Jul 2026· Journal of The Institution of Engineers (India) Series B· Vol 107, pp. 1985 - 1993· 0 citations· 16 references
TL;DR
The proposed BAT framework achieves superior classification accuracy of 96%, effectively combining feature-level attention with gradient-boosted ensemble learning to improve detection precision, reduce manual feature engineering, and handle diverse attack categories including DoS, Probe, R2L, and U2R.
The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
Esther J., Grace Phiri, Arockia Venice J.· International Journal of Dat...· 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.
As a technology that connects even more systems and services to the Internet with each passing day, cybercrime has grown out of control globally. Significant problems with traditional IDSs have been uncovered when they are exposed to new attack vectors and advanced evasion strategies not contained in their rule sets and/or signature databases. This paper contains comprehensive research and empirical analysis of 9 machine learning and deep learning algorithms for binary classification of network traffics into normal or malicious network traffic. The benchmark dataset used in this study is the KDD Cup 1999 dataset, which consists of around 494,021 network connection records derived from a relatively large network, characterized with 41 different features of continuous, discrete and categorical attribute types. Its performance was compared with five classical machine learning algorithms, namely decision tree, random forest, support vector machine with a linear kernel, K-nearest neighbours and gaussian naive bayes. Furthermore, four deep learning architectures were studied, a fully connected Artificial Neural Network, a one dimensional Convolutional Neural Network (CNN), a Long Short-Term Memory (LSTM) recurrent network, and an Autoencoder based model for anomaly detection (AD). These models were thoroughly validated with accuracy, precision, recall, F1 score and ROC-AUC on an 80-20 stratified train test partition. Experimental results showed that the ensemble of Random Forest classifiers has the best overall performance with an overall accuracy of 99.98% and a near-perfect value of ROC-AUC (99.99%). Decision Tree obtained a value of 99.97% in terms of accuracy and a near-perfect value of 99.98% for the ROC-AUC measure. The best deep learning models were the ANN (accuracy of 99.95%) and the LSTM (accuracy of 99.95%) with their nearest architectures, followed by the unsupervised model of Autoencoder (accuracy of 98.94%) with the reconstruction error thresholding. The results highlight that the ensemble tree based methods are still highly effective for the structured network traffic classification cases, and a Deep learning-based approach presents an even more competitive solution with the task of feature selection through raw data, which opens doors of opportunity to extract features by various means from raw data for these cases. In addition, the model was deployed practically with a RESTful API built using FastAPI, allowing for the real-time analysis of live traffic with the trained models.
Dhilleswari Palli, Jyothi Musireddy, V. P· International Journal of Res...· 0 citations
The rapid growth of digital communication technologies, cloud computing, and Internet of Things (IoT) devices has
increased both the frequency and sophistication of cyber-attacks, making effective intrusion detection an essential component of
modern cybersecurity systems. Traditional signature-based intrusion detection systems (IDS) are effective against known attacks
but fail to detect previously unseen or evolving threats. This study investigates the application of deep learning models for binary
network intrusion detection using the NSL-KDD benchmark dataset. Three standalone architectures, Convolutional Neural
Networks (CNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are implemented and
evaluated, alongside a CNN-LSTM Hybrid model that integrates spatial and sequential learning, and a DNN-LSTM Ensemble
model that combines independently trained DNN and LSTM predictions through weighted averaging. Following data cleaning,
categorical encoding, normalization, and Random Forest-based feature selection (41 features reduced to 20), all models were
trained and evaluated under identical conditions using Accuracy, Precision, Recall, F1-Score, ROC-AUC, training time, and
inference time. The standalone DNN model achieved the best overall performance, with 80.98% accuracy, 97.08% precision,
68.66% recall, 80.43% F1-score, and 96.11% ROC-AUC, while also requiring the shortest training time (39.69 s). The CNNLSTM Hybrid model attained the highest precision (97.23%) but did not outperform the standalone architectures overall, and
the DNN-LSTM Ensemble produced balanced but not superior results. These findings indicate that carefully designed
standalone architectures can match or exceed the performance of more complex hybrid and ensemble models for binary
intrusion detection, while incurring substantially lower computational cost. The study contributes a controlled, commonframework comparison of five deep learning architectures and provides practical guidance for selecting computationally
efficient models for anomaly-based intrusion detection.
Ketki Naik, Sanjeev Ghosh· International Journal for Re...· 0 citations
The findings indicate that hybrid deep learning techniques can improve network security by enhancing intrusion detection capability while reducing false alarms.
A. O. Jimoh-Mahmud, Abubakar Dayyabu, Abubakar Sadiq Idris et al.· FUDMA Journal of Sciences· 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
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