Supervisory control and data acquisition (SCADA) systems are vulnerable to cyberattacks; hence, cybersecurity is a major concern. Hybrid methodologies using advanced machine learning (ML) may increase intrusion detection and system security. The intrusion detection algorithms have little adaptability, high false-positive rates for novel threats, and restricted feature extraction. SCADA systems are subject to sophisticated attacks. This study’s hybrid autoencoder-hybrid ResNet–long short-term memory (LSTM) (HAE–HRL) architecture includes deep feature extraction, anomaly detection, and sequential analysis. This framework uses these three methods to improve threat detection. AI can scan massive amounts of data and find patterns humans and traditional systems miss. The hybrid approach gives defenders an unequal edge. Autoencoders identify anomalies, convolutional neural networks (CNNs) extract features, and hybrid ResNet–LSTM learns temporal patterns. Cyber risks are correctly classified using this method. With SCADA security and intrusion detection, the model may considerably enhance network abnormality and hostile activity detection. According to experimental tests, HAE–HRL reduces false positives and improves detection accuracy, making it a robust cybersecurity solution.

Supervisory control and data acquisition (SCADA) systems monitor and supervise our daily infrastructure systems and industrial processes. Hence, the security of the information systems of critical infrastructures cannot be overstated. The effectiveness of unsupervised anomaly detection approaches is sensitive to parameter choices, especially when the boundaries between normal and abnormal behaviours are not clearly distinguishable. Therefore, the current approach in detecting anomaly for SCADA is based on the assumptions by which anomalies are defined; these assumptions are controlled by a parameter choice. This paper proposes an add-on anomaly threshold technique to identify the observations whose anomaly scores are extreme and significantly deviate from others, and then such observations are assumed to be ”abnormal”. The observations whose anomaly scores are significantly distant from ”abnormal” ones will be assumed as ”normal”. Then, the ensemble-based supervised learning is proposed to find a global and efficient anomaly threshold using the information of both ”normal”/”abnormal” behaviours. The proposed technique can be used for any unsupervised anomaly detection approach to mitigate the sensitivity of such parameters and improve the performance of the SCADA unsupervised anomaly detection approaches. Experimental results confirm that the proposed technique achieved a significant improvement compared to the state-of-the-art of two unsupervised anomaly detection algorithms.

Intrusion detection systems (IDSs) based on machine learning algorithms represent a key component for securing computer networks, where normal and abnormal behaviours of network traffic are automatically learned with no or limited domain experts’ interference. Most of existing IDS approaches rely on labeled predefined classes which require domain experts to efficiently and accurately identify anomalies and threats. However, it is very hard to acquire reliable, up-to-date, and sufficient labeled data for an efficient traffic intrusion detection model. To address such an issue, this paper aims to develop a novel self-automatic labeling intrusion detection approach (called SAL) which utilises only small labeled network traffic data to potentially detect most types of attacks including zero-day attacks. In particular, the proposed SAL approach has three phases including: (i) an ensemble-based decision-making phase to address the limitations of a single classifier by relying on the predictions of multi-classifiers, (ii) a function agreement phase to assign the class label based on an adaptive confidence threshold to unlabeled observations, and (iii) an augmentation labeling phase to maximise the accuracy and the efficiency of the intrusion detection systems in a classifier model and to detect new attacks and anomalies by utilising a hybrid voting-based ensemble learning approach. Experimental results on available network traffic data sets demonstrate that the proposed SAL approach achieves high performance in comparison to two well-known baseline IDSs based on machine learning algorithms.

This paper discusses an approach for fast learning in big data. The proposed approach combines momentum factor and training rate, where the momentum is a dynamic function of the training rate in order to avoid overshoot weight to speed up training time of the back propagation neural network engine. The two factors are adjusted dinamically to assure the fast convergence of the training process. Experiments on 2-bit XOR parity problem were conducted using Matlab and a sigmoid function. Experiments results show that the proposed approach signifcantly performs better compare to the standard back propagation neural network in terms of training time. Both, the maximum training time and the minimum training time are significantly faster than the standard algorithm at error threshold of 10^-5.

The recent frame of IoT and Industrial IoT security brings new type of intrusion detection systems. We propose a new method for Intrusion Detection in IoT based EVCS with the integration of Convolutional Neural Network, Long Short-term Memory model and Gated Recurrent Unit. This work uses a naturally pervasive, and representative real-world security focusing on IoT typical applications to address the capillary layer inherent challenges (e.g. at each EVCS vs through IT infrastructure as compared peripheral equipment). We have tested this both with Binary and Multiclass exhaustively. The benchmarks are perfectly accurate (100 % binary class, 97.44% six-class classification and near ~96/90%% in fifteen classes that certainly sets a new bar for the going forward!) Collectively, the entire ensemble architecture demonstrates a scalable high performance mean so again this ways these accomplishments certify that CNN-LSTM-GRU-based complex models can be used in space-strapped and processing constrained Intrusion Detection system for IoT to perform consistently robust. The ensemble algorithm, which is open sourced on the GitHub under our simulation framework codebase also represents a significant improvement in securing Internet of Things based EVCS from various cyber security threats.