In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

With the increasing day-to-day acceptance of IOT computing, the issues related to it are also getting more attention. The current IOT computing infrastructure brings some security challenges concerned with the users/customers and CSP. The users can store their confidential data at IOT storage and can access them anytime when they need. Lack of trust exists among IOT users and between IOT users and CSP. The prevention of this risk is a big research issue and it needs to be solved. There is a need for trusted IOT computing in recent times to provide trusted services. Here, we propose the integration of TPM in IOT computing to performs cryptographic operations and provide hardware-based security. In this domain, different schemes and methods have been proposed to build trust in IOT computing, but the suitable solution has not been presented by these schemes because these schemes lack in terms of some security services. A comparative study based on trusted computing schemes has also been presented in this paper along with different implementations of critical analysis. Our study is based on an overview of the main issues and summarizing the literature along with their strengths and limitations. In the end, we integrated the trusted platform module in the IOT architecture to establish the trust in IOT computing and to enhance the cybersecurity challenges and evaluated it with the help of mathematical/algorithms/graph theory/matrices and logical diagrams.

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.

Internet of Things-based environments pose various challenges due to their anytime/anywhere computing, and the efficient cryptographic based key management is one of the major challenges in Internet of Things. The key management life cycle consists of initialization, key generation, key registration, key backup, key update, key recovery, and key revocation. Our contribution in this paper is to summarize the state-of-the-art key management schemes and techniques in different scenarios, such as mobile ad hoc networks, wireless sensor networks, and the Internet of Things environments. Further different issues related specifically to the Internet of Things environment are discussed and the causes and effects pertaining to the security breach for Internet of Things are identified. Furthermore, in this research work, we develop a novel permutation of threshold and identity-based key management schemes for the Internet of Things environment and have proposed future directions to counteract the attacks on confidentiality, integrity, authentication, and availability of security services in the Internet of Things environment and identified the two key management schemes, that is, identity and threshold schemes for Internet of Things, to resolve Internet of Things key management issues and maximum possible security services effective implementation. We evaluate our scheme with the help of mathematical and statistical techniques.

Big data refers to numerous forms of complex and large datasets which need distinctive computational platforms in order to be analyzed. Hadoop is one of the popular frameworks for analytics of big data. In Hadoop, a big job is split into multiple small tasks and then they are distributed to worker nodes in a parallel way using MapReduce to speed up computational processes. In this aspect, it is important how to improve throughput performance. MapReduce jobs require quick responses from the worker nodes to complete them under their deadlines. The existing scheduling schemes for Hadoop such as FIFO, fair, and capacity schedulers cannot guarantee the quick response requirement satisfying a prior deadline. Thus, Hadoop system needs to improve response time and completion time for the heterogeneous MapReduce jobs. In this paper, we propose an efficient preemptive deadline constraint scheduler based on least slack time and data locality. In order for better allocation of tasks and load balancing, we first analyze the task scheduling behaviors of the Hadoop platform. Based on that, we propose a novel preemptive approach which considers the remaining execution time of the job being executed in deciding preemption. The experimental results show that the proposed scheme significantly reduces the job execution time and queue waiting time, compared to existing schemes.