Next-generation intrusion detection in cyber-physical systems using an ensemble of quantum-inspired and deep neural models

Efficient Machine Learning Approaches for Intrusion Detection Systems in Cyber Security

The rapid expansion and spread of networked systems and digital services has tremendously expanded the complexity and frequency of cyberattacks, and conventional security tools are no longer relevant to contemporary cyber threats. Intrusion Detection Systems (IDS) are very important in detection of malicious activities, but the traditional signature based and rule-based IDS are limited in that they have high false-positive, cannot be able to detect the attacks of the zeroday, and fail to be adapted to changing patterns of threats. The recent developments in machine learning (ML) have brought intelligent and adaptive methods that can learn the complicated patterns based on large volumes of network traffic data. This paper provides an in-depth analysis of effective machine learning methods to intrusion detection system in cybersecurity. The paper compares the efficacy of supervised, unsupervised and ensemble-based ML algorithms that conduct intrusion detection with enhanced accuracy, lowered computation load, and improved scalability. It focuses on the feature selection, dimensionality reduction, and model optimization to enhance the detecting performance and retain the capability of running it in real-time. In the results, the hybrid and ensemble models of machine learning prove to be much more efficient than the conventional IDS methods and provide a strong protection against the current cyber threats. This research contributes toward developing intelligent, adaptive, and efficient IDS frameworks suitable for contemporary and future cybersecurity infrastructures.

A Machine Learning-Based Intrusion Detection Framework for Enhanced Network Security

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.

An Intelligent Intrusion Detection Framework for Cyber Security in Hybrid Network Environments

In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to decrease in resource-limited environments. The paper presents the enhanced intrusion detection system based on deep learning architecture, which can be flexible, adaptive and as well maintain the high detection capability with confidence under changing or to-be changed network settings. The objectives of this and aforementioned also address the issue of avoiding strong overtting behavior by models during the transfer learning and even rich feature representation through the first-stage operation: moving to address and ideally preventing attacks rather than supporting other attacks. The work is tailored to the deployment of the light-weight and adaptive IDS design which is supposed to be large enough to work in real time on low-powered devices such as IoTs and edge devices that are nondominated in energy and computationally less demanding. Real-time adaptability of the model will be examined through operational deployment simulations. It is also expected that such simulations would take into account latency, throughput, and energy consumption of the IDS model. On the one hand, In a stage nested within the very last period of this research, the IDS model has been merged with Explainable AI technologies; now LIME and SHAP are also preserved to improve the interpretability of the model decisions and the level of decision-making. What kind of feature attributions are made with the intrusion data? How is Interpretability of the model evaluated in terms of fidelity, comprehensibility, and expert belief? Therefore, all the above-mentioned events will be a perfect example of how the technologically ingrained tasks, particularly in the technical discipline of security studies, can be wrapped into the very cognitive resource of human beings.

AI-Enabled Threat Detection using Deep Learning

The advent of Internet of Things (IoT) and Cyber-Physical Systems (CPS) has led to the rapid development of highly dynamic and complex communication infrastructures in various domains ranging from healthcare, transportation, industrial automation to intelligent energy systems. Even though intrusion detection and network security is a wellresearched research area, existing intrusion detection systems cannot efficiently overcome shortcomings such as unknown threat detection, false-positive alert detection, network adaptivity and improved accuracy with large-scale real-time heterogeneous traffic data. In order to overcome these challenges, this paper proposes an AI-enabled threat detection framework using hybrid deep learning techniques for intelligent cyber threat analysis and intrusion detection. First, network traffic data is pre-processed, Min-Max-normalized, and enhanced by feature selection along with the Principal Component Analysis (PCA)-based dimension reduction to minimise the redundancy and to improve the quality of the dataset. Second, the optimized feature set is leveraged for AIenabled detection of anomalies using Autoencoder, spatial traffic patterns detection using Convolutional Neural Network (CNN), and the temporal dependencies of virtual attacks in traffic data using an LSTM-Recurrent Neural Network (LSTM-RNN). Finally, a hybrid Deep Neural Network (DNN) and Decision Tree classifier on the output of the hybrid model, classifies the normal and malicious network traffic with a reduced false-positive rate. Experimental results of the proposed framework on network intrusion datasets confirmed the efficiency of the proposed framework significantly outperforming the existing standalone deep learning approaches in terms of accuracy, precision, recall, F1-score, scalability, and real-time cyber threat detection.

Artificial Intelligence based Intrusion Detection System

Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks

The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.