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
The growing amount of heterogeneous devices with scarce resources is compromising the security of the Internet of Things (IoT), as they are more likely to adapt to a fixed and identity-based access control. Conventional…
This research provides a comprehensive synthesis of Multimodal Machine Learning (MML) as a transformative paradigm for IoT defense. By integrating heterogeneous data streams, including network flow statistics, device-lev…
This study presents an investigation of the HiTar-2024 dataset performed in terms of the distribution of label attack types and the distribution of attacks by protocol, normal, and Denial of Service (DoS) connections ove…
Ransomware is one of the most dangerous cyber threats today, as it can disrupt systems and cause serious financial losses. Traditional detection methods often fail to catch newer attacks because they can hide within norm…
Early detection of cyberattacks remains a major challenge in enterprise networks due to encrypted traffic, protocol diversity, and highly dynamic service behavior. This study evaluates a machine learning-based intrusion…
The rapid expansion of Internet of Things (IoT) deployments has increased the exposure of interconnected devices to cyber threats, particularly in heterogeneous and resource-constrained environments. Although recent rese…
A rapid increase in the instances of cyberattacks has been observed with the expanding digitization. This leads to an urgent and critical need for developing robust intrusion detection systems (IDS) which can identify th…
The rapid growth of computer networks has increased demand for more sophisticated tools for network traffic analysis and monitoring. The increasing reliance on networks has amplified the need for robust security and intr…
The increasing connectivity of systems and the rapid growth of the Internet have intensified cybersecurity threats. It has been demonstrated that conventional signature-based intrusion detection methods are deficient, es…
The Internet of Medical Things (IoMT) environment is highly sensitive due to the nature of medical data and its direct connection to patient health, making it a prime target for sophisticated cyberattacks. This study exp…
