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Feature selection is one of the main issues in machine learning algorithms. In this paper, a new binary hyper-heuristics feature ranks algorithm is designed to solve the feature selection problem in high-dimensional classification data called the BFRA algorithm. The initial strong population generation is done by ranking the features based on the initial Laplacian Score (ILR) method. A new operator called AHWF removes the zero-importance or redundant features from the population-based solutions. Another new operator, AHBF, selects the key features in population-based solutions. These two operators are designed to increase the exploitation of the BFRA algorithm. To ensure exploration, we introduced a new operator called BOM, a binary counter-mutation that increases the exploration and escape from the BFRA algorithm’s local trap. Finally, the BFRA algorithm was evaluated on 26 high-dimensional data with different statistical criteria. The BFRA algorithm has been tested with various meta-heuristic algorithms. The experiments’ different dimensions show that the BFRA algorithm works like a robust meta-heuristic algorithm in low dimensions. Nevertheless, by increasing the dataset dimensions, the BFRA performs better than other algorithms in terms of the best fitness function value, accuracy of the classifiers, and the number of selected features compared to different algorithms. However, a case study of sentiment analysis of movie viewers using BFRA proves that BFRA algorithms demonstrate affordable performance.

Today, intelligent transportation systems (ITS) have received a lot of attention due to their significant impact on increasing the safety, efficiency, and convenience of transportation. One of the main applications of ITS is vehicular ad hoc networks (VANETs). In particular, a more flexible, reliable, real-time, and scalable routing scheme across the large urban areas is one of the most critical issues for VANETs. Past VANET routing methods have various technical issues with VANET evolutions. On the other hand, clustering improves the reliability and scalability of routing schemes in VANETs. In this paper, a cluster-based, Traffic-aware and Low-Latency Routing Schema (TaLAR) is proposed for VANETs. In the proposed scheme, the Harris hawks optimization (HHO) algorithm is used to select cluster head (CH) nodes by considering appropriate parameters such as intracluster distance, link reliability, and relative speed of vehicles. The path between source and destination CH nodes is identified by using the HHO algorithm; it chooses the appropriate route based on link reliability and intercluster distance. Also, in the interconnection area, a traffic-aware and reliable route is identified by using a digital map of the streets and the Dijkstra algorithm. The performance evaluation of the proposed scheme is analyzed in terms of packet delivery rate (PDR), average end-to-end delay, and throughput. The output of the proposed scheme is compared with the Clustering Routing Based on PSO (Particle Swarm Optimization) (CRBP) and Grey Wolf Optimization Based Clustering in Vehicular Ad Hoc Network (GWOCENT) methods. Simulation results show that the proposed scheme improves PDR (22 and 19%), throughput (25 and 21%), and average end-to-end delay (23 and 18%).

The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing (FSR) protocol. The proposed QFS-RPL is as lightweight and agile as the standardized RPL and partially outperforms the mRPL protocol on mobile networks. This method supports multi-path routing, and at any given time in the network lifetime, all possible paths for sending data from any node to the sink are available. Therefore, QFS-RPL provides high resilience against errors, failures, and sudden network changes. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The performance evaluation criteria considered for simulation are load balancing, energy consumption, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, and control packet overhead.

Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. We can use hybrid meta-heuristic algorithms to arrive at an efficient algorithm. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, two algorithms, Salp Swarm Optimization(SSO) and the African Vulture Optimization Algorithm (AVOA) have been hybridized. And to improve the optimization process of the Modified Choice Function and Learning Automata mechanisms. In addition, two other improving mechanisms, named Opposition-Based Learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA-SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA-SSA algorithm. Finally, an improved version of the Extreme Learning Machine(ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA-SSA algorithm in `solving optimization problems and has been able to achieve high-quality solutions.

As the Internet of Things networks expand globally, it is becoming increasingly important to protect against threats. one of the main reasons for the high number of false positives and low detection rates is the presence of redundant and irrelevant features. To address this problem, we propose a binary chimpanzee optimization algorithm for the feature selection process. This paper presents accurate network-based intrusion detection network, named parallel convolutional neural network long and short-term memory network branch, which has two branches. The input vector of the network is permuted in a 3-dimention space. This allows the model to extract highly discriminative features using a small number of layers. On the second branch, we used long and short-term memory network in parallel. The efficacy of the proposed deep model has been evaluated using three benchmark internet of things intrusion detection datasets, namely ToN-IoT, UNSW-NB15, and IoTID20 datasets. The experimental results demonstrated that the proposed binary chimpanzee optimization approach reduces about 60% of features, and the effectiveness of the proposed model was demonstrated by experimental results showing a high detection rate, high accuracy, and a relatively low false positive rate, which are measured as 99.54%, 99.56%, and 0.024 in the ToN-IoT and 99.79%, 99.78%, and 0.0032 in UNSW-NB15 and 100%, 100%, and zero in IoTID20 datasets, respectively.

With the increase in the number of processing cores on a chip, a highly efficient and scalable structure such as a network on a chip was proposed. Network-on-chip is a technology presented to solve the limitations of system-on-chip that includes a set of multiprocessors. In network-on-chip systems, delay in communication and reaching the ideal speed is one of the concerns of academic and industrial researchers today. Although network-on-chip offers a favorable solution to reduce the problem of the long delay of wires compared to traditional structures, communication delay is still a challenge. In this article, the various types of network router input ports architecture on the chip are reviewed and the operational comparison and evaluation of the technologies proposed in this field are mentioned.

Network on chip (NoC) is one of the communicative structures for multiple cores that has scalability. In designing the NoC micro-router architecture, the arbitration unit is very important due to its significant impact on performance, chip occupation level and NoC power consumption. In this paper, a router arbitration architecture is proposed with a combination of variable priority arbitration and Round Robin. In this architecture, arbitration examines the requests of other channels based on the Round Robin index after requesting the flit to exit the virtual channel in addition to checking the availability of the relevant virtual channel.The simulation results show that the architecture of the RR-SFVP arbitration unit compared to the standard RR method, is 13.7% smaller in area and has 5.7% less power consumption and 53.7% less critical path delay.

The Internet of Things (IoT) has rapidly grown recently, and mobile devices (MDs) have encountered widespread usage. All of these cause an increase in the demand for more powerful computing resources. Meanwhile, a new concept called mobile edge computing (MEC) was introduced as a promising technology to access powerful computing resources closer to the user side for a quick and effective response, especially for time-intensive applications. Task offloading has emerged as a solution to allocate resources among computing resources of smart devices or computational resources available in MEC. This study presents a new binary quantum approach based on an arithmetic optimization algorithm (BQAOA) for computational tasks offloading decisions on MDs with low complexity and guaranteed convergence. However, since task offloading is an NP-hard problem, there is a need to use methods that provide the optimal possible solution for various quality criteria, including response time and energy consumption. Indeed, this is where the advantages of arithmetic optimization algorithms (AOA) and quantum computing have been used to improve the performance of MDs. This paper introduces a 2-tier architecture from the user to the cloud computing server-side. Also, a Markov model is proposed to compute the average network bandwidth in the offloading problem. The proposed BQAOA is compared with the best state-of-the-art algorithms in heuristic and meta-heuristic fields in different scenarios. The simulation results showed 12.5%, 12%, and 26% improvement in energy consumption, makespan, and Energy SLA Violations (ESV) optimization parameters, respectively.

Energy consumption is one of the main concerns in wireless sensor networks (WSNs). In this context, congestion is one of the problems which by dropping the data packets, increases the energy consumption of WSN, and reduces its lifetime. In this paper, we deal with these problems and present a distributed fuzzy clustering scheme that uses two Fuzzy Logic Controllers (FLCs) to organize WSN into some clusters. Besides, in this scheme, we consider multiple mobile sink nodes and provide another FLC for fuzzy sink selection used by cluster heads (CHs). In this scheme, CHs cooperate in multi-hop routing of data packets to minimize the energy consumption of WSN. However, in the data routing step, congestion may happen in the data forwarding nodes. In this scheme, we deal with the congestion problem by proposing a distance-based version of the Random Early Detection (RED) congestion control method to drop the data packets more intelligently. Besides, to increase the effectiveness of the proposed FLCs, we tune them using the Moth-Flame Optimization algorithm and minimize their rules. Simulation results indicate the effectiveness of the proposed clustering and distance-based RED congestion control method in improving the WSN’s lifespan, reducing the number of retransmissions, and mitigating the percentage of packet loss.

Services Oriented Architecture provides Web Services (WSs) as reusable software components that can be applied to create more complicate composite services for users according to the specified QoS limitations. However, considering many WSs that may be appropriate for each task of a user-submitted workflow, finding the optimal WSs for a composite WS to maximize the overall QoS is an NP-hard problem. As a result, numerous composition schemes have been suggested in the literature to untangle this problem by using various metaheuristic algorithms. This paper presents a comprehensive survey and taxonomy of such QoS-oriented metaheuristic WS composition schemes provided in the literature. It investigates how metaheuristic algorithms are adapted for the WS composition problem and highlight their main features, advantages, and limitations. Also, in each category of the studied composition schemes, a comparison of their applied QoS factors, evaluated metrics, exploited simulators, and properties of the applied metaheuristic algorithms are explained. Finally, the concluding remarks and future research directions are summarized to help researchers in working in this area.

Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high dimensional datasets. One of the most common feature selection methods is the wrapper method that works on the feature set to reduce the number of features while improving the accuracy of the classification. In this paper, two different wrapper feature selection approaches are proposed based on Farmland Fertility Algorithm (FFA). Two binary versions of the FFA algorithm are proposed, denoted as BFFAS and BFFAG. The first version is based on the sigmoid function. In the second version, new operators called Binary Global Memory Update (BGMU) and Binary Local Memory Update (BLMU) and a dynamic mutation (DM) operator are used for binarization. Furthermore, the new approach (BFFAG) reduces the three parameters of the base algorithm (FFA) that are dynamically adjusted to maintain exploration and efficiency. Two proposed approaches have been compared with the basic meta-heuristic algorithms used in feature selection on 18 standard datasets. The results show better performance of the proposed approaches compared with the competing methods in terms of objective function value, the average number of selected features, and the classification accuracy. Also, the experiments on the emotion analysis dataset demonstrate the satisfactory results.

Cloud computing is a computing paradigm, where a large pool of systems is connected in private or public networks to provide dynamically scalable infrastructure for application, data, and file storage. With the advent of this technology, the cost of power computation, application hosting, content storage, resource wastage, and delivery is reduced significantly. Cloud computing provides the possibility of merely concentrating on business goals instead of expanding hardware resources for users. Challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines over physical ones in cloud data centers can lead to the management of resources and prevention of the resources waste. Hereby, a new approach is proposed based on the combination of the hybrid discrete multi-object whale optimization algorithm, multi-verse optimizer with chaotic functions for optimal placement in the cloud data center. The first object of the proposed algorithm is to decrease power consumption, which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut the resource wastage and managing resources using the optimal placement of virtual machines over physical machines in cloud data centers. With this method, the increasing rate of virtual migration to physical machines is prevented. Finally, the results obtained from the proposed algorithm were compared to some algorithms such as first fit, VMPACS, MBFD.

Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author’s knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.

In the data security context, anomaly detection is a branch of intrusion detection that can detect emerging intrusions and security attacks. A number of anomaly detection systems (ADSs) have been proposed in the literature that using various algorithms and techniques try to detect the intrusions and anomalies. This paper focuses on the ADS schemes which have applied fuzzy logic in combination with other machine learning and data mining techniques to deal with the inherent uncertainty in the intrusion detection process. For this purpose, it first presents the key knowledge about intrusion detection systems and then classifies the fuzzy ADS approaches regarding their utilized fuzzy algorithm. Afterward, it summarizes their major contributions and illuminates their advantages and limitations. Finally, concluding issues and directions for future researches in the fuzzy ADS context are highlighted.

Security attacks are a nightmare to many computing environments such as fog computing, and these attacks should be addressed. Fog computing environments are vulnerable to various kinds of DDoS attacks, which can keep fog resources busy. Typically in such attacks, fog environments often have less available resources, which can negatively impact the scheduling of Internet of Things (IoT) submitted workflows. However, most of the existing scheduling schemes do not consider DDoS attacks’ effect in the scheduling process, increasing the deadline missed workflows and offloaded tasks on the cloud. For dealing with these issues, a hybrid optimization algorithm is proposed, comprising both Particle Swarm Optimization (PSO) and Salp Swarm algorithm (SSA), to solve the workflow scheduling problem in multiple fog computing environments. Two discrete-time Markov chain models are proposed for each fog computing environment to address DDoS attacks’ effects on them. Our first Markov model computes the average available network bandwidth for each fog. The second Markov model finds the average number of available virtual machines (VMs) for each fog; the models address different levels of DDoS attacks. Extensive simulations show that by predicting the effects of DDoS attacks on fog environments, the proposed approach can effectively mitigate the number of offloaded tasks on cloud data centers and can reduce the number of the deadline missed workflows.

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

Nowadays cloud computing is progressing very fast and has resulted in advances in other technologies too. Cloud computing provides quite a convenient platform for millions of users to use computing resources through the internet. Cloud computing provides the possibility of only concentrating on business goals instead of expanding hardware resources for users. Using virtualization technology in computing resources results in the efficient use of resources. A challenging work in virtualization technology is the placement of virtual machines under optimal conditions on physical machines in cloud data centers. Optimal placement of virtual machines on physical machines in cloud data centers can lead to the management of resources and prevention of the resources waste. In this paper, a new method is proffered based on the combination of hybrid discrete multi-object sine cosine algorithm and multi-verse optimizer for optimal placement. The first goal of the proposed approach is to decrease the power consumption which is consumed in cloud data centers by reducing active physical machines. The second goal is to cut in resource wastage and managing resources using the optimal placement of virtual machines on physical machines in cloud data centers. With this approach, the increasing rate of virtual migration to physical machines is prevented. Finally, the results gained from our proposed algorithm are compared to some algorithms like the first fit (FF), virtual machine placement ant colony system (VMPACS), modified best fit decreasing (MBFD).

Efficient task and workflow scheduling are very crucial for increasing performance, resource utilization, customer satisfaction, and return of investment for cloud service providers. Based on the number of clouds that the scheduling schemes can support, they can be classified as single-cloud and inter-cloud scheduling schemes. This paper presents a comprehensive survey and an overview of the inter-cloud scheduling schemes aimed to allocate user-submitted tasks and workflows to the appropriate virtual machines on multiple clouds regarding various objectives and factors. It classifies the scheduling schemes designed for a variety of inter-cloud environments and describes their architecture, key features, and advantages. Also, the inter-cloud scheduling approaches are compared and their various features are highlighted. Finally, the concluding remarks and open research challenges in the multi-cloud scheduling context are illuminated.

Efficient VM management is very crucial for energy saving, increasing profit, and preventing SLA violations. VM placement schemes can be classified into reactive and proactive/predictive schemes which try to improve the VM placement results, by forecasting future workloads or resource demands using various prediction techniques. This paper puts forward an extensive survey of the proactive VM placement approaches and categorizes them according to their applied forecasting methods. It describes how each scheme has applied the prediction algorithms to conduct more effective and low overhead VM placement. Moreover, in each category, factors such as evaluation parameters, simulation software, workload data, power management method, and prediction factors are compared to illuminate more details about the investigated VM placement approaches. At last, the concluding issues and open future studies trends and area are highlighted.

Mobile edge computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of mobile devices (MDs). However, MECs contain limited resources, and they should be appropriately managed to prevent resource wastage. Workflow scheduling is a process that tries to map tasks to the most proper set of resources based on some objectives. This paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight method to improve its convergence speed and prevent local optima problems. We also employed a task prioritization method to find the task execution order in the scientific workflows. Then, we use DBOA for Dynamic Voltage and Frequency Scaling or DVFS-based data-intensive workflow scheduling and data placement in MEC environments. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that our method can outperform other algorithms in terms of energy consumption, data access overheads, and so on.

Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR, which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three‐tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.

Providing a high-performance Intrusion Detection System (IDS) can be very effective in controlling malicious behaviors and cyber-attacks. Regarding the ever-growing negative impacts of the security attacks on computer systems and networks, various Artificial Intelligence (AI)-based techniques have been used to introduce versatile IDS approaches. Deep learning is a branch of AI techniques, mainly based on multi-layer artificial neural networks. Recently, deep learning techniques have gained momentum in the intrusion detection domain and several IDS approaches are provided in the literature using various deep neural networks to deal with privacy concerns and security threats. For this purpose, this article focuses on the deep IDS approaches and investigates how deep learning networks are employed by different approaches in different steps of the intrusion detection process to achieve better results. It classifies the studied IDS schemes regarding the deep learning networks utilized in them and describes their main contributions and capabilities. Besides, in each category, their main features such as evaluated metrics, datasets, simulators, and environments are compared. Also, a comparison of the deep IDS approaches main properties are provided to illuminate the main techniques applied in them as well as the area less focused in the literature. Finally, the concluding remarks in the deep IDS context are provided and possible directions at the subsequent studies are listed.

The miniaturization of wireless sensors and medical devices have empowered the extension of Wireless Body Area Networks (WBANs) and E-Health care systems. However, in WBANs, wireless communications are exposed to a variety of passive and active security attacks. As a result, protection of privacy and integrity of the collected data from WBANs is of high importance. To mitigate these problems, numerous key management and security solutions have been designed for the WBANs in the literature, that this paper provides a comprehensive analysis of them. It mainly classifies the key management methods into the biometric and non-biometric categories and illustrates their main capabilities in detail. Furthermore, it provides a complete comparison of the security schemes and highlights their features and limitations.

Mobile ad hoc networks (MANETs) are vulnerable to various security attacks conducted by the malicious nodes and attackers. To defend against the attackers, their public key certificates should be revoked by certificate authority. Thus, each MANET node before conducting any secure communications, should check the status of communicating parties certificates. Inconsistency of the certificate status information is one of the critical problems which reduce the correctness and effectiveness of the whole certificate validation process. In this paper, we conduct a Markov-chain based analysis and evaluation of the OCSP-based certificate validations in the hybrid MANETs. In particular, we focus on the ADOPT and PS-ADOPT protocols and present four absorbing Markov-chain models, three for online states whose OCSP responder is available to the MANET and the other to support offline cases where MANET may be disconnected from the OCSP responder occasionally. Finally, the results of absorbing Markov models are verified by the extensive simulations of the ADOPT and PS-ADOPT protocols in the OMNeT++ simulator.

In the original publication of the article, the corresponding author name and the e-mail ID was incorrect. The correct name and e-mail ID is given in this correction.

WBANs are of the promising technologies, applied in the e-healthcare systems, for extracting and transferring critical medical data from patient body. In WBANs, the privacy and integrity of the patient's private medical data are very important, as a result, the WBANs communications and even communication with other E-Healthcare components should be authenticated and secured. This paper provides a complete survey and analysis of the various authentication schemes proposed in the literature to improve the WBANs security. Besides, it classifies the proposed authentication schemes based on the applied techniques for authentication and illustrates each scheme in detail. Furthermore, it highlights the advantages and limitations of the authentication schemes and presents a comprehensive comparison of their capabilities and features. Finally, the paper concludes with open issues and future research directions. Copyright © 2016 John Wiley & Sons, Ltd.

Denial-of-service DoS attacks are one of the major security challenges in the emerging cloud computing models. Currently, numerous types of DoS attacks are conducted against the various cloud services and resources, which target their availability, service level agreements, and performance. This paper presents an in-depth study of the various types of the DoS attacks proposed for the cloud computing environment and classifies them based on the cloud components or services, which they target. Besides, it provides a comprehensive analysis of the vulnerabilities utilized in these DoS attacks and investigates about the state-of-the-art solutions presented in the literature to prevent, detect, or deal with each kind of DoS attacks in the cloud. Finally, it presents open research issues. Copyright © 2016 John Wiley & Sons, Ltd.

Workflow scheduling is one of the prominent issues in cloud computing which is aimed at complete execution of workflows by considering their QoS requirements such as deadline and budget constraints. Numerous state of the art workflow scheduling schemes have been proposed in the literature for scheduling simple and scientific workflows in the cloud computing and this paper presents a comprehensive survey and analysis of these schemes. It illuminates the objectives of scheduling schemes in the cloud computing and provides a classification of the proposed schemes based on the type of scheduling algorithm applied in each scheme. Beside, each scheme is illustrated and a complete comparison of them is presented to highlight their objectives, properties and limitations. Finally, the concluding remarks and future research directions are provided.

In cloud computing, Virtual Machine (VM) placement is a critical operation which is conducted as part of the VM migration and aimed to find the best Physical Machine (PM) to host the VMs. It has a direct effect on the performance, resource utilization and power consumption of the data centers and can reduce the maintenance cost of the data centers for cloud providers. Numerous VM placement schemes are designed and proposed for VM placement in the cloud computing environment aimed to improve various factors affecting the data centers, the VMs and their executions. This article provides a complete survey and analyses of the existing state of the art VM placement schemes proposed in the literature for the cloud computing and data centers. Furthermore, it classifies the VM placement schemes based on the type of the placement algorithm and assesses their capabilities and objectives. Moreover, the properties, advantages and limitations of the VM placement schemes are compared. Finally, the concluding remarks and future research directions are provided.

WSNs consist of many low-cost autonomous sensors that are equipped with a small battery, data processing capabilities, and short-range wireless communication interfaces. They are often deployed for monitoring and surveillance of certain phenomena of interest. In this paper, we propose a fuzzy Cmeans-based clustering scheme which improves the network lifetime by creating symmetric clusters. It improves the random process of selecting the clusterhead nodes and by creating symmetric clusters reduces the total distances of intra-cluster communications. Thus, because, sensed data are transmitted to the closer clusterheads, our proposed clustering solution can prolong the network lifetime.

Organizations that benefit from information technologies are vulnerable to various attacks and malicious behaviors. Intrusion Detection Systems (IDS) are one of the main lines of defense which in conjunction with firewalls and other security components are applied to deal with intrusions and unauthorized misbehaviors. Misuse detection is one of the main branches of the intrusion detection which intends to prevent known security attacks regarding their previously known signatures. This paper presents a comprehensive investigation of the fuzzy misuse detection schemes designed using various machine learning and data mining techniques to deal with different kinds of intrusions. For this purpose, it first presents the key points and knowledge about intrusion detection and then classifies the fuzzy misuse detection approaches regarding their applied fuzzy techniques and algorithms. Then, it illustrates the major contributions of the fuzzy IDS schemes and illuminates their merits and limitations. Besides, in each section, the comparison of their applied datasets, performance evaluation factors, feature extraction methods as well as the type of fuzzy logic controller (FLC) and membership functions are provided. Finally, the concluding issues and the directions for future researches are highlighted.

Each public key infrastructure needs an efficient certificate status validation method to exclude the revoked certificates from network. In this paper, we present a novel certificate validation scheme called E-ADOPT or Enhanced-ADOPT which utilizes a new kind of certificate status information. In this solution, we modify the OCSP response messages to carry information about the accusations issued against the certificate and this additional security information helps the client nodes to tune the OCSP results refresh rate more intelligently. As a result, client node can mitigate the certificate status information inconsistency problem with lower overheads and conduct more effective certificate status validations in MANET. Simulation results demonstrate that by appending accusation-related information to the OCSP responses, our solution achieves better results.

One of the most important tools in security field is Intrusion Detection System. The aim of the IDS is to monitor suspicious network traffic and generate alerts. These systems are known to generate numerousfalse positive alerts. Analyzing the alerts manually by security expert need more time and could be error prone.Another problem with IDS is Identifying attack types and generating correct alerts related to attacks.we introducenew alert management systems to overcome mentioned problems. Alert management systems help security experts to manage alerts and produce a high level view of alerts. In this paper a new alert clustering algorithm for IDS Alert Management System proposed that uses the K-mean Based Genetic (KBG). The proposed algorithm reduces alerts and detects false positive alerts. By the experimental results on DARPA KDD cup 98 the system is able to cluster and classify alerts and causes reducing false positive alerts considerably.

Freshness of certificate status information is very important in validating public key certificates. However, existing certificate validation schemes suffer from high inconsistency, which makes network vulnerable to various security attacks. In this paper, we propose a new publish/subscribe-based certificate validation scheme that provides fresh certificate status information in the mobile ad hoc networks. This scheme increases the security of public key infrastructure-based security systems and improves the scalability of certificate validation systems. Also, according to the simulation results, our solution effectively decreases the processing overheads on responder nodes and reduces the messaging overhead of certificate status validations in mobile ad hoc networks. Copyright © 2014 John Wiley & Sons, Ltd.

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a data set of patients clinical variables data with Parkinsons Disease (PD) to study the disease progression. The data set includes 22 properties of 42 people that all of our algorithms are applied to this data set. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy., 13 Pages, 4 Figures