Low-and-slow scanning attacks are deliberate, stealthy attempts to probe systems over extended periods, aiming to avoid detection by traditional security mechanisms. These attacks often mimic legitimate user behavior, making them challenging to identify. Artificial Intelligence (AI) plays a pivotal role in detecting such subtle, time-dispersed patterns by employing advanced techniques that go beyond conventional detection methods.
How AI Detects Low-and-Slow Scanning Attacks?
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Anomaly Detection Through Behavioral Analysis
AI systems analyze baseline behaviors of users, devices, and network traffic to establish what is "normal." By continuously monitoring for deviations from this baseline, AI can identify unusual patterns indicative of low-and-slow attacks, such as sporadic scanning attempts that would be imperceptible to traditional systems.
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Temporal Pattern Recognition
Machine Learning (ML) models, particularly those utilizing time-series analysis, can detect temporal anomalies. For instance, if a system typically experiences a certain volume of traffic at specific intervals, AI can flag deviations, such as scanning attempts that occur at irregular intervals, which are characteristic of low-and-slow attacks.
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Contextual Awareness and Correlation
AI systems correlate data across various sources, including network logs, user activities, and system behaviors, to build a comprehensive understanding of the environment. This contextual awareness allows AI to identify subtle anomalies that might indicate an attack, even when individual events appear benign.
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Adaptive Learning and Model Refinement
AI models continuously learn from new data, adapting to evolving attack strategies. This self-learning capability ensures that AI systems remain effective against increasingly sophisticated low-and-slow scanning techniques, which may evolve to bypass traditional detection methods.
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Advanced Statistical Analysis
AI employs advanced statistical methods to analyze network traffic and user behavior, identifying outliers that deviate from established norms. These statistical anomalies can signify low-and-slow scanning attempts, enabling early detection and mitigation.
Real-World Application: Darktrace's AI Detection
Darktrace, a leader in cyber AI, utilizes machine learning to detect low-and-slow attacks by learning the normal patterns of activity within an organization. When an attack deviates from these patterns, even subtly, Darktrace's AI can identify and respond to the threat in real-time, often before traditional security measures can react.
AI's ability to analyze vast amounts of data, recognize subtle patterns, and adapt to new threats makes it an invaluable tool in detecting low-and-slow scanning attacks. By employing techniques such as anomaly detection, temporal pattern recognition, and contextual awareness, AI enhances an organization's capability to identify and mitigate these stealthy threats effectively.
