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How Behavior Analytics Improves Cybersecurity | #hacking | #cybersecurity | #infosec | #comptia | #pentest | #ransomware


Identification of Compromised Credentials

A common network entry point for a malicious actor is to use legitimate credentials that have been stolen or otherwise compromised. The actor appears to be a legitimately authorized user—until they perform some activity that isn’t common for that user account. By comparing the bad actor’s actions to the real user’s baseline profile, behavior analytics can quickly identify anomalous activity that could be indicative of a threat.

Device Profiling

Behavior analytics can build profiles of normal device behavior by monitoring and analyzing device activities over time. This includes examining device interactions, network traffic patterns, resource usage, application usage, and system events. Any deviations from the established device profiles can be flagged as suspicious or potentially malicious. This is helpful in detecting variances in devices on the Internet of Things.

Detection of Unknown or Advanced Threats

Traditional security measures often rely on known signatures or patterns of known threats. Behavior analytics, however, can identify unknown or advanced threats that do not match any predefined signatures. By analyzing behavioral indicators and detecting anomalous activities, behavior analytics can uncover sophisticated attacks, including zero-day exploits and advanced persistent threats (APTs).

Continuous Monitoring and Adaptation

Behavior analytics enables continuous monitoring and analysis of user behavior and system activities. It can adapt to evolving threats and changing patterns of behavior, allowing security systems to stay up to date and respond effectively to emerging risks. This dynamic nature of behavior analytics enhances the overall security posture of an organization.

Enhanced Incident Response and Forensic Analysis

By analyzing behavior patterns leading up to a security incident, security teams can gain a better understanding of attack vectors, timelines, and tactics used by attackers. This information can help in containing and mitigating the incident effectively, as well as improving future incident response strategies.

Reduction of False Positives

Behavior analytics can help reduce false positives, which are alerts or alarms triggered by legitimate user activities that may appear suspicious. By analyzing behavior patterns, historical data, and context, behavior analytics can differentiate between normal and abnormal behavior, resulting in more accurate and targeted alerts, reducing the number of false positives and minimizing the burden on security teams.

Types of Behavior Analytics Used to Secure Organizations

Various types of behavior analytics techniques are employed to secure organizations. Here are some common ones.

User Behavior Analytics (UBA)

UBA monitors and analyzes the behavior of individual users within an organization’s network. It establishes baselines for normal behavior and identifies anomalies that could indicate malicious activity, such as unusual login times, excessive access requests, or data exfiltration attempts.

User and Entity Behavior Analytics (UEBA)

UEBA extends the analysis beyond individual users to other entities such as applications, servers, or IoT devices. It looks for abnormal behavior patterns, such as sudden spikes in network traffic, unexpected communication between devices, or unauthorized changes to system configurations.

Network Traffic Analysis (NTA)

NTA monitors network traffic and analyzes communication patterns between devices such as routers, switches, firewalls, and endpoints. It detects anomalies in network behavior such as unusual traffic volumes, abnormal communication flows, suspicious connection attempts, and data exfiltration attempts. NTA can help identify network-based attacks, malware infections, and unauthorized network access.

Anomaly Detection

Anomaly detection techniques use statistical models or machine learning algorithms to identify deviations from normal behavior. By establishing baselines of typical network, user or entity behavior, anomalies like unusual data flows, access attempts, or unexpected configuration changes can be detected and investigated as potential security incidents.

Threat Hunting

Threat hunting involves actively searching for indicators of compromise and potential threats within an organization’s network. Behavior analytics aids in this process by examining patterns and trends across multiple data sources, such as logs, network traffic, and security events, to proactively identify potential threats that may have evaded traditional security measures.

Risk Scoring and Prioritization

Behavior analytics provides a basis for assigning risk scores to users, entities, or events based on their behavior patterns. These risk scores help prioritize security responses, focusing resources on higher-risk activities and individuals, and enabling efficient incident response.

Is UEBA Effective in Cybersecurity?

UEBA can be highly effective in enhancing cybersecurity for several reasons. It enables early threat detection by leveraging advanced analytics and machine learning algorithms to establish baselines of normal behavior for users and entities within an organization. By continuously monitoring and analyzing their activities, UEBA can quickly detect anomalous or suspicious behavior patterns that may indicate a security threat. This early threat detection helps security teams respond promptly and mitigate potential risks before they escalate.

Detecting insider threats is another important function of UEBA. By monitoring user behaviors such as data access patterns, privileged account usage, and unusual activity, UEBA helps identify insiders who may be engaged in malicious activities, unauthorized data exfiltration, or privilege abuse. This can significantly reduce the time to detect and respond to insider threats, minimizing potential damage.

UEBA also provides excellent advanced threat detection. Traditional security solutions often rely on predefined signatures or patterns to detect known threats. UEBA complements these solutions by employing machine learning and behavior analytics to identify unknown or advanced threats that may evade traditional detection mechanisms. By detecting deviations from established behavior patterns, UEBA can uncover sophisticated attack techniques, including zero-day exploits, targeted attacks, and insider collusion.

UEBA provides valuable contextual insights by correlating data from multiple sources such as logs, network traffic, and authentication data. By analyzing a wide range of data points, UEBA helps security teams gain a holistic view of user and entity activities, enabling them to understand the context of observed behaviors. This contextual awareness allows for more accurate threat detection and reduces false positives.

UEBA enhances incident response capabilities by providing security teams with actionable insights. When a suspicious behavior is detected, UEBA solutions can generate real-time alerts or notifications, along with detailed information about the observed behavior. These alerts help security analysts investigate incidents more efficiently and prioritize their response efforts, enabling quicker incident resolution and minimizing the impact of security breaches.

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