Biblio

Cyber Physical Systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical Systems are a crucial component of Internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the Internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective Network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection (NSRM-AD) using deep learning model is proposed. The security levels of the proposed model are contrasted with the proposed model and the results represent that the proposed model performance is accurate

Performance and improved packet handling capacity against high traffic load are important requirements for an effective intrusion detection system (IDS). Snort is one of the most popular open-source intrusion detection system which runs on Linux. This research article discusses ways of enhancing the performance of Snort by modifying Linux key parameters related to NAPI packet reception mechanism within the Linux kernel networking subsystem. Our enhancement overcomes the current limitations related to NAPI throughput. We experimentally demonstrate that current default budget B value of 300 does not yield the best performance of Snort throughput. We show that a small budget value of 14 gives the best Snort performance in terms of packet loss both at Kernel subsystem and at the application level. Furthermore, we compare our results to those reported in the literature, and we show that our enhancement through tuning certain parameters yield superior performance.

For the security enhancement of the conventional visible light (VL) communication which allows the easy intrusion by adjacent adversary due to visible signal characteristic, the VL communication technique based on the asymmetric Rivest-Shamir-Adleman (RSA) encryption method is proposed for smart indoor service in this paper, and the optimal key length of the RSA encryption process for secure VL communication technique is investigated, and also the error performance dependent on the various asymmetric encryption key is analyzed for the performance evaluation of the proposed technique. Then we could see that the VL communication technique based on the RSA encryption gives the similar RMSE performance independent of the length of the public or private key and provides the better error performance as the signal to noise ratio (SNR) increases.