Sep 2026· Information Security Journal: A Global Perspective· Vol 35, pp. 708 - 720· 0 citations· 29 references
TL;DR
Experimental outcomes indicate that EL-NTP demonstrates comparable detection capabilities to several previously established NI detection (NID) methods and is proficient in recognizing a diverse array of NI types.
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
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
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
Applying Artificial Intelligence (AI) to the context of networking systems has greatly enhanced the ability to detect, protect, and manage the kind of flows in networks in real time. This research explores the relationship between AI and networking, and more particularly, the extent to which AI technology can be used to enhance, protect, and scale the NIDS using the NSL-KDD dataset. This research focuses on the following objectives: (a) enhancing the performance of network intrusion detection models, and (b) strengthening the defense mechanisms against dangerous new forms of cyber threats, and (c) making flexible technical solutions for effectively managing large and dynamically changing networks. To address these objectives, different classification algorithms such as SVM of different kernels, KNN, Random Forest, and Decision trees are applied on the NSL-KDD dataset. Testing accuracy, precision, recall, F1 score, and cross-validation score are used to evaluate them. In addition, feature extraction algorithms, including both fuzzy and correlation-based methods, are applied to reduce the model’s computational complexity. Targets of the investigation include the evaluation of each model and feature extraction, and its impact on the model’s ability to predict. The results affirm that, compared with other methods, SVM-based models with Gaussian and Sigmoid kernels are particularly more accurate (up to 99%) and less sensitive to fluctuations.
Anish Antony, S. Thaseen, Ashvini Alashetty et al.· Discover Artificial Intellig...· 0 citations
As MNI becomes increasingly vulnerable to new kinds of attacks from the cyber world, accurate and timely detection of intrusions becomes a primary key to the power of cybersecurity. More complex attack patterns, complex traffic interactions within large scales are not very collaborable with the typical signature-based detection methods. A Transformer Based Network Anomaly Detection System for intelligent cyber security monitoring based on network flow analysis (NFAs) is proposed in the paper. This framework is derived from the CICIDS2017 data-set and proposes 78 of the statistical flow characteristics, where each flow characteristic impacts the behaviour of a packet, protocol, volume of traffic and temporal communication pattern. The model uses a Transformer Encoder network architecture along with multiple heads of self-attention, which provides greater understanding to deal with complex relationships between features from network traffic. LabelEncoder and StandardScaler have been applied to the columns with values that need to be encoded for categorical variables and scaled to fit the values for models training. A trained model is then applied to progress multiple different categories of cyberattacks including DDoS attacks, PortScan, Brute Force, Botnet, Web Based, etc. and different kinds of traffic, all traffic is considered benign traffic. For providing real-time predictions, confidence interval, prediction of class severity and alerts using trained model an API developed on flask to connect the trained model to a dashboard was built. The experimental results show that the Transformer-based learning could be very effective in achieving successful capturing of the network behavior and conducting realistic detection. The overall proposed system offers an intelligent, scalable and deployment-centric approach to improve the monitoring and proactive detection of threats in contemporary networks in the field of cybersecurity.
S. Nagendrudu, Shaik Mohammed Anays, F. Mahammad et al.· 2026 International Conferenc...· 0 citations
An OFLN by addition of PSO to perform automated hyperparameter optimization and MAML to enable high-speed adaptation to new and previously seen attacks is proposed, which is more effective than baseline ensemble models in detection accuracy, resistance to class imbalance and training efficiency.
