The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Intrusion detection is the process of monitoring network traffic or system activity to identify unauthorized access, policy violations, or malicious behavior. Intrusion detection systems are generally classified as signature-based, which match activity against known attack patterns, or anomaly-based, which flag deviations from an established baseline of normal behavior and can therefore detect previously unseen attacks. Deployment architectures include network-based systems that inspect traffic at chokepoints and host-based systems that monitor activity on individual machines, often combined in layered defense strategies. Recent research combining machine learning with IoT network traffic has reported detection accuracy above 99 percent on benchmark datasets such as IoTID20, alongside a broader shift toward deep learning architectures, including transformers, for more effective pattern recognition. Other active areas include federated learning approaches and detecting intrusions in encrypted traffic and industrial control systems. As an open-access intrusion detection journal, IJACSA publishes research evaluating intrusion detection models against benchmark datasets, alongside applied detection systems for specific network environments.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Machine-learning intrusion detectors are usually reported with accuracy or F1 on a single train and test split, and their confidence scores are often read operationally as probabilities without an explicit calibration ch…
Machine learning-based intrusion detection systems are often constrained by severe class imbalance, while generative augmentation may memorize distinctive network-flow records and expose membership information. This stud…
Comparative studies of feature selection (FS) and feature extraction (FE) for IoT intrusion detection rely almost universally on random train/test splits, which let temporally adjacent, highly similar records appear on b…
Smart hospitals deploy Internet of Medical Things (IoMT) sensors and MQTT brokers to stream clinical telemetry over resource-constrained edge gateways. Centralized network intrusion detection systems (NIDS) expose sensit…
The swift deployment of IoT-based smart home appliances has increased the attack surface for the smart environment and exposed it to attacks like botnet command-and-control communications, brute force attacks, denial-of-…
Cloud computing provides scalable infrastructure but introduces critical challenges to data privacy, trust management, and intrusion detection. To address these issues, we present a trust-aware framework that integrates…
Intrusion detection in web traffic remains a challenging task due to the high dimensionality, heterogeneity, and imbalance of normal and malicious requests. This study investigates a hybrid anomaly detection framework co…
With the widespread adoption of the Internet of Medical Things (IoMT), hospitals have become prime targets for cyberattacks. To overcome the limitations of traditional defenses and computationally heavy deep learning mod…
The explosive growth of online education platforms has led to increased exposure to cybersecurity threats, which makes secure Learning Management Systems (LMS) a critical requirement. However, the current methods often c…
New and unknown attack patterns are creating more cybersecurity issues for cloud environments. Intrusion detection systems (IDS) are usually capable of high-performance in closed-world scenarios and are less effective in…
