Machine Learning and Big Data Analytics for Cybersecurity

- Overview

Cybersecurity is a promising area for AI/ML. In theory, if a machine has access to everything you currently know is bad, and everything you currently know is good, you can train it to find new malware and anomalies when they surface. In practice, there are three fundamental requirements for this to work. 

• First, you need access to data - lots of it. The more malware and benign samples you have, the better your model will be. 
• Second, you need data scientists and data engineers to be able to build a pipeline to process the samples continuously and design models that will be effective. 
• Third, you need security domain experts to be able to classify what is good and what is bad and be able to provide insights into why that is the case. 

However, many companies touting AI/ML-powered security solutions lack one or more of these pillars.

- Machine Learning and Data Analytics for Cyber Security 

Machine learning (ML) and big data analytics are used in cybersecurity to help identify and mitigate security threats in real-time. 

Here are some ways ML and big data analytics are used in cybersecurity:

• Identify patterns and anomalies: ML algorithms can identify patterns and anomalies in large datasets to detect malicious activity.
• Analyze large amounts of data: ML can analyze large amounts of data and spot patterns, which makes it ideal for detecting attacks in their earliest stages.
• Expose network vulnerabilities: ML can expose network vulnerabilities and anticipate when and how future cyber attacks will occur.
• Identify suspicious keywords: ML can identify suspicious keywords that scammers use in emails to imitate reputable organizations.
• Flag suspicious salary increases: ML can flag suspicious salary increases in payroll systems.

ML techniques can help security systems identify patterns and threats with no prior definitions, rules, or attack signatures, and with much higher accuracy. However, to be effective, ML needs very big data.

- Adversarial Machine Learning in Cybersecurity and Intrusion Detection

Adversarial machine learning (AML) is a field that combines cybersecurity and AI. It involves techniques to identify weaknesses in machine learning systems and develop safeguards against potential manipulation or deception. 

AML is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks. Adversarial attacks are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. 

Adversarial attacks can be mainly classified into the following categories: Poisoning Attacks, Evasion Attacks.

Adversarial attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life.

[More to come ...]