Friday 23rd April 2021

BLOG: How Machine Learning and AI can be used to prevent cyberattacks

At Orpheus Cyber, we deploy award-winning machine learning technology to predict the likelihood of a vulnerability being exploited in the future. This enables cyber teams to deploy risk-based vulnerability management. We also analyse large volumes of information on threats, turning this into actionable insights. When combined, the two provide a risk rating that can be used to manage risk within the third-party supply chain.

Machine Learning and Artificial intelligence are playing an increasing role in cybersecurity, with security tools analysing data from millions of cyber incidents and using it to identify potential threats such as malware or phishing methods via links. There is an increasing number of use cases on machine learning, being deployed in the security industry.

With new threats emerging frequently, there is a constant battle between attackers and defenders. Cybercriminals and threat actors have tried to modify their malware code so that security software no longer recognises it as malicious. Machine learning databases can attract data about any form of malware that’s been detected before. When a new form of the malware appears, whether a modified modification of active malware or a new version, the technology can verify the safety of this variant by examining the code and blocking the attack on the foundation that similar occurrences have formerly been marked as ‘malicious’.

This can be notified even when the malicious code is hidden using substantial volumes of benign coding to conceal the malicious purpose of the code. These tools can also track what users do daily, understanding a pattern of users normal behaviour. By examining this type of information, our tools can distinguish anomalies and respond appropriately.

Machine Learning and AI allow cyber teams to respond intelligently, understanding the relevance and consequences of a breach or a change of behaviour, and in real-time develop a proportionate response. For example, if an employee accesses a new system, the system can work out that this was not normal behaviour and could potentially be malicious activity. Through the use of machine learning, this can be spotted almost immediately, blocking the potential damage of a malicious intrusion and preventing login credentials from being stolen, malware being implemented, or otherwise enabling attackers to gain access to the network.

There is also machine learning technology hoping to prevent phishing attacks. AI can detect changes in language or beahviour that may indicate that an account has been compromised. This is useful for sophsiticated account takeovers, where it may not be immediately obvious that the email is a phishing scam.

Undeniably, machine learning and AI are not perfect and like any other cyberattack prevention method; it’s possible that threat actors and cybercriminals can use the same techniques used to prevent them to make their attacks more effective. Cybercriminals could develop self-understanding automated malware, ransomware, or phishing attacks. Nevertheless, Machine Learning and AI-based cybersecurity tools continue to develop and improve, and when utilised accurately and precisely in conjunction with human security teams, this could help businesses stay protected against progressively intelligent and effective cyberattacks.

With so many uses for machine learning in cybersecurity, many hail this as the solution to our resource problem within cybersecurity. If you would like to learn more about Orpheus’ use of machine learning please click here

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