Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-5· 0 citations· 22 references
Abstract
In Cybersecurity, Intrusion detection system is a tool that is used to identify the abnormal activity on the network in a timely manner. This is done by inspecting and analyzing the features of network packets to find any anomaly in them. In high dimensional traffic, the intrusion detection system needs to inspect and analyze all the features of a network packet to make the decision, which is an overhead. Feature reduction techniques are used to map the existing feature set to a new but less feature space to make the intrusion detection system more efficient. In this work we compared the performance of three commonly used feature reduction techniques, viz. Principle Component Analysis, Independent Component Analysis and Linear Discriminant Analysis. We used k-Nearest Neighbor and Naïve Bayes algorithm as classifiers. It was observed that Linear Discriminant Analysis resulted in improved accuracy of 95-100% and reduced false alarms between 0-0.5%.
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
: As the internet usage is exponentially increasing and with the emerging cyber threats, the conventional rule-based intrusion detection systems (IDS) are limited to identifying new and advanced attacks. The paper features an artificial intelligence-powered Intrusion Detection System (IDS) utilizing the machine learning methods to detect the malicious actions in the network traffic automatically. The system based on the CICIDS datasets, data preprocessing, feature selection, and a Random Forest classifier was used to determine various types of intrusions with a high success rate. The capability of the system to classify intrusions, generate confidence scores and be interpretable with feature importance analysis is evidenced by real-time packet-level simulation. Besides, the model is combined with a notification system that will notify through email or SMS in case of intrusions, so that timely response and mitigation is realized. The suggested framework does not only improve the detection performance, it also brings in scalability, automation, and transparency, which is appropriate in the present day network setup.
S. S, Martin Victor· Proceedings of the 1st Inter...· 0 citations
Cyber intrusion detection in sensor network domains is quite complicated, mainly because of high, dimensional traffic features, redundant data, and strict resource limitations. To overcome these problems, a hybrid intrusion detection system incorporating Principal Component Analysis (PCA), Fuzzy C-Means (FCM) clustering, and a Multilayer Perceptron (MLP) classifier was introduced in this study for efficient and accurate threat detection. While PCA was used to decrease the dimensionality of features and retain the most informative network characteristics, FCM clustering was focused on providing cluster-based membership features that reveal the inner pattern of the traffic. These dimensionally reduced features and the membership features were combined to create a vector of enhanced features and then used in training the MLP model for the binary intrusion classification task. A masking mechanism is placed in the input layer of the MLP which emphasizes intrusion related features and de-emphasizes redundant or noisy features to improve feature learning and reduce computational cost. Various standard performance metrics thereby accuracy, precision, recall, and F1, score were employed to assess the proposed method and achieved the accuracy of 97.24%. Besides, the hybrid masked MLP turns out to be an effective and dependable solution to the problem of cyber intrusion detection in sensor network domains.
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 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.
Muhammad Moosa, B. Naseem, Sana Alam et al.· 0 citations
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