Machine Learning to detect and prevent cyber threats - Hindustan Times

Machine Learning to detect and prevent cyber threats

May 18, 2024 12:59 PM IST

This article is authored by Romel Bhattacharjee, senior analyst, technology research & advisory, Aranca.

In the digital battleground of cybersecurity, the rise of cyber threats has necessitated the adoption of advanced tools and strategies to defend against malicious actors. Thankfully, artificial intelligence (AI) and machine learning (ML) have emerged as formidable allies in this ongoing battle, offering innovative approaches to detect and prevent cyber threats before they can cause significant harm. By leveraging the power of AI and ML, organisations can significantly enhance their cybersecurity capabilities and better protect themselves against the constantly evolving threat landscape.

Cyber attack(Representative image)
Cyber attack(Representative image)

Behavioural analysis, powered by AI and ML, is playing a crucial role in identifying and mitigating cyber threats. User and Entity Behaviour Analytics (UEBA) focuses on understanding the normal behaviour patterns of users and entities within a network, such as devices and applications. By analysing historical data, UEBA can establish baselines for normal activity and swiftly detect deviations that may indicate malicious intent. For example, sudden changes in user access patterns or login attempts at unusual hours could raise red flags, prompting further investigation.

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Similarly, Network Traffic Analysis (NTA) tools utilise AI and ML algorithms to scrutinise network traffic patterns, identifying anomalies that may signify potential threats. These anomalies could include unusual traffic volumes, communication with known malicious IP addresses, or suspicious data transfer patterns. By continuously monitoring network traffic, organisations can proactively identify and mitigate cyber threats before they escalate.

Deception Technology is another innovative approach that leverages AI to enhance cybersecurity. By deploying decoys within a network, organisations can trick attackers into revealing their presence and tactics. These decoys mimic real assets, such as servers or databases, and are designed to lure attackers away from valuable resources. Through AI-powered analysis of attacker behaviour, organisations can gain valuable insights into their methods and objectives, enabling them to strengthen their defences and better protect against future attacks.

In addition to detecting threats, AI and ML are also instrumental in automating defensive responses. Malware detection, for instance, relies on ML algorithms trained on vast datasets of known malware samples. By identifying the unique characteristics and behaviours of malicious software, these algorithms can detect both known and previously unseen malware variants with high accuracy, enabling organisations to swiftly neutralise threats.

Phishing detection is another area where AI-powered systems excel. By analysing emails and websites, AI algorithms can identify features commonly associated with phishing attempts, such as suspicious URLs, grammatical errors, and urgency-inducing language. This enables organisations to identify and block phishing attempts before they can compromise sensitive information or systems.

Intrusion Detection Systems (IDS) have also benefited from AI and ML technologies. Modern IDS leverage AI to analyse network traffic and system logs in real-time, identifying patterns indicative of intrusion attempts. By rapidly detecting and responding to potential threats, organisations can mitigate the impact of cyber attacks and minimise the risk of data breaches or system compromise.

Furthermore, AI and ML algorithms power automated defence responses, enabling organisations to respond rapidly to cyber threats. Security Orchestration, Automation and Response (SOAR) platforms integrate AI and ML to automate incident response workflows. Upon detection of a threat, these platforms can automatically initiate actions such as isolating infected systems, blocking malicious traffic, and triggering counter-measures, reducing the burden on human analysts and enabling faster response times.

Automated Patch Management is another area where AI and ML are making a significant impact. By analysing vulnerability data and prioritising patching efforts based on risk level and potential impact, AI-powered systems ensure that critical vulnerabilities are addressed promptly and efficiently, reducing the window of opportunity for attackers to exploit weaknesses in systems or software.

Next-generation firewalls are also leveraging AI to enhance their capabilities. By dynamically adapting their rules and policies based on real-time threat intelligence and network activity, AI-powered firewalls provide more robust and proactive protection against evolving threats. This adaptive approach enables organisations to stay one step ahead of attackers and effectively defend against emerging cyber threats.

The automation of tasks such as threat detection and response free up human analysts to focus on more complex and strategic issues, improving overall efficiency and effectiveness. AI and ML algorithms are capable of analysing vast amounts of data with enhanced precision, enabling organisations to identify and mitigate threats more accurately and efficiently than ever before.

Despite their strengths, AI and ML in cybersecurity are not without limitations. The effectiveness of these systems relies heavily on the quality and quantity of data they are trained on. Biased or incomplete data can lead to inaccurate predictions and false alarms. Additionally, understanding how an AI system arrives at its decisions is often difficult, leading to trust and transparency issues.

To overcome these limitations, researchers are exploring several strategies. Federated learning allows multiple organisations to collaborate on training AI models without sharing sensitive data, improving data diversity and model accuracy. Explainable AI (XAI) techniques aim to make AI decision-making more transparent and understandable, fostering trust and acceptance among users and stakeholders. Furthermore, by deliberately exposing AI models to adversarial attacks during training, their resilience to such attacks can be significantly enhanced, ensuring that they remain effective in the face of evolving cyber threats.

Looking to the future, the potential of AI and ML in cybersecurity is vast. AI-powered threat hunting will enable organisations to proactively search for and neutralise threats hidden within their networks, going beyond simply reacting to known attack patterns. As quantum computing evolves, AI and ML will be crucial in developing new encryption methods that are resistant to quantum attacks, ensuring that sensitive information remains secure in an increasingly digitised world. Additionally, security operations will become increasingly automated, with AI handling tasks such as vulnerability management, incident response, and security policy enforcement, enabling organisations to stay ahead of cyber threats and effectively protect their assets and data.

AI and ML are transforming the cybersecurity landscape, offering powerful tools to combat the ever-evolving threat landscape. While challenges remain, continuous research and development efforts are paving the way for a future where AI becomes an indispensable asset in safeguarding our digital world. By leveraging the capabilities of AI and ML, organisations can enhance their cybersecurity posture, protect against emerging threats, and stay one step ahead of cybercriminals.

This article is authored by Romel Bhattacharjee, senior analyst, technology research & advisory, Aranca.

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