Cybersecurity has entered an era where machine learning plays a pivotal role in responding to, preventing, and predicting cyberattacks. A diverse range of machine learning techniques, including Regression, Classification, Clustering, Association Rules, Dimensionality Reduction, and Generative Modelling, are being harnessed, tailored to specific use cases. Drawing from my extensive experience, I have curated a list of ten exceptional use cases in this critical field.
- Windows User Login Anomaly Detection: Leveraging the Windows event logger, which meticulously records every system event, offers a means to detect anomalies in user logins. By correlating event code 4624, representing login events, with user activities, clustering techniques can identify irregular login patterns.
- Risk Identification on IT Assets: Ensuring compliance with organizational policies is of paramount importance. Detecting risks associated with non-compliant assets is critical, as non-compliance can create vulnerabilities to cyberattacks. Utilizing regression techniques to predict potential non-compliance events, such as outdated antivirus signatures due to infrequent laptop connectivity, enables proactive actions to safeguard against security breaches.
- API Monitoring: In an evolving API-driven landscape, monitoring both consumed and exposed APIs is imperative. Transforming API data into a model and applying dimensionality reduction techniques can identify anomalies, thereby ensuring the security of interdependent APIs.
- System Incident and Event Management (SIEM): Leading SIEM solutions like IBM QRadar and Splunk integrate AI and ML models to detect anomalies in system events. Creating situational event models from SIEM-generated data allows for proactive event detection, a crucial approach in a cybersecurity landscape where businesses invest substantially.
- Malware Detection and Classification: Real-time identification of malware is a linchpin of cybersecurity. Clustering algorithms followed by classification are instrumental in monitoring endpoints for unusual behaviors, such as new processes listening on unused ports or unexpected registry changes.
- Network Log Analysis: Analyzing network and application log files plays a pivotal role in identifying intrusions. Machine learning models designed for network security, including DNS tunnel attack detection and network intrusion detection, facilitate the recognition of deviations from trained models, a critical defense against zero-day attacks.
- User and Entity Behavioral Analysis (UEBA): Tracking user and entity behavior is paramount for anomaly detection. Alterations in user login locations, access patterns, or privileges can signal potential threats. Translating this data into a model allows for the detection of anomalies.
- AI-Based Threat Mitigation: Cyber attackers increasingly employ machine learning to exploit vulnerabilities. To counter this, harnessing all available data on IT assets and users to construct real-time models that pinpoint vulnerabilities is indispensable.
- AI-Enabled Penetration Testing: AI-enhanced penetration testing prioritizes vulnerabilities and predicts remediation steps, substantially enhancing the effectiveness of security administrators.
- Deception Using Honeypots: Implementing deception-centered honeypot models aids in comprehending hacker intent. Tracking hacker activities and constructing data models based on their actions yield invaluable insights.
Machine learning algorithms serve as a powerful arsenal to address a wide spectrum of cybersecurity challenges, empowering organizations to fortify their defenses against ever-evolving threats.
Ram is a Cloud Security Expert with 30+ years of IT experience, holding 26 patents in Infra, AI-ML, and Automation. He’s a Wipro Fellow, an Independent Consultant for Fortune 15 companies, and has won international awards for Automation. Ram’s cost rationalization work benefited enterprises like Citi Bank, Credit Suisse, and UBS.