The particular features of vehicular ad hoc networks (VANETs) make them very vulnerable to attacks, especially when these latter become frequent and have intelligent behaviors.For these, the security of vehicular ad hoc networks is substantially important to protect them from the misbehavior of cyber-attacks.Game theory is one of the important tools that have been proposed to accurately model and analyze attack misbehavior.This paper presents a review of game theory-based intrusion detection, prediction, and reaction in VANETs for enriching the literature and helping design a new game theorybased framework.It gives state-of-the-art of game theory-based frameworks by showing their advantages and weaknesses against attacks.In addition, it determines their players and strategies, the proposed solutions and their descriptions, and the types of attacks envisaged.Then, it treats the challenges of designing an efficient framework for intrusion detection, prediction, and reaction against attacks.

Multiparty computation is essential in offering a better form of non-repudiation, which is not much explored in past research work.A review of existing non-repudiation-based approaches found various shortcomings that do not offer a good balance between robust security and algorithm efficiency.Therefore, the proposed study presents a novel yet simple multiparty computation framework to ensure a higher degree of non-repudiation considering a use-case of a highly distributed and large network, i.e., Internet-of-Things (IoT).The study implements a unique encryption mechanism that uses a transformation strategy to perform encoding while using split key management to retain maximal secrecy and multiparty authentication for enhanced security.The simulation outcome of the study showcases that the proposed scheme offers approximately a 48% reduction in computation overhead, 54% minimization in delay, and 58% faster processing in contrast to frequently reported non-repudiation schemes.

Wireless Network is one of the Internet-of-Things (IoT) prototypes that come up with monitoring services, therefore, influencing the life of human beings.To ensure efficiency and robustness, Quality-of-Service (QoS) is of the predominant point at issue.Congestion in wireless networks will moreover minimize the anticipated QoS of the related applications.Motivated by this, a novel method called, Ornstein-Uhlenbeck Transition and Cache Obliviousness Neural Adaptive (OUT-CONA) to improve congestion control of wireless mesh networks is presented.Adaptive actor-critic deep reinforcement learning scheme on Ornstein-Uhlenbeck State Transition scheduling model to address handovers during data transmission for IoT-enabled Wireless Networks is first designed.Here, by employing the Ornstein-Uhlenbeck state transition scheduling model, both the advantages of the Gauss and Markov Processes are exploited, therefore reducing the energy consumption involved while performing the transition.Next, in the OUT-CONA method, LSTM is imposed for learning the current state representation.The LSTM with the current state representation achieves the objective of controlling congestion with cache obliviousness.The Cache Obliviousness-based Congestion method is utilized for congestion control with obliviousness caching using coherent shielding among organized as well as disorganized data.Furthermore, the performance of the OUT-CONA method is evaluated and compares the results with the performances of conventional techniques, adaptive aggregation as well as hybrid deep learning.The evaluation of the OUT-CONA congestion control method attains better network using lesser misclassification rate, consumption of energy, delay as well as higher goodput using conventional methods in Wireless Mesh Networks.

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

Nowadays, Cloud Computing acts a major role in every field. These days, more large data centers are in service and many small cloud data centers are enlarging all over the universe. Cloud Computing is a catchword in the domain of HPC and offers on-demand services to the resources on the internet. The VMs (Virtual Machines) specified in the cloud data centres may have different specifications and instable resource usage, which causes imbalanced resource utilization within servers. Thus, it leads to performance degradation. Hence to achieve efficient selection of VM, these challenges must be addressed and solved by using meta-heuristics algorithms. In order to process the data, the VMs are placed on the PMs (Physical Machines). There will be multiple and dynamic request of input in the IaaS(Infrastructure as a Service) framework, hence the system’s responsibility is to create a VMs without knowing the types of tasks. Therefore, the fixed tasks scheduling is not right for this system. The most important research area that needs to be addressed is its performance in scheduling. The best and optimal solution is to find out in the cloud environment. Metaheuristics-based algorithms provide the near-optimal solution. In this paper, we proposed an Improved Particle Swarm Optimization algorithm to reduce the makespan and improve the throughput. We have compared our results with adaptive three-threshold energy-aware (ATEA) algorithm and PSO. The investigational results display the proposed Improved PSO algorithm will schedule and balance the load in the dynamic cloud environment better than the other approaches.

Internet is no longer a mere source of information as the concept of interconnectivity has expanded to connect real things or objects like every kind of machines, cars, homes, hospitals, even our bodies through wearable devices. The concept of interconnectivity of billions of objects (mobile or stationary) providing and exchanging real time data is called Internet of Things (IoT). Myriad IoT applications are touching every aspect of our lives and have the latent to develop the basic quality of life for masses. However, prerequisite for successful implementation of any IoT application is uninterrupted and high-quality network connectivity and handling of huge amounts of personal and sensitive user data which gives rise to the questions of security. A handoff authentication protocol with high security and efficiency is required for enabling secure and seamless handoff of mobile nodes between different access points (AP). However, there are number of challenges in designing a secure handoff protocol for IoT systems like limited power of mobile nodes, computational capability, security and vulnerability of open IoT networks. In this paper, we propose a secure and reliable handoff authentication protocol for such IoT devices. Compared with other well-known similar handoff protocols, the protocol proposed here satisfies all relevant security requirements of handoff such as batch verification, mobile node un-traceability, and anonymity and is unaffected by other attacks like replay attacks and also provides mutual authentication. To demonstrate the security strength (against replay attacks) of our protocol, simulation has been done using AVISPA. Thus, protocol proposed by us is more appropriate for IoT environment compared to the alike protocols.

The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Wireless Sensor Network (WSN), there are low capacity, low cost, tiny sensor nodes, and sinks. Sensor nodes detect an event occurring in its surroundings and send data about the event to the sink. Sensor nodes have a limited transmission range and computational power. Since the wireless sensor network operates with limited resources than the ad hoc network, it is difficult to apply the defense method as it is, so research on a new defense method is needed. In a WSN, sensor nodes manage, monitor, and collect data for a specific environmental and physical application, and the collected data is transmitted to and used by a base station. Base stations are connected via the Internet and share data with users. Since the sensor node is composed of low power and low capacity, it is mainly used in an unattended environment, so it is easily exposed to various attacks and can be damaged. This type of network makes it difficult to detect wormhole attacks when they occur along with other attacks like false report injection attacks and Sybil attacks. Therefore, to prevent this, in this study, the hop count and the encrypted node ID are added in the report generation process of the statistical en-route filtering technique to detect wormhole attacks even when a wormhole attack occurs along with a false report injection attack to improve security.

Energy-efficiency, Coverage-quality, and Faulttolerance Issues (ECFI) are the three primary and crucial quality assurance standards in a Wireless Sensor Network (WSN). These three standards ensure the network design of a self-configurable and sustainable WSN. First, energy-efficiency mechanisms ensure the prolonging of the overall network lifetime. Second, the coverage quality means how well a region of interest (RoI) is covered. Third, fault-tolerance refers to the ability of a network to continue operating without interruption when one or more components fail. With rapid innovations and developments in Wireless Sensor Network (WSN) applications, many researchers' most sustainable objective is to provide an adeptly operational sensor network. A broad range of applications of WSNs can be seen in the fields of academic, military, industry, medical, and daily needs worldwide. This analysis and survey article's main objective is to highlight different promising challenges on various fundamental characteristics, designing limitations, and their impacts on applications of WSNs. The authors also highlight the energy depletion and energy-provision issues with a concise survey of coverage and fault-related issues in WSNs. The authors finally summarized with discussion and analysis of the overall impacts of ECFI and other parameters on WSNs with simulator availability and future research direction.

Wireless Sensor Network (WSN) design to be efficient expects better energy optimization methods as nodes in WSN are operated only through batteries. In WSN, energy is a challenging one in the network during transmission of data. To overcome the energy issue in WSN, Taylor based Grey Wolf Optimization algorithm proposed, which is the integration of the Taylor series with Grey Wolf Optimization approach finding optimal hops to accomplish multi-hop routing. This paper shows the multiple objective-based approaches developed to achieve secure energyaware multi-hop routing. Moreover, secure routing is to conserve energy efficiently during routing. The proposed method achieves 23.8% of energy, 75% of Packet Delivery Ratio, 35.8% of delay, 53.2% of network lifetime, and 84.8% of scalability.