Top 8 Machine Learning Tools For Cybersecurity

In the present scenario, techniques like AI and machine learning are involved in almost all sectors. These techniques help organisations by various means, starting from getting insights from raw data to predicting future outcomes, and more. 

Focussing all the benefits of AI and ML, the utilisation of machine learning techniques in cybersecurity has been started only a few years ago and still at a niche stage. AI in cybersecurity can help in various ways, such as identifying malicious codes, self-training and other such. 

Here is a list of top eight machine learning tools, in alphabetical order for cybersecurity.


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bioHAIFCS is a bio-inspired hybrid artificial intelligence framework for cybersecurity. This framework combines timely and bio-inspired machine learning methods suitable for the protection of critical network applications, namely military information systems, applications and networks.

More specifically, it combines the hybrid evolving spiking anomaly detection model (HESADM), which is used to prevent cyber-attacks, which cannot be avoided otherwise by — using passive security measures; the evolving computational intelligence system for malware detection (ECISMD); and the evolutionary prevention system from SQL injection (ePSSQLI) attacks.

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Cyber Security Tool Kit (CyberSecTK)

The cybersecurity toolkit, CyberSecTK, is a Python library for preprocessing and feature extraction of cyber-security-related data. The purpose of this library is to bridge the gap between cybersecurity and machine learning techniques.

The toolkit is basically a suite of program modules, datasets as well as tutorials supporting research in cybersecurity. The CyberSecTK works by helping cyber experts to implement a basic machine learning pipeline from scratch. 

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Cognito by Vectra

Cognito by Vectra is an AI tool that detects and responds to attacks inside the cloud, data centre, IoT, and enterprise networks. Some of the benefits of using Vectra Cognito platform includes automated threat detection, empowering threat hunters, providing visibility across entire deployment and other such.

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DefPloreX is a machine learning toolkit for large-scale e-crime forensics. It is a flexible toolkit that is based on the open-source libraries to efficiently analyse millions of defaced web pages.

DefPloreX or Defacement eXplorer uses a combination of machine learning and data visualisation techniques to turn unstructured data into meaningful high-level descriptions. One of the most interesting aspects of DefPloreX is that it automatically groups similar defaced pages into clusters and organises web incidents into campaigns. 

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IBM QRadar Advisor

IBM QRadar Advisor with Watson uses IBM cognitive artificial intelligence to assist users with the incident and risk analysis, triage and response, enables security operations teams and more.

The tool helps in reducing the time spent investigating incidents from days and weeks down to minutes or hours. It automates routine SOC tasks, finds commonalities across investigations and provides actionable feedback to analysts, freeing them up to focus on more important elements of the investigation and increase analyst efficiency.

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StringSifter is a machine learning tool which ranks strings automatically based on their relevance for the malware analysis. It is built to sit downstream from the Strings program. This means it gets a list of strings as input and delivers the same strings as output ranked according to their relevance for malware analysis. 

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Sophos’ Intercept X tool

Sophos’ Intercept X tool is a cybersecurity tool that is integrated with a deep learning neural network that works by changing the endpoint security from a reactive to a predictive approach to protect against both known and never-seen-before threats.

Sophos Intercept X employs a comprehensive defence-in-depth approach to endpoint protection, rather than simply relying on one primary security technique. The features of this tool include enforcing data execution prevention, stack pivot, heap spray allocation, among others. 

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Targeted attack analytics (TAA) by Symantec

Targeted attack analytics (TAA) tool is developed by Symantec to deliver various benefits, such as cloud-based analytics that automatically adapt to new attack techniques, continuously delivered attack detections plus the ongoing addition of new attack analytics and more.

It also provides benefits to Advanced Threat Protection customers by multiple incidences of attack detections combined with AI-driven and human analysis customised to each customer’s environment.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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