Network Anomaly Detection with AI PDF

Network Anomaly Detection with AI  The increasing complexity of interconnected devices, such as IoT, demands automated tools to detect network anomalies and address evolving cybersecurity threats as traditional perimeter security becomes ineffective. This chapter will cover the following topics...

Network Anomaly Detection with AI  The increasing complexity of interconnected devices, such as IoT, demands automated tools to detect network anomalies and address evolving cybersecurity threats as traditional perimeter security becomes ineffective. This chapter will cover the following topics: Network anomaly detection techniques How to classify network attacks Detecting botnet topology Different machine learning (ML) algorithms for botnet detection  Network anomaly detection techniques Network anomaly detection has evolved from rule-based and statistical methods to advanced AI and machine learning approaches, enabling more accurate and adaptive threat detection. It relies on establishing baselines, extracting relevant features, real-time monitoring, adaptability, reducing false positives, and ensuring scalability. Beyond cybersecurity, anomaly detection is used in fraud detection, user profile compromise, IoT security, and healthcare. Key challenges include evolving threats, handling large data volumes, minimizing false positives, and addressing privacy concerns. AI integration, edge computing, explainable AI, and zero trust architectures will shape the future of anomaly detection, making systems more autonomous and efficient.  Anomaly detection rationales Aspect Signature-Based Detection Anomaly Detection Identifies deviations from Matches network traffic against Approach normal network behavior to known attack signatures. detect anomalies. Capable of detecting new, Effective against known attacks with Strengths unknown attacks by predefined signatures. analyzing unusual patterns. Challenging to distinguish Requires constant updates to detect true positives (actual threats) Weaknesses new threats; ineffective against zero- from false positives (benign day attacks. changes). - Unusual number of connections. - Unexpected traffic patterns Key Indicators Predefined attack signatures. or ports. - Traffic peaks at odd times. - High bandwidth usage by specific hosts. Filters and alerts based on Alerts triggered when traffic matches normal traffic baselines; Implementation archived signatures. suspicious traffic can be dropped or flagged. Adapts to new traffic patterns Limited; relies on updates to the but must account for Adaptability signature database. legitimate network changes  Intrusion Detection Systems (IDS): Type of IDS Description Key Features Monitors individual host machines - Tracks system metrics like Host-Based IDS (HIDS) for intrusions, focusing on critical running processes, user accounts, systems. kernel modules, and file activity. - Monitors registry changes, task schedulers, background processes, and host network activity. - Uses OS tools or specialized monitoring software for data collection. Monitors network traffic for Network-Based IDS - Analyzes packets and traffic suspicious activity across the (NIDS) patterns to detect intrusions. entire network. - Effective for identifying external threats and attacks targeting network infrastructure. Uses AI techniques to detect - Focuses on identifying unusual deviations from normal behavior, Anomaly-Driven IDS patterns in network or host identifying unknown or zero-day behavior. threats. - Adapts to new threats by learning from network and system behavior over time.  Network Intrusion Detection Systems Aspect Description Primary Task Analyzes network traffic (incoming and outgoing packets) to detect known attack patterns. - Adware (malicious advertisements). - Spyware (sensitive data transmission). Common Attacks Detected - Advanced Persistent Threats (APTs). - Botnets (zombie machines executing remote commands). - Network diagnostic tools like tcpdump and Wireshark. Implementation Tools - Integrated software solutions like Snort for real-time intrusion detection. Defines rules to compare normal vs. malicious traffic, triggering actions when attacks are Rule-Based Detection identified. - Static thresholds can be exploited by attackers (e.g., stealth-access mode). Threshold Challenges - Dynamic thresholds (e.g., moving averages, median, IQR) are preferred for adaptability. - Multiple trigger thresholds may be needed due to correlations between variables. Complex Scenarios - Statistical approaches alone may be insufficient for complex intrusion detection. - Tracks packet flow to correlate different types of packets. Stateful Inspection - Detects connection attempts, DoS attacks, and lower-level protocol attacks (e.g., ARP cache poisoning). Stateful inspection enables more sophisticated anomaly detection by analyzing packet Advanced Anomaly Detection correlations and network behavior.  Anomaly detection strategies Strategy Description Use Case Examples Analyzes data collected at regular - Detecting unnatural increases in Time Series Analysis intervals to detect changes over user input frequency (e.g., time. automated bots). - Monitoring financial markets or network traffic for unusual patterns. Uses labeled datasets to train Supervised Learning - Credit card fraud detection models that distinguish between Algorithms (predefined suspicious patterns). normal and anomalous behavior. - Identifying known attack signatures or behaviors. Detects anomalies without labeled Unsupervised Learning - Detecting zero-day attacks or data, identifying deviations from Algorithms new vulnerabilities. normal behavior. - Cybersecurity scenarios where predefined patterns are unavailable or insufficient.  ML algorithms for botnet detection botnet (short for “robot network”) is a network of computers infected by malware that is under the control of a single attacking party, known as the “bot herder.” Each machine controlled by the bot herder or bot-master is known as a bot. Type Algorithms Description Use Cases Supervised Splits data into branches Detecting botnet C2 1. Decision Trees Learning to classify traffic. traffic. Finds a hyperplane to 2. Support Vector Classifying botnet traffic in separate normal and Machines (SVM) network flow data. botnet traffic. Detecting botnet traffic by Unsupervised Groups traffic into clusters 1. K-Means Clustering clustering similar Learning based on similarity. behaviors. Identifies outliers as Detecting botnet traffic as 2. DBSCAN anomalies based on outliers in network data. density. Extracts spatial features Detecting botnets in 1. Convolutional Neural Deep Learning from traffic data (e.g., image-like traffic Networks (CNNs) traffic matrices). representations. Captures temporal 2. Long Short-Term Identifying botnet C2 dependencies in Memory (LSTM) communication over time. sequential traffic data.