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A cyber physical system (CPS) is a network of cyber (computation, communication) and physical (sensors, actuators) components which interact with one another in a feedback form with human intervention. CPS authorizes the critical infrastructure and is treated as essential in day to day life as they exist in the basis of future smart devices. The increased exploitation of the CPS results in various threats and becomes a global problem. Therefore, it becomes essential to develop a safe, efficient, and robust CPS for real tine environment. For resolving this problem and accomplish security in CPS environment, intrusion detection system (IDS) can be developed. This study introduces an imbalanced generative adversarial network (IGAN) with optimal kernel extreme learning machine (OKELM), called IGAN-OKELM technique for intrusion detection in CPS environment. The proposed IGAN-OKELM technique mainly aims to address the class imbalance problem and intrusion detection. Besides, the IGAN-OKELM technique involves the IGAN model handling the class imbalance problem by the use of imbalanced data filter and convolution layers to the conventional generative adversarial network (GAN), which generates new instances for minority class labels. Moreover, the OKELM model is applied as a classifier and the optimal parameter tuning of the KELM model is performed by the use of sand piper optimization (SPO) algorithm and thereby improvises the intrusion detection performance. A wide ranging simulation analysis is carried out using benchmark dataset and the results are examined under varying aspects. The experimental results reported the better performance of the IGAN-OKELM technique over the recent state of art approaches interms of different measures. [ABSTRACT FROM AUTHOR]

Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client's proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992. [ABSTRACT FROM AUTHOR]