Intrusion Detection Systems: Overview & Advances

• IDS is a cybersecurity system that monitors and analyzes network and host activities to detect malicious behavior.
• IDS utilize signature-based, anomaly-based, and specification-based methods to balance low false positives with the detection of novel threats.
• Modern IDS integrate distributed, collaborative, and machine learning techniques to enhance real-time threat detection and system resilience.

An Intrusion Detection System (IDS) is a hardware or software entity that monitors activities in a computer system or network, analyzes observed events for evidence of malicious or policy-violating behavior, and signals alerts or logs when a possible intrusion is detected. IDSs address the practical reality that not all vulnerabilities can be foreseen or perfectly blocked; hence, ongoing, adaptive observation is a foundational pillar of network security. They are integral to supporting the confidentiality, integrity, and availability (CIA) of digital assets and are embedded throughout operating networks in forms ranging from host-level audit agents to network packet sniffers and fully distributed, multi-agent architectures (Yeo et al., 2017, Sen, 2010).

1. Taxonomy and Architectural Paradigms

IDSs may be categorized by deployment locus, functional approach, and level of distribution:

• Host-based IDS (HIDS): Reside on endpoints to monitor low-level events such as system calls, application logs, and file-system modifications. HIDS offer detailed context and visibility into encrypted activities but incur overhead per host and present management complexity (Yeo et al., 2017, Coulibaly, 2020).
• Network-based IDS (NIDS): Deployed at strategic points in the network (e.g., tap or SPAN ports), NIDS examine packet flows for malicious content or behavior patterns. They excel at centralized monitoring with low per-host resource demand but struggle with deep packet inspection in encrypted environments and can be susceptible to evasion (Yeo et al., 2017, Bahrami et al., 2012).
• Distributed/Hybrid IDS: Combine features of HIDS and NIDS, coordinating across multiple sensors using peer-to-peer communication or correlation engines. This grouping enables the detection of distributed and coordinated attacks, and supports advanced resilience features such as fault-tolerant isolation and trust management (Sen, 2010, Davies et al., 23 Apr 2025).

IDSs are further distinguished as passive (detection only) or active (incorporating prevention components, i.e., IDPS), with modern systems often integrating both logging/alerting and automated response orchestration.

2. Detection Methodologies and Algorithms

IDSs use distinct approaches to discern benign from malicious activities:

• Signature-based Detection: Compares observed events to a database of known attack patterns (e.g., byte sequences, protocol signatures). Efficient for known threats, this approach offers low false-positive rates but is ineffective against previously unseen or polymorphic attacks (Yeo et al., 2017, Coulibaly, 2020). Algorithms include regular expression matching, finite state machines, and hash-based classifiers.
• Anomaly-based Detection: Builds probabilistic or machine learning models of "normal" behavior, flagging deviations as potential attacks. Common methods span parametric statistics (e.g., Gaussian models), unsupervised clustering (K-means, hierarchical), and supervised ML algorithms such as SVM, k-NN, and neural networks (MLP, CNN, RNN, Autoencoder) (Zamani et al., 2013, Akter et al., 2024, Parhizkari et al., 2020). These approaches can detect zero-day threats, but typically incur elevated false-positive rates due to normal behavior variability.
• Specification-based Detection: Encodes accepted behaviors using formal specifications (e.g., protocol state machines, temporal logic rules). Attacks are detected as deviations from these specifications, offering robust detection of protocol abuses but limited flexibility (Yeo et al., 2017).
• Hybrid/Ensemble Approaches: Fuse signature and anomaly detection, often via staged or ensemble machine learning architectures (e.g., voting, stacking, or cascading). These exploit the low false positive rates of signature detection and the zero-day coverage of anomaly models (Andalib et al., 2020, Gharib et al., 2019, Sen, 2010).

Representative mathematical models include Bayesian networks (global distribution partitioned across agents), SVM decision functions using kernel methods, and autoencoder anomalies based on reconstruction error (Sen, 2010, Zamani et al., 2013, Gharib et al., 2019). Additive ensembles or meta-learning strategies are common in high-performance IDSs (Andalib et al., 2020, Akter et al., 2024).

3. Distributed and Collaborative IDS Architectures

Robust, large-scale IDS implementations increasingly rely on multi-agent or collaborative designs:

• Peer-to-peer agent frameworks: In distributed IDSs such as those using Multiply Sectioned Bayesian Networks (MSBN), each host runs dedicated agents responsible for monitoring, inference, and local decision-making. Registry agents manage which features or variable classes are managed by which subsystem. Local Bayesian subnets allow for the decomposition of global attack knowledge and scalable communication (Sen, 2010, Sen, 2010).
• Collaborative detection with centralized aggregation: Configurations using multiple Snort sensors forward alerts via syslog to a central node and SIEM platform, where cross-sensor correlation and alert aggregation dramatically reduce alert fatigue and improve detection of distributed attacks. Sophisticated logic ensures correlated alerts and supports threshold-based aggregation across multiple timescales or physical vantage points (Davies et al., 23 Apr 2025).

Byzantine Fault-Tolerant Consensus: To contend with node compromise, consensus protocols such as the Signed Message Algorithm (SMA, a variant of the Byzantine Agreement Protocol) are used so that all honest agents reach agreement on which hosts are trustworthy. Compromised nodes are quarantined through a combination of ACL enforcement and exclusion from registry services (Sen, 2010, Sen, 2010).

SIEM integration and real-time analytics are common in large-scale deployments, supporting visualization and advanced analytics over aggregated events from multiple distributed nodes (Davies et al., 23 Apr 2025).

4. Performance Metrics and Empirical Validation

IDS evaluation is grounded in standard classification metrics:

• Detection Rate (DR) or True Positive Rate: $DR = \frac{TP}{TP+FN}$
• False Positive Rate (FPR): $FPR = \frac{FP}{FP+TN}$
• Precision, Recall, F1-score and Accuracy: Common in comparative studies (Zamani et al., 2013, Gharib et al., 2019, Akter et al., 2024, Abreu et al., 2024)

Resource Overhead: CPU, memory, and network traffic impacts are measured (e.g., <9% average CPU, below 5% network bandwidth in a 50-host agent-based prototype) (Sen, 2010, Sen, 2010). Large-scale collaborative systems report high throughput (>100 k events/2 min), with aggregation logic reducing alert volumes and lowering FPR (e.g., standalone Snort FPR 5% vs. 3% in collaborative deployment) (Davies et al., 23 Apr 2025).

Comparative Analysis: Hybrid Bayesian-agent IDSs demonstrated high DR, especially for DoS (98.25%) and Probe (94.28%), with lower but non-negligible FPR. R2L classes remain challenging, with DR lagging for both classical and advanced systems (Sen, 2010, Sen, 2010). Advanced ML and deep learning models have reported surpassing 99% accuracy and recall in NSL-KDD and CIC-based benchmarks (Akter et al., 2024, Yeo et al., 2017, Zamani et al., 2013).

5. Adaptability, Extensibility, and Self-Learning

Continuous learning and extensibility are central characteristics for modern IDS:

• Knowledge base update modules in agent systems dynamically incorporate confirmed new anomalies, updating Bayesian structures and CPTs to absorb the latest attack pattern as a signature (Sen, 2010).
• Modular architectures (e.g., clearly separated perception, deliberation, and action modules) enable the pluggable addition or replacement of inference mechanisms, supporting alternate ML/distance-based anomaly measures or new detection engines (Sen, 2010, Akter et al., 2024).
• Self-adaptation via meta-diagnosis: In web IDS, local model-based diagnosers validate each other's results, and discrepancies automatically trigger online model adaptation, leading to reduced long-term false positives and improved adaption to previously unseen attack modes (0907.3819).
• Continual unsupervised learning: Frameworks such as CND-IDS employ feature extraction modules that self-update on streaming traffic, supporting unsupervised novelty detection and yielding multi-fold improvements in F1-score over static or batch-trained competitors (Fuhrman et al., 19 Feb 2025).

A core trend is the combination of real-time unsupervised anomaly modeling with ongoing integration of confirmed attack labels for continual enhancement of detection efficacy.

6. Machine Learning, Quantum, and Adversarial Perspectives

Machine learning, deep learning, and quantum ML methodologies underpin the state-of-the-art in IDS:

• Classical ML: Decision Trees (J48/C4.5), SVM, Random Forest, Bayesian Network, and MLP are heavily utilized for both signature and anomaly-based IDS. Decision Trees often provide the best trade-off between accuracy, interpretability, and computational efficiency; hybrid and stacking approaches further enhance performance (Zamani et al., 2013, Alkasassbeh et al., 2018).
• Deep Learning: Architectures such as autoencoders, CNN, GRU/LSTM, and hybrid SCGNet (1D-CNN+GRU) have achieved state-of-the-art detection and attack classification rates exceeding 99% on curated benchmarks. Autoencoder-based semi-supervised cascades offer computational efficiency and improved anomaly discrimination (Akter et al., 2024, Gharib et al., 2019, Ieracitano et al., 2018, Andalib et al., 2020).
• Quantum IDS: QML-IDS frameworks encode network flows into quantum feature spaces, applying VQC, QSVM, and QCNN for attack detection and showing up to 10% improvement in F1-score over best classical models in limited-scale, NISQ-era experiments (Abreu et al., 2024).

Adversarial Challenges: Defending against training data poisoning and adversarial evasion remains active research. Ensuring real-time, robust detection as threats evolve and evasion tactics improve is cited as a principal challenge (Yeo et al., 2017, Coulibaly, 2020, Fuhrman et al., 19 Feb 2025).

7. Future Research and Open Challenges

Key avenues for IDS research include:

• Scaling to encrypted and high-throughput environments: Handling privacy-preserving, high-speed, and cloud-native traffic—where both packet visibility and latency requirements are stringent—demands new approaches exploiting side-channel features or hardware offloading (Bahrami et al., 2012).
• Minimizing false positives while preserving zero-day detection: Hybrid and self-adaptive techniques are explored to address this enduring trade-off (0907.3819, Yeo et al., 2017).
• Dataset Realism and Benchmarks: The development and usage of up-to-date, domain-specific IDS datasets (IoT, mobile, enterprise, network telescope) is crucial for credible evaluation; collaborative and longitudinal datasets are particularly needed (Jindal et al., 2021).
• Integrated, collaborative, and explainable security: Collaborative IDS leveraging multi-sensor, AI-augmented architectures with explainable alert generation and remediation recommendations remain a prominent direction for resilient, real-time cyber-defense (Davies et al., 23 Apr 2025, Coulibaly, 2020).
• Adapting to concept drift and continual learning: Real-world deployments must address the continual evolution of benign and malicious behaviors; online adaptation and continual learning pipelines are imperative (Fuhrman et al., 19 Feb 2025, 0907.3819).

In aggregate, IDS research is converging on architectures that are distributed, adaptive, modular, and capable of leveraging advances in machine learning and quantum computing while remaining robust against distributed, evasive, and dynamically evolving threats. Foundational challenges—including high accuracy for rare/novel threats, scalable real-time deployment, reduction of operational overhead, and autonomous or semi-autonomous self-adaptation—remain at the forefront of ongoing inquiry and system design (Sen, 2010, Yeo et al., 2017, Fuhrman et al., 19 Feb 2025, Akter et al., 2024, Davies et al., 23 Apr 2025).