a customer service bot a question, it can still recognize the general purpose of your query, thanks to data from machine -earning pattern recognition.
Computers are able to “look” at things and categorize them. They can then use these categories to make decisions. Using machine vision, a computer can, for example, see a small boy crossing the street, identify what it sees as a person, and force a car to stop. Similarly, a machine-learning model can distinguish an object in its view, such as a guardrail, from a line running parallel to a highway. It can then use that information to steer a vehicle.
Recommendation engines can analyze past datasets and then make recommendations accordingly. This machine-learning application depends on regression models. A regression model uses a set of data to predict what will happen in the future.
For example, a company invested $20,000 in advertising every year for five years. Each year, sales went up by 10%. With all other factors being equal, a regression model may indicate that a $20,000 investment in the following year may also produce a 10% increase in sales.
Automated Stock Trading
With the help of AI, automated stock traders can make millions of trades in one day. The systems use data from the markets to decide which trades are most likely to be profitable. They can then execute trades in less than a second.
Challenges Ahead in the Machine-learning Arena
Machine learning, like most technologies, comes with significant challenges. Some of these impact the day-to-day lives of people, while others have a more tangible effect on the world of cybersecurity.
Impact on the Jobs Market
Many people are concerned that machine-learning may do such a good job doing what humans are supposed to that machines will ultimately supplant humans in several job sectors. In some ways, this has already happened although the effect has been relatively limited.
For example, the car industry has robots on assembly lines that use machine learning to properly assemble components. In some cases, these robots perform things that humans can do if given the opportunity. However, the fallibility of human decisions and physical movement makes machine-learning-guided robots a better and safer alternative.
Also, a machine-learning model does not have to sleep or take lunch breaks. It also will not call in sick or get into disputes with others. Some manufacturers have capitalized on this to replace humans with machine learning algorithms.
However, the fear may be somewhat overblown. While machine-learning can do things humans cannot, it also does jobs that humans would rather not do. The same human resources that machine learning “replaced” can, in many cases, be used to accomplish other tasks—tasks that machines cannot do. These include making managerial decisions on the fly, and serving as mentors, teachers, artists, and other jobs where human discretion is paramount.
Technological singularity refers to the concept that machines may eventually learn to outperform humans in the vast majority of thinking-dependent tasks, including those involving scientific discovery and creative thinking. This is the premise behind cinematic inventions such as “Skynet” in the Terminator movies.
However, not only is this possibility a long way off, but it may also be slowed by the ways in which people limit the use of machine learning technologies. The ability to create situation-sensitive decisions that factor in human emotions, imagination, and social skills is still not on the horizon. Further, as machine learning takes center stage in some day-to-day activities such as driving, people are constantly looking for ways to limit the amount of “freedom” given to machines.
Because these debates happen not only in people’s kitchens but also on legislative floors and within courtrooms, it is unlikely that machines will be given free rein even when it comes to certain autonomous vehicles. If cars that completely drove themselves—even without a human inside—become commonplace, machine-learning technology would still be many years away from organizing revolts against humans, overthrowing governments, or attacking important societal institutions.
Since machine learning can analyze objects and people’s faces, it is possible for human privacy to be invaded by the machines that collect and store their data, including those that pertain to their belongings and objects within their homes.
For example, if machine learning is used to find a criminal through facial recognition technology, the faces of other people may be scanned and their data logged in a data center without their knowledge. In most cases, because the person is not guilty of wrongdoing, nothing comes of this type of scanning. However, if a government or police force abuses this technology, they can use it to find and arrest people simply by locating them through publicly positioned cameras. For many, this kind of privacy invasion is unacceptable.
On the other hand, machine learning can also help protect people’s privacy, particularly their personal data. It can, for instance, help companies stay in compliance with standards such as the General Data Protection Regulation (GDPR), which safeguards the data of people in the European Union. Machine learning can analyze the data entered into a system it oversees and instantly decide how it should be categorized, sending it to storage servers protected with the appropriate kinds of cybersecurity.
Bias and Discrimination Issues
Because machine-learning models recognize patterns, they are as susceptible to forming biases as humans are. For example, a machine-learning algorithm studies the social media accounts of millions of people and comes to the conclusion that a certain race or ethnicity is more likely to vote for a politician. This politician then caters their campaign—as well as their services after they are elected—to that specific group. In this way, the other groups will have been effectively marginalized by the machine-learning algorithm.
Similarly, bias and discrimination arising from the application of machine learning can inadvertently limit the success of a company’s products. If the algorithm studies the usage habits of people in a certain city and reveals that they are more likely to take advantage of a product’s features, the company may choose to target that particular market. However, a group of people in a completely different area may use the product as much, if not more, than those in that city. They just have not experienced anything like it and are therefore unlikely to be identified by the algorithm as individuals attracted to its features.
Methods of Machine Learning
There are a few different machine learning types or methods, including the following:
Supervised learning: Supervised learning algorithms are trained with labeled examples, such as inputs when you already know the desired output. The algorithm learns by comparing the produced output with the correct one.
Unsupervised learning: This involves data without any prior labeling, meaning the “correct answer” is not provided. The algorithm then has to figure out what is being shown, with the goal of finding structure within the data.
Semi-supervised learning: This employs both labeled and unlabeled data to train the system, generally combining a sizable amount of unlabeled data with a small amount of labeled data.
Reinforcement learning: This machine learning method is based on trial and error. The algorithm learns which actions result in the biggest rewards.
The Future of Machine Learning
The future of machine learning lies in hybrid AI, which combines symbolic AI and machine learning. Symbolic AI is a rule-based methodology for the processing of data, and it defines semantic relationships between different things to better grasp higher-level concepts. This enables an AI system to comprehend language instead of merely reading data.
Real-World Machine Learning Use Cases
In the real world, machine learning can be used for:
Speech recognition, such as the translation of speech into text
Customer service, including online chatbots that can answer questions as well as a live human
Recommendation engines, such as recommending products that customers may like while they are checking out or browsing items
Automated stock trading, which can involve maximizing the performance of stock portfolios or making trades without the help of a human
How Fortinet Can Help
Fortinet FortiInsight uses machine learning to identify threats presented by potentially malicious users. FortiInsight leverages user and entity behavior analytics (UEBA) to recognize insider threats, which have increased 47% in recent years. FortiInsight monitors users and endpoints. It looks for the kind of behavior that may signal the emergence of an insider threat and then automatically responds.
FortiInsight can detect when a user or device is out of compliance with security protocols, acting suspiciously, or engaging in other anomalous behavior. It then automatically alerts the users associated with those accounts. Not only does FortiInsight protect organizations from threats but it also provides admins with enhanced visibility into activity on the network. Admins and supervisors can then use the data generated by FortiInsight to examine work patterns, productivity, and habits and adjust training and procedures accordingly.
What exactly is machine-learning?
Machine learning involves enabling computers to learn without someone having to program them. In this way, the machine does the learning, gathering its own pertinent data instead of someone else having to do it.
What are machine-learning examples?
Examples of machine-learning include computers that help operate self-driving cars, computers that can improve the way they play games as they play more and more, and threat detection systems that can analyze user behavior and recognize anomalous activity.
What are the types of machine-learning?
There are a few different types of machine-learning, including supervised, unsupervised, semi-supervised, and reinforcement learning.
Machine learning is more than just a buzz-word — it is a technological tool that operates on the concept that a computer can learn information without human mediation. It uses algorithms to examine large volumes of information or training data to discover unique patterns. This system analyzes these patterns, groups them accordingly, and makes predictions. With traditional machine learning, the computer learns how to decipher information as it has been labeled by humans — hence, machine learning is a program that learns from a model of human-labeled datasets.
It is unique in how it becomes, in a way, intuitive. Through repetition, it learns by inference without a need to be deliberately programmed each and every time. However, a caveat: Machine learning can make mistakes and appropriate caution should be used. 1
Machine learning proves to be useful especially in today’s big data world. We come into contact with machine learning on a daily basis. It supports technologies such as identifying voice commands on our phones, recommending which songs to listen to on Spotify or which items to purchase next on Amazon, and even determining the fastest way to reach your destination on Waze, to name a few.
How Machine Learning Can Help Businesses
Machine Learning helps protect businesses from cyberthreats. However, it works best as part of a multilayered security solution.
Machine learning is also used in healthcare, helping doctors make better and faster diagnoses of diseases, and in financial institutions, detecting fraudulent activity that doesn’t fall within the usual spending patterns of consumers.
Machine Learning Algorithm Types
Supervised Machine Learning
The traditional machine learning type is called supervised machine learning, which necessitates guidance or supervision on the known results that should be produced. In supervised machine learning, the machine is taught how to process the input data. It is provided with the right training input, which also contains a corresponding correct label or result. From the input data, the machine is able to learn patterns and, thus, generate predictions for future events. A model that uses supervised machine learning is continuously taught with properly labeled training data until it reaches appropriate levels of accuracy.
Unsupervised Machine Learning
Unsupervised machine learning, through mathematical computations or similarity analyses, draws unknown conclusions based on unlabeled datasets.An unsupervised machine learning model learns to find the unseen patterns or peculiar structures in datasets. In unsupervised machine learning, the machine is able to understand and deduce patterns from data without human intervention. It is especially useful for applications where unseen data patterns or groupings need to be found or the pattern or structure searched for is not defined. This also refers to clustering.
Instance-Based Machine Learning
Another type is instance-based machine learning, which correlates newly encountered data with training data and creates hypotheses based on the correlation. To do this, instance-based machine learning uses quick and effective matching methods to refer to stored training data and compare it with new, never-before-seen data. It uses specific instances and computes distance scores or similarities between specific instances and training instances to come up with a prediction. An instance-based machine learning model is ideal for its ability to adapt to and learn from previously unseen data.
Machine Learning and Cybersecurity
The emergence of ransomware has brought machine learning into the spotlight, given its capability to detect ransomware attacks at time zero.
Evolution is malware’s game. A few years ago, attackers used the same malware with the same hash value — a malware’s fingerprint — multiple times before parking it permanently. Today, these attackers use some malware types that generate unique hash values frequently. For example, the Cerber ransomware can generate a new malware variant — with a new hash value every 15 seconds.This means that these malware are used just once, making them extremely hard to detect using old techniques. Enter machine learning. With machine learning’s ability to catch such malware forms based on family type, it is without a doubt a logical and strategic cybersecurity tool.
Machine learning algorithms are able to make accurate predictions based on previous experience with malicious programs and file-based threats. By analyzing millions of different types of known cyber risks, machine learning is able to identify brand-new or unclassified attacks that share similarities with known ones.
From predicting new malware based on historical data to effectively tracking down threats to block them, machine learning showcases its efficacy in helping cybersecurity solutions bolster overall cybersecurity posture.
And though machine learning has become a major talking point in cybersecurity fairly recently, it has already been an integrated tool in Trend Micro’s security solutions since 2005 — way before the buzz ever started.
Machine Learning-powered Threats
Advanced technologies such as machine learning and AI are not just being utilized for good — malicious actors are also abusing these for nefarious purposes. In fact, in recent years, IBM developed a proof of concept (PoC) of an ML-powered malware called DeepLocker, which uses a form of ML called deep neural networks (DNN) for stealth.
There are other ways in which cybercriminals exploit these technologies. A popular example are deepfakes, which are fake hyperrealistic audio and video materials that can be abused for digital, physical, and political threats. Deepfakes are crafted to be believable — which can be used in massive disinformation campaigns that can easily spread through the internet and social media. Deepfake technology can also be used in business email compromise (BEC), similar to how it was used against a UK-based energy firm. Cybercriminals sent a deepfake audio of the firm’s CEO to authorize fake payments, causing the firm to transfer 200,000 British pounds (approximately US$274,000 as of writing) to a Hungarian bank account.
We discuss the current and possible future ML- and AI-powered threats here:
Foreseeing a New Era: Cybercriminals Using Machine Learning to Create Highly Advanced Threats
We listed a rundown of PoCs and real-life attacks where machine learning was weaponized to get a clearer picture of what is possible and what is already a reality with regard to machine learning-powered cyberthreats.
Exploiting AI: How Cybercriminals Misuse and Abuse AI and ML
We discuss the present state of the malicious uses and abuses of AI and ML and the plausible future scenarios in which cybercriminals might abuse these technologies for ill gain.
How Does Trend Micro Use Machine Learning?
Machine learning is a key technology in the Trend Micro™ XGen™ security, a multi-layered approach to protecting endpoints and systems against different threats, blending traditional security technologies with newer ones and using the right technique at the right time.
For over a decade, Trend Micro has been harnessing the power of machine learning to eliminate spam emails, calculate web reputation, and chase down malicious social media activity. Trend Micro continuously develops the latest machine learning algorithms to analyze large volumes of data and predict the maliciousness of previously unknown file types.
Connected Threat Defense for Tighter Security
Learn how Trend Micro’s Connected Threat Defense can improve an organizations security against new, 0-day threats by connecting defense, protection, response, and visibility across our solutions. Automate the detection of a new threat and the propagation of protections across multiple layers including endpoint, network, servers, and gateway solutions.
Trend Micro’s Machine Learning Milestones
Machine Learning Milestone
As early as 2005, Trend Micro has utilized machine learning to combat spam emails via the Trend Micro Anti Spam Engine (TMASE) and Hosted Email Security (HES) solutions.
To accurately assign reputation ratings to websites (from pornography to shopping and gambling, among others), Trend Micro has been using machine learning technology in its Web Reputation Services since 2009.
Trend Micro’s Script Analyzer, part of the Deep Discovery™ solution, uses a combination of machine learning and sandbox technologies to identify webpages that use exploits in drive-by downloads.
With the goal of helping law enforcement with cybercriminal investigations dealing specifically with targeted attacks, Trend Micro has developed SPuNge, a system that uses a combination of clustering and correlation techniques to “identify groups of machines that share a similar behavior with respect to the malicious resources they access and the industry in which they operate.”
Trend Micro developed Trend Micro Locality Sensitive Hashing (TLSH), an approach to Locality Sensitive Hashing (LSH) that can be used in machine learning extensions of whitelisting. It generates hash values that can be analyzed for whitelisting purposes. In 2013, Trend Micro open sourced TLSH via GitHub to encourage proactive collaboration.
In 2015, Trend Micro successfully employed machine learning in its Mobile App Reputation Service (MARS) for both iOS and Android, as well as in its mobile security products (Trend Micro™ Mobile Security for Android™ for end users and Trend Micro™ Mobile Security for Enterprise for organizations).Machine learning algorithms enable real-time detection of malware and even unknown threats using static app information and dynamic app behaviors. These algorithms used in Trend Micro’s multi-layered mobile security solutions are also able to detect repacked apps and help capacitate accurate mobile threat coverage in the TrendLabs Security Intelligence Blog.Since 2015, Trend Micro has topped the AV Comparatives’ Mobile Security Reviews. The machine learning initiatives in MARS are also behind Trend Micro’s mobile public benchmarking continuously being at a 100 percent detection rate — with zero false warnings — in AV-TEST’s product review and certification reports in 2017.
Predictive Machine Learning Engine was developed in 2016 and is a key part of the Trend Micro XGen solution. It uses two types of machine learning: pre-execution machine learning that identifies malicious files based on the file structure, and run-time machine learning for files that execute malicious behavior.
AV-TEST featured Trend Micro Antivirus Plus solution on their MacOS Sierra test, which aims to see how security products will distinguish and protect the Mac system against malware threats. Trend Micro’s product has a detection rate of 99.5 percent for 184 Mac-exclusive threats, and more than 99 percent for 5,300 Windows test malware threats. It also has an additional system load time of just 5 seconds more than the reference time of 239 seconds.Overall, at 99.5 percent, AV-TEST reported that Trend Micro’s Mac solution “provides excellent detection of malware threats and is also well recommended” with its minimal impact on system load (something more than 2 percent).
On February 7, 2017, Trend Micro further solidified its position at the forefront of machine learning technology — by being the first standalone next-generation intrusion prevention system (NGIPS) vendor to use machine learning in detecting and blocking attacks in-line in real time.The patent-pending machine learning capabilities are incorporated in the Trend Micro™ TippingPoint® NGIPS solution, which is a part of the Network Defense solutions powered by XGen security.Through advanced machine learning algorithms, unknown threats are properly classified to be either benign or malicious in nature for real-time blocking — with minimal impact on network performance.
[ Read: Machine Learning Masters ]
Trend Micro’s Dual Approach to Machine Learning
Machine learning at the endpoint, though relatively new, is very important, as evidenced by fast-evolving ransomware’s prevalence. This is why Trend Micro applies a unique approach to machine learning at the endpoint — where it’s needed most.
Pre-execution machine learning, with its predictive ability, analyzes static file features and makes a determination of each one, blocks off malicious files, and reduces the risk of such files executing and damaging the endpoint or the network. Run-time machine learning, meanwhile, catches files that render malicious behavior during the execution stage and kills such processes immediately.
Both machine learning techniques are geared towards noise cancellation, which reduces false positives at different layers.
A high-quality and high-volume database is integral in making sure that machine learning algorithms remain exceptionally accurate. Trend Micro™ Smart Protection Network™ provides this via its hundreds of millions of sensors around the world. On a daily basis, 100 TB of data are analyzed, with 500,000 new threats identified every day. This global threat intelligence is critical to machine learning in cybersecurity solutions.
The Trend Micro™ XGen page provides a complete list of security solutions that use an effective blend of threat defense techniques — including machine learning.
Data is vital to machine learning. Traditional machine learning models get inferences from historical knowledge, or previously labeled datasets, to determine whether a file is benign, malicious, or unknown.
We developed a patent-pending innovation, the TrendX Hybrid Model, to spot malicious threats from previously unknown files faster and more accurately. This machine learning model has two training phases — pre-training and training — that help improve detection rates and reduce false positives that result in alert fatigue.
Learn more about how we utilize both static and dynamic features to accurately and efficiently analyze unknown files here:
Faster and More Accurate Malware Detection Through Predictive Machine Learning
We have developed a machine learning model called TrendX Hybrid Model that uses two training phases — pre-training and training — and allows us to correlate static and behavior features to improve detection rates and reduce false positives.
Machine Learning vs. the Hype
How Is Big Data Relevant to Machine Learning?
The prevalence of the internet and the Internet of Things (IoT) — from devices, smart homes, and connected cars to smart cities — has made available large amounts of digital data that are generated on a daily basis, all available for collecting, analyzing, and utilizing.
These large amounts of data is aptly called big data. It is a combination of structured data (searchable by algorithms and databases) and unstructured data (hard or impossible to search via machine algorithm, such as macro files, emails, web searches, and images) that continue to grow at a highly accelerated pace. In fact, it is predicted that by 2025, 180 zettabytes (180 trillion gigabytes) of data will be generated.
Big data is being harnessed by enterprises big and small to better understand operational and marketing intelligences, for example, that aid in more well-informed business decisions. However, because the data is gargantuan in nature, it is impossible to process and analyze it using traditional methods.
Machine learning plays a pivotal role in addressing this predicament. Machine learning algorithms enable organizations to cluster and analyze vast amounts of data with minimal effort. But it’s not a one-way street — Machine learning needs big data for it to make more definitive predictions. Essentially, big data is necessary for machine learning to exist.
An understanding of how data works is imperative in today’s economic and political landscapes. And big data has become a goldmine for consumers, businesses, and even nation-states who want to monetize it, use it for power, or other gains.
[ Read: Knowledge is Power: The societal and business impact of big data ] [ Read: Big data analytics in the real world: Unique big data use cases ]
The world of cybersecurity benefits from the marriage of machine learning and big data. As the current cyberthreat environment continues to expand exponentially, organizations can utilize big data and machine learning to gain a better understanding of threats, determine fraud and attack trends and patterns, as well as recognize security incidents almost immediately — without human intervention.
[ Read: Big data and machine learning: A perfect pair for cyber security? ] [ Read: Machine learning and the fight against ransomware ] [ Read: Artificial intelligence could remake cyber security – and malware ]
Cognizant of these benefits, Trend Micro has partnered up with Hadoop developers to help improve its security model. Hadoop is a popular big data framework used by giant tech companies such as Amazon Web Services, IBM, and Microsoft.
[ Read: Securing Big Data and Hadoop ]
Are Data Mining and Machine Learning the Same?
Despite their similarities, data mining and machine learning are two different things. Both fall under the realm of data science and are often used interchangeably, but the difference lies in the details — and each one’s use of data.
Data mining is defined as the process of acquiring and extracting information from vast databases by identifying unique patterns and relationships in data for the purpose of making judicious business decisions. Data mining is effectively used for different purposes. A clothing company, for example, can use data mining to learn which items their customers are buying the most, or sort through thousands upon thousands of customer feedback, so they can adjust their marketing and production strategies.
Machine learning, on the other hand, uses data mining to make sense of the relationships between different datasets to determine how they are connected. Machine learning uses the patterns that arise from data mining to learn from it and make predictions.
To simplify, data mining is a means to find relationships and patterns among huge amounts of data while machine learning uses data mining to make predictions automatically and without needing to be programmed.
Can end-to-end deep learning solutions replace expert-supported AI solutions?
ML- and AI-powered solutions make use of expert-labeled data to accurately detect threats. However, some believe that end-to-end deep learning solutions will render expert handcrafted input to become moot. There have already been prior research into the practical application of end-to-end deep learning to avoid the process of manual feature engineering. However, deeper insight into these end-to-end deep learning models — including the percentage of easily detected unknown malware samples — is difficult to obtain due to confidentiality reasons.
In an attempt to discover if end-to-end deep learning can sufficiently and proactively detect sophisticated and unknown threats, we conducted an experiment using one of the early end-to-end models back in 2017. Based on our experiment, we discovered that though end-to-end deep learning is an impressive technological advancement, it less accurately detects unknown threats compared to expert-supported AI solutions.
Learn more about our experiment that measured the detection rates of end-to-end deep learning technology here:
Diving Into End-to-End Deep Learning for Cybersecurity
We look into developments in end-to-end deep learning for cybersecurity and provide insights into its current and future effectiveness.
Is Machine Learning a Security Silver Bullet?
Machine learning is a useful cybersecurity tool — but it is not a silver bullet. While others paint machine learning as a magical black box or a complicated mathematical system that can teach itself to generate accurate predictions from data with possible false positives, we at Trend Micro view it as one valuable addition to other techniques that make up our multi-layer approach to security.
Machine learning has its strengths. It is effective in catching ransomware as-it-happens and detecting unique and new malware files. It is not the sole cybersecurity solution, however. Trend Micro recognizes that machine learning works best as an integral part of security products alongside other technologies.
Trend Micro takes steps to ensure that false positive rates are kept at a minimum. Employing different traditional security techniques at the right time provides a check-and-balance to machine learning, while allowing it to process the most suspicious files efficiently.
A multi-layered defense to keeping systems safe — a holistic approach — is still what’s recommended. And that’s what Trend Micro does best.