Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
In spite of the tangible advantages of cloud computing, it is still vulnerable to potential attacks and threats. In light of this, security\nhas turned into one of the main concerns in the adoption of cloud computing. Therefore, an anomaly detection method plays an\nimportant role in providing a high protection level for network security. One of the challenges in anomaly detection, which has\nnot been seriously considered in the literature, is applying the dynamic nature of cloud traffic in its prediction while maintaining\nan acceptable level of accuracy besides reducing the computational cost. On the other hand, to overcome the issue of additional\ntraining time, introducing a high-speed algorithm is essential. In this paper, a network traffic anomaly detection model grounded in\nCatastropheTheory is proposed.This theory is effective in depicting sudden change processes of the network due to the dynamic\nnature of the cloud. Exponential Moving Average (EMA) is applied for the state variable in sliding window to better show the\ndynamicity of cloud network traffic. Entropy is used as one of the control variables in catastrophe theory to analyze the distribution\nof traffic features. Our work is compared with Wei Xiong et al.â??s Catastrophe Theory and achieved a maximum improvement in\nthe percentage of Detection Rate in week 4 Wednesday (7.83%) and a 0.31% reduction in False Positive Rate in week 5 Monday.\nAdditional accuracy parameters are checked and the impact of sliding window size in sensitivity and specificity is considered....
Volunteer computing (VC) is a distributed computing paradigm, which provides\nunlimited computing resources in the form of donated idle resources for many large-scale scientific\ncomputing applications. Task scheduling is one of the most challenging problems in VC. Although,\ndynamic scheduling problem with deadline constraint has been extensively studied in prior studies\nin the heterogeneous system, such as cloud computing and clusters, these algorithms canâ??t be fully\napplied to VC. This is because volunteer nodes can get offline whenever they want without taking\nany responsibility, which is different from other distributed computing. For this situation, this paper\nproposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint,\ncalled deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the\nnearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic\ntask scheduling problem with deadline constraint. To make full use of resources and maximize the\nnumber of completed tasks before the deadline constraint, on the basis of the DPDS algorithm,\nimproved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we\nconducted experiments, and the results show that the proposed algorithms can effectively solve the\ndynamic task assignment problem with deadline constraint in VC....
Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data\nvolumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use\nindustrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud\ncomputing technology provides a good solution. It receives more and more attention and support due to its flexible configuration,\non-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work\nand concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms\nof security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In\norder to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate.At present,\nmore and more applications are using ABE to ensure data security.However, the privacy protection issues during the key generation\nphase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and\ncorresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great\ndamage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects userâ??s privacy\nduring key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the\nKGC cannot know userâ??s attributes and that the attribute auditing center (AAC) cannot obtain the userâ??s secret key.This is ideal for\nmany privacy-sensitive scenarios, such as industrial big data scenario....
Cloud computing (CC) is fast-growing and frequently adopted in information technology\n(IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the\nhot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load\nbalancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC)\nfor task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, usersâ??\ntasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more\nclusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish\nTime (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual\nmachines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions.\nLoad balancing is subsequently performed using a WLC algorithm, which distributes the load based\non server weight and capacity as well as client connectivity to server. A highly weighted or least\nconnected server is selected for task allocation, which in turn increases the response time. Finally,\nwe evaluate the proposed architecture using metrics such as response time, makespan, resource\nutilization, and service reliability....
Investigating the security pitfalls of cryptographic protocols is crucial to understand how to improve security. At ICCCSâ??17,Wu and\nXu proposed an efficient smart-card-based password authentication scheme for cloud computing environments to cope with the\nvulnerabilities in Jiang et al.â??s scheme. However, we reveal that Wu-Xuâ??s scheme actually is subject to various security flaws, such\nas offline password guessing attack and replay attack. Besides security, user friendly is also another great concern. In 2017, Roy et\nal. found that in most previous two-factor schemes a user has to manage different credentials for different services and further\nsuggested a user-friendly scheme which is claimed to be suitable for multiserver architecture and robust against various attacks. In\nthis work, we show that Roy et al.â??s scheme fails to achieve truly two-factor security and shows poor scalability. At FGCSâ??18, Amin\net al. pointed out that most of existing two-factor schemes are either insecure or inefficient for mobile devices due to the use of\npublic-key techniques and thus suggested an improved protocol by using only light-weight symmetric key techniques. Almost at\nthe same time, Wei et al. also observed this issue and proposed a new scheme based on symmetric key techniques with formal\nsecurity proofs in the random oracle model. Nevertheless, we point out that both Amin et al.â??s and Wei et al.â??s schemes cannot\nachieve the claimed security goals (including the most crucial goal of â??truly two-factor securityâ?). Our results invalidate any use of\nthe scrutinized schemes for cloud computing environments....
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