Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 242-247· 0 citations· 17 references
Abstract
Due to the rising frequency as well as complexity of Cyber-attacks the real-time Intrusion Detection Systems (IDS) have a greater demand for reliable. Conventional IDS techniques frequently encounter performance limitations when dealing with high-dimensional data as well as temporal patterns. In order to efficiently detect and prevent cyber-attacks, this research offers a hybrid Swin Transformer and Recurrent Neural Network (RNN) model with Principal Component Analysis (PCA) for dimensionality reduction. To identify time-dependent patterns and spatial linkages in network traffic, spatial and temporal learning modules are used. To handle complicated data and retain high predicted accuracy, a hybrid model that combines the advantages of the Swin Transformer and RNN is used for training. Using Network Intrusion dataset (CIC-IDS-2017) from kaggle delivers accuracy, precision, F1-score, as well as AUC-ROC measures for the proposed method is of 99.9%. Method delivers an accessible as well as effective resolution for contemporary cyber security requirements by addressing the difficulties of real-time detection in high-dimensional datasets.
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...
S. Nagendrudu, Shaik Mohammed Anays, F. Mahammad et al.· 2026 International Conferenc...· 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
The hybrid intrusion detection system model of extreme gradient boosting (XGBoost) and KMeans++ clustering algorithm to balance the trade-off between accuracy, efficiency, and robustness in detecting malicious traffic is proposed.
Premananda Sahu, V. Himthani, Ashwani Kumar· International Research Journ...· 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.
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.
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
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