With the rapid development of in modern technologies has led to a novel generation of cyberspace equipment and secure analysis methods such as cyber‐physical system (CPS). The CPS system refers to an intelligent dependable system of embedded smart devices used to monitor and control the cyberspace conditions in a fog computing environment. The fog computing environment connects resources even human beings together to enhance the quality of life through performing of CPS applications using computation resources at the network edge. The optimization of task scheduling for the CSP applications as a challenging issue has been considered as the NP‐hard problem in fog computing environment which is not trivial. On the one hand, reasonable tasks scheduling can increase resource utilization, which can avoid less idle resource. This paper presents a task scheduling algorithm based on moth‐flame optimization algorithm to assign an optimal set of tasks to fog nodes to meet the satisfaction of quality of service requirements of CPS applications in such a way that the total execution time of tasks is minimized. The minimization of task execution and transfer time in the proposed algorithm are considered as objective functions. The experimental testing of the proposed solution is carried out in iFogSim toolkit. As a result of the simulation based on proposed algorithm found, the optimal solution for the scheduling of tasks and equal distribution of tasks to fog nodes has been provided, and less total execution time consumption has been achieved compared with other algorithms.

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Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.

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Cloud-edge computing is a hybrid model of computing where resources and services provided via the Internet of Things (IoT) between large-scale and long-term data informs of the cloud layer and smallscale and short-term data as edge layer. The main challenge of the cloud service providers is to select the optimal candidate services that are doing the same work but offer different Quality of Service (QoS) values in IoT applications. Service composition in cloud-edge computing is an NP-hard problem; therefore, many meta-heuristic methods introduced to solve this issue. Also, the correctness of meta-heuristic and machine learning algorithms for evaluating service composition problem should be proven using formal methods to guarantee functional and non-functional specifications. In this paper, a hybrid Artificial Neural Networkbased Particle Swarm Optimization (ANN-PSO) Algorithm presented to enhance the QoS factors in cloudedge computing. To illustrate the correctness and improve the reachability rate of candidate composited services and QoS factors for the proposed hybrid algorithm, we present a formal verification method based on a labeled transition system to check some critical Linear Temporal Logics (LTL) formulas. The experimental results illustrated the high performance of the proposed model in terms of minimum verification time, memory consumption, and guaranteeing critical specifications rules as the Linear Temporal Logic (LTL) formulas. Also, we observed that the proposed model has optimal response time, availability, and price with maximum fitness function value than other service composition algorithms.INDEX TERMS Cloud-edge computing, Internet of Things, service composition, formal verification, quality of service, artificial neural network, and particle swarm optimization.

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By raising evolutionary network connections, a software‐defined network (SDN) offers a well‐managed and flexible novel network topology. The SDN efforts are provided to abstract the original network configuration for business applications and web services. Additionally, the SDN modifies the network management policies such as topology policies, deployment policies, the applicability, and infrastructure maintenance. One of the important features of the SDN is standardizing the network interfaces with the precise functional semantics in application and control flow layers. In another hand, specification and evaluation of the existing network interfaces is a main and significant challenge to prove the correctness of the configurable scenarios in the SDN approach. Recently, formal verification presents a high potential platform to evaluate the main‐layer scenarios of the SDN as the novel perspective in this area. Up to now, there is no survey and state‐of‐the‐art review on the formal verification methods in the SDN. This paper provides a systematic literature review (SLR) for the formal verification methods in the SDN in form of the SDN plans to recognize the state of the art of the open challenges. The presented SLR is classified into three main fields: application plan, data plan, and control plan approaches. The verification mechanisms are compared with each other according to the important factors such as structural properties, quality‐of‐service metrics, applied algorithms, and measurement tools.