The increasing sophistication of malware and the rise of zero-day attacks pose significant challenges to intrusion detection systems (IDS). This necessitates a new taxonomy for comparing IDSs to effectively evaluate their capabilities in detecting both known and unknown threats. This article explores the evolution of intrusion detection, examining existing taxonomies and proposing a framework for comparing IDSs based on detection methodologies, data sources, and techniques for implementing anomaly-based intrusion detection systems (AIDS). We also discuss performance metrics, publicly available datasets for evaluation, feature selection techniques, types of computer attacks, IDS evasion techniques, and current challenges in the field.
The Evolution of Intrusion Detection: From Signatures to Anomalies
Traditional signature-based intrusion detection systems (SIDS) rely on matching observed activity against a database of known attack patterns. While effective for known threats, SIDS struggle with zero-day attacks and polymorphic malware. This limitation has led to the development of anomaly-based intrusion detection systems (AIDS), which profile normal system behavior and flag deviations as potential intrusions.
A Taxonomy for Anomaly-Based Intrusion Detection Systems
AIDS can be categorized based on various factors, including:
Detection Methodologies:
- Statistics-based: These methods establish a statistical profile of normal behavior and identify deviations as anomalies. Techniques include univariate, multivariate, and time series analysis.
- Knowledge-based: Leveraging expert knowledge, these systems define normal activity through rules and flag violations as intrusions. Examples include finite state machines, description languages, and expert systems.
- Machine Learning-based: These methods employ algorithms to learn patterns from data and identify anomalies. This category can be further divided into supervised, unsupervised, semi-supervised, and ensemble methods.
Data Sources:
- Host-based IDS (HIDS): Analyze data from individual hosts, such as system logs and application audits.
- Network-based IDS (NIDS): Monitor network traffic for suspicious patterns.
Techniques for Implementing AIDS and Performance Evaluation
Various machine learning techniques, including decision trees, naive Bayes, support vector machines (SVM), and clustering algorithms, have been employed in AIDS. Performance evaluation relies on metrics such as true positive rate (TPR), false positive rate (FPR), and accuracy, often visualized using Receiver Operating Characteristic (ROC) curves.
Datasets and Feature Selection
Publicly available datasets like KDD Cup 99, NSL-KDD, and CICIDS 2017 are crucial for evaluating IDS performance. Feature selection techniques, including wrapper and filter methods, are used to reduce data dimensionality and improve detection accuracy.
Computer Attacks and IDS Evasion
Understanding the landscape of computer attacks, including Denial of Service (DoS), probing, User-to-Root (U2R), and Remote-to-Local (R2L) attacks, is essential for developing effective IDSs. Attackers employ evasion techniques such as fragmentation, flooding, obfuscation, and encryption to bypass detection.
Challenges and Future Directions
Current challenges in IDS research include improving detection accuracy, reducing false positives, addressing evasion techniques, and developing specialized IDSs for critical infrastructure like Industrial Control Systems (ICSs). The development of a comprehensive taxonomy for comparing IDSs is crucial for guiding future research and ensuring the robust security of computer systems. Addressing the challenges of evolving malware and sophisticated evasion techniques requires ongoing innovation in detection methodologies, data analysis, and the integration of diverse approaches.
