Security is an essential service for wired and wireless network communication. This work concerned with a particularly sever security attack that affects the ad hoc networks routing protocols, called "wormhole attack". There are many solutions to detect and prevent this attack like packet leashes, cluster base, hop count analysis etc., but none of them is perfect solution. This paper contains a proposal for new technique for wormhole avoidance. Proposed technique has been implemented with NS2 simulator over the DSR protocol. This technique for wormhole avoidance addresses the malicious nodes and avoids the routes having wormhole nodes without affecting the overall performance of the network. The performance metrics used for evaluating network performance are jitter, throughput and end to end delay. The performance of proposed techniques is good.

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

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Asymmetric denial-of-service (DDoS) attacks have become very complicated to deal with because of the use of several Internet Protocol (IP) addresses in these types of attacks. The purpose of research is different strategies are used by an attacker to achieve the objective of forcing the unavailability of the targeted server. Sometimes the large size of the file is used with a higher transfer rate so that the targeted website could be hanged for a legitimate customer. The design of a novel mechanism, in the form of Dynamic Honey Link (DHL), to prevent identification by sophisticated DDoS attacking tools, the findings this developed here for the detection of asymmetric attacks. Parameters such as IP addresses, Time of Request, and difference of time between requests of IPs are used in this mechanism and are verified applying correlation coefficients and p-values. This new analysis of different website attacks using an analysis of dataset leads to inspection and identification of hyperlinks.

One of the main problems of networked control systems is that signal transmission delay is inevitable due to long distance transmission. This will affect the performance of the system, such as stability range, adjustment time, and rise time, and in serious cases, the system cannot maintain a stable state. In this regard, a definite method is adopted to realize the compensation of network control system. To improve the control ability of mobile sensor network time delay system, the control model of mobile sensor network time delay system based on reinforcement learning is proposed, and the control objective function of mobile sensor network time delay system is constructed by using high-order approximate differential equation, combined with maximum likelihood estimation method for parameter estimation of mobile sensor network time delay, the convergence of reinforcement learning methods for mobile sensor network control and adaptive scheduling, and sensor network time delay system control model of multidimensional measure information registration in strengthening tracking learning optimization mode to realize the adaptive control of mobile sensor network time delay system. The simulation results show that the proposed method has good adaptability, high accuracy of estimation of delay parameters, and strong robustness of the control process.