Subscription
Thanks for submitting the form.
Introduction to Machine Learning in Cybersecurity
Many activities exist or happen every day as part of ordinary life. From all of them, one of the most popular and essential is internet security. Tracking threats is challenging, and the challenge is even more significant if you try to identify the root of the threat. Seeing threats from under a hacker is an arduous task. Applying effective online security methods is incredibly challenging today because there are more devices than humans, and attackers are starting to invent new things. ML and artificial intelligence (AI) have emerged as essential tools to deal with the ever-increasing volume and complexity of cybersecurity threats.
What is Cybersecurity?
Cyber security protects Internet-connected systems such as hardware, software, and data from cyber threats. It is a practice that protects systems, networks, and systems from digital attacks that can ruin. The cyber attacks aim to gain access to, modify, or destroy sensitive information, prevent consumer fraud, or assassinate normal business processes. So one needs to protect their systems so they can surpass and handle those threats.
Considering the "source" of the attack is not very helpful in identifying threats because we need to focus almost entirely on system actions. Most "probably" because there is a network component, IP, host, location, and another shadow, which serve as an indication of probability.
Click to read about Fairness in ML
Why do we need Machine Learning in Cybersecurity?
Everyone connected to the Internet needs security online. This is because most online attacks are spontaneous and are intended to exploit common vulnerabilities rather than using certain websites or organizations. Using ML, machines can be made eligible enough to recognize patterns to detect malicious and abnormal activity better than humans and traditional software. These latest technologies can also predict potential attacks using various techniques and tools and automatically respond to threats by identifying specific trends and cycles.
Indeed, it is not uncommon to have similar incidents that often require the same response. Instead of repeating the same process, sometimes the system can detect attacks, report and disconnect an event, and then install auto-correction. Some more behavioral analytics tools can also spot attacks by just noticing anomalous activity of any user if it exists. Various types of UEBA (User entity behavioral analysis) tools exist like:
- Microsoft Azure Advanced Threat Analytics (ATA)
- ManageEngine Log360
- Aruba Introspect
- Exabeam Advanced Analytics
- Cynet 360
- LogRhythm UserXDR
How to enable Machine Learning for Cybersecurity?
The most common approach is the regression technique, which can be used for prediction. Defenders can use existing data to detect fraud and malware in this approach. They can train on previous data and perform stable and accurate detection.
The other way to solve this problem is User and Entity Behavior Analytics, a category of security solutions that uses analytical techniques such as machine learning, deep learning, statistical analysis, etc., to discover abnormal and risky behavior by users, machines, and other entities on a corporate network.
UEBA solutions build profiles that model the standard behavior of users and entities in an IT environment, such as users, servers, data repositories, etc. UEBA using a variety of analytics establishes a baseline (normal behavior for any user or entity) and uses these baselines to compare and identify anomalous behavior.
Explore here about Bringing AI at the Edge
Future of Machine Learning in Cybersecurity
It is important to remember that, while technology is evolving, advancements in AI and ML are improving at a large scale. Very rapidly, technology is only good or bad, as analysts minds control and use it. The Cyber Security Industry is struggling to meet the demand for specialists so that technology can help alleviate stress and ensure that technology advances to prevent aggressive attacks.
However, hackers will eventually use this technology to carry out complex attacks. Botnets using these algorithms can detect network access and use risk. Deep Neural Network(DNN) algorithms need to learn about other DNNs and whether one is malicious. That will be the key to using AI in Cyber Security. It should also see when its technology is being used against it.
What are the challenges of using Machine Learning in Cybersecurity?
One can face many challenges while using ml in Cyber security. Some of them are as follows:
- False positives and alert fatigue
- Dynamic environments
- Anti-ML attacks
- Labelled data is not available, and ground truth is certain
- Imbalanced Data Sets
- Concept Drift
- Domain Adaptation
- Access to Data Sets
- Host similarity (same IP issues)
Click to know about Explainable AI in Manufacturing Industry
Use Cases of Machine Learning in Cybersecurity
There are already a few of its models used in Cybersecurity. Let's understand how ML is applied to Cybersecurity in these application cases. Some of the use cases are as follows:
- Using ML to detect abnormal/malicious activity: It can be used to detect any unusual activity and stop attacks before they get started.
- Using ML to detect SMS Frauds: As SMS frauds are increasing daily, it is not easy to differentiate between a simple SMS and a fraudulent SMS. So ML models are trained to segregate the hackers from genuine information, and different endpoints can be safeguarded using UEM -Unified Endpoint Management program.
- Using ML for Enhancing Human Analysis and Safeguarding against Human Errors: When the number of data increases rapidly and it becomes hard for the human to analyze tons of data so there should be some machine learning security platform that should be able to filter out all the malicious activities out of millions of actions.
- Using ML in Anti-Virus software and Malware Detection: Anti-virus software powered by Machine learning can detect anomaly detection and track program behavior. Regular updates are required by anti-virus software to stay updated with all new viruses coming up the way.
- Using ML in Email Monitoring: Also, many fraudulent emails take so much time and effort to categorize, so the latest monitoring tools can detect viruses/malware without the mail being opened. The patterns are matched with ordinary mails using the NLP algorithm to solve this problem, and phishing emails are detected. Using this, one can find whether the email, attachment, or sender is a phishing scam or attack.
- Using ML Against Bots: With the rapid advancement in technology, a vast amount of data containing behavioral patterns is required to distinguish ‘good bots’ from the ‘bad bots.’ The main differentiation factors are unnatural patterns, fast movement across the net, etc. It is needed to fight against already-automated bots.
- Using ML in Network Threat Detection: With the amount of data coming in and out of the network, it is no joking matter. Also, analyzing the data, maintaining the web, and identifying the behavior of the connection plays a vital role in detecting network threats. The enhanced ML-based network security system will track all outgoing and incoming calls/data and detect any suspicious information patterns in the network.
Conclusion
In conclusion, DNN and ML are complex data science technology subjects requiring an in-depth understanding of mathematical and neural receptors that I can personally discover the true picture of during this keystone research project. The data science aspect of DNN and ML will be widely explored in the coming decades. It will be used in many aspects of internet technology, such as IoT and visual and Geo Spatial applications. Happy time working in IT!
- Read more about Machine Learning Observability
- Click to know about Governing AI initiatives with ModelOps
