Staying Ahead of the Game: Why AI is Essential for Effective Cyber Security?

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As the threat of cyber-attacks continues to grow, it is becoming increasingly clear that traditional security measures are not enough to keep up with the constantly evolving tactics of cybercriminals. This is where artificial intelligence (AI) comes in. 

AI has the potential to transform the way we approach cyber security by helping us detect and respond to threats in real time. With machine learning algorithms, AI systems can analyse vast amounts of data to identify patterns and anomalies that may be indicative of an attack. This means that we can detect threats much faster than we would be able to with human analysis alone. 

But AI is not just useful for detecting threats - it can also be used to prevent them. By using machine learning to analyse network behaviour, AI can identify potential vulnerabilities and make recommendations for how to patch them. This means that we can proactively address security weaknesses before they can be exploited by attackers. 

Of course, there are also risks associated with using AI in cyber security. As with any technology, there is always the potential for errors or biases in the algorithms that could lead to false positives or negatives. It is important to carefully monitor and test AI systems to ensure that they are working as intended. 

From Fraud Detection to Incident Response: Top Real-Time Cybersecurity Use Cases using AI 

 

Malware Detection and Prevention: Machine learning algorithms can detect and prevent malware by analysing the code and behaviour of programs running on a device, network or in the cloud. They can recognize patterns of suspicious activity and identify and block potential malware in real-time. 

User Authentication and Access Management: AI can be used to identify users and their behaviour to detect anomalies or unauthorized access attempts. It can also provide multi-factor authentication and risk-based access management to prevent unauthorized access. 

Threat Intelligence and Analytics: AI and machine learning technologies can collect, analyse, and interpret vast amounts of threat intelligence data from various sources in real-time. This enables faster detection and response to potential threats before they can cause damage. 

Network Security: AI and machine learning can detect and respond to network intrusions, as well as identify threats, such as distributed denial-of-service (DDoS) attacks, advanced persistent threats (APTs), and other malicious activities on the network. 

Fraud Detection: AI can be used to identify fraudulent transactions and prevent financial fraud. It can analyse patterns and behaviours of transactions, detect anomalies, and identify fraudulent activities. 

Incident Response: AI and machine learning can help automate the incident response process by enabling security teams to quickly identify and contain threats. They can also provide predictive analysis of potential threats and recommend response plans. 

Popular Cyber Security Frameworks: 

 

MITRE ATT&CK: The Adversarial Tactics, Techniques & Common Knowledge (ATT&CK) framework is a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. AI and machine learning algorithms are used to analyse and identify patterns in attack techniques, enabling quicker and more accurate threat detection and response. 

NIST Cybersecurity Framework: The National Institute of Standards and Technology (NIST) Cybersecurity Framework is a voluntary framework that outlines a set of best practices for managing cybersecurity risk. AI and machine learning are recommended as technologies that organizations can use to automate and enhance their cybersecurity practices, such as anomaly detection and threat intelligence. 

ISO/IEC 27001: The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) jointly developed the ISO/IEC 27001 standard for information security management systems (ISMS). AI and machine learning can be used to assist in threat intelligence and vulnerability management, as well as for monitoring and analysis of security events. 

CIS Controls: The Center for Internet Security (CIS) Controls is a prioritized set of best practices that organizations can use to improve their cybersecurity posture. AI and machine learning can be used to enhance threat intelligence, identify, and prevent malicious activity, and automate security processes. 

SANS Critical Security Controls: The SANS Institute developed the Critical Security Controls as a set of prioritized cybersecurity actions that organizations can take to protect their systems and data. AI and machine learning can be used to enhance detection and response capabilities, automate security processes, and provide real-time threat intelligence. 

Top 8 Machine Learning Tools for Cybersecurity 

 

bioHAIFCS: 

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. 

 

Cyber Security Tool Kit (CyberSecTK) 

The cybersecurity toolkit, CyberSecTK, is a Python library for pre-processing 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.  

 

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. 

 

DefPloreX 

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.  

 

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. 

 

StringSifter 

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.  

 

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. 

 

Targeted attack analytics (TAA) by Symantec 

Targeted attack analytics (TAA) tool is developed by Symantec to deliver numerous 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. 

 

Conclusion 

Overall, however, the benefits of using AI in cyber security far outweigh the risks. By combining the power of machine learning with human expertise, we can create a more robust and effective defence against cyber-attacks.