This article presents an advanced IDS that uses deep learning, specifically stacked Long Short-Term Memory (LSTM) and the CatBoost algorithm, to detect anomalies in network traffic to monitor and detect the cyber threats in real-time.
A two-tier hybrid IDS that uses a Random Forest model for quick initial detection and a Neural Network for deeper analysis of suspicious traffic is proposed that provides a balanced and efficient solution that overcomes key limitations of existing IDS models and offers a pathway towards a more robust real-time IoT intrusion detection.
Research Paper, Wong Zoey, Yu Watanabe et al.· International Journal of Eme...· 0 citations
Security researchers rely heavily on Network Intrusion Detection Systems (NIDS) to keep an eye on network traffic and notify administrators of any suspicious activities. The purpose of this paper is to offer a comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS. This covers several detection methods, including signature-based, anomaly-based, specification-based, and behavior-based approaches, as well as their advantages and disadvantages in recognizing both existing and new cyber threats. The review also covers the architecture of NIDS which consists of network sensors, preprocessors, network traffic analysis, alert generation and security analysis. A variety of ML techniques, including supervised, unsupervised, semi-supervised, ensemble, and deep learning (DL) approaches, are being explored to improve the accuracy and adaptability of intrusion detection systems (IDS). Other applications such as DoS/DDoS attack detection, Malware detection, Botnets, Brute force attacks, Insider compromise, IoT compromise and Critical infrastructure threats are also shown. Despite all the challenges in terms of false positives, scalability, computational complexity, data quality, and novel attack styles, the features that ML can provide for intelligent, adaptive, and accurate intrusion detection systems are appealing.
Madhav Sharma· International Journal of Cyb...· 0 citations
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Ranobir Hasan, H. Jamal, Kamal Kamal et al.· The Eastasouth Journal of In...· 0 citations
A thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles, foreseeing an all-encompassing solution to modern network security issues.
Gaurav Kishor Saxena, Shambhu Dayal Sahu· International Journal of Cre...· 0 citations
The exponential rise in cyber threats has created a critical need for intelligent and adaptive intrusion detection systems
(IDS) capable of identifying both known and emerging attack patterns. Traditional rule-based IDS mechanisms, such as Snort,
rely heavily on predefined signatures and struggle against sophisticated attacks including port scanning, web-based exploits, and
distributed denial-of-service (DDoS) attacks. This paper presents an AI-based Intrusion Detection System that integrates
network simulation, machine learning, and real-time visualization into a unified three-layer framework. The NS-3 network
simulator generates realistic normal and malicious traffic between attacker, router, and victim nodes; the resulting packetcapture (PCAP) data is processed by a Python-based IDS engine that applies signature rules for port scanning, DoS flooding,
and web attacks (SQL Injection, XSS, LFI, command injection); and a Random Forest classifier, trained on the CIC-IDS2017
benchmark dataset, augments detection with machine-learning-based classification. A Flask-based web dashboard provides realtime visualization of alerts, packet statistics, and attack distribution. Experimental results show an average detection accuracy of
98.5%, an average F1-score of 97.7%, and a false-positive rate below 1.2%, outperforming rule-based and prior deep-learning
baselines on comparable attack categories. The proposed multi-layered architecture demonstrates that combining simulation,
machine learning, and visualization can produce a scalable and effective solution for modern network security challenges.
T. Senthil, V. Shanmuganeethi· International Journal for Re...· 0 citations
Experimental results demonstrate that the proposed model achieves high detection accuracy, strong discriminative capability, and low false alarm rates across both datasets, confirming its effectiveness and scalability for next-generation cybersecurity applications.
