Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. Machine Learning (ML) and Artificial Intelligence (AI) have the potential to make these the worst of moments, but it may also be the finest of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. Hence, in this paper, we propose a novel cyberattack detection framework by integrating AI and ML (ML) methods. Here, initially we collect the dataset from the CPS database and preprocess the data using normalization for removal of errors and redundant data. The features are extracted using Linear Discriminant Analysis (LDA). We have proposed Self-tuned Fuzzy Logic-based Hidden Markov Model (SFL-HMM) with Heuristic Multi-Swarm Optimization (HMS-ACO) algorithm for detection of the cyberattacks. The proposed method is evaluated using the MATLAB simulation tool and the metrics are compared with existing approaches. The results of the experiments reveal that the framework is more successful than traditional strategies in achieving high degrees of privacy. Furthermore, in terms of detection rate, false positive rate, and computing time, the framework beats traditional detection algorithms

Detection of hand gestures with human computer recognition by using support vector machine

Many applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition. We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time. Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level. Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings. It's also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data. In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset. On practically all subsets, the proposed base architecture improved classification accuracy. When the entire dataset was used, there was even a modest improvement. Correct identification could be regarded as a quality indicator. The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier