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An Efficient Data Analysis Framework for Online Security Processing
Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.
: MCDM-Based Interest Forwarding and Cooperative Data Caching for Named Data Networking
Named data networking (NDN), as a specific architecture design of information-centric networking (ICN), has quickly became a promising candidate for future Internet architecture, where communications are driven by data names instead of IP addresses. To realize the NDN communication paradigm in the future Internet, two important features, stateful forwarding and in-network caching, have been proposed to cope with drawbacks of host-based communication protocols. The stateful forwarding is designed to maintain the state of pending Interest packets to guide Data packets back to requesting consumers, while the in-network caching is used to reduce both network traffic and data access delay to improve the overall performance of data access. However, the conventional stateful forwarding approach is not adaptive and responsive to diverse network conditions because it fails to consider multiple network metrics to make Interest forwarding decision. In addition, the default in-network caching strategy relies on storing each received Data packet regardless of various caching constraints and criteria, which causes the routers in the vicinity of data producers to suffer from excessive caching overhead. In this paper, we propose the , a novel stateful forwarding and in-network caching strategy for NDN networks. The consists of multicriteria decision-making (MCDM) based interest forwarding and cooperative data caching. The basic idea of the MCDM-based interest forwarding is to employ Technique for Order Performance by Similarity to Idea Solution (TOPSIS) to dynamically evaluate outgoing interface alternatives based on multiple network metrics and objectively select an optimal outgoing interface to forward the Interest packet. In addition, the cooperative data caching consists of two schemes: CacheData, which caches the data, and CacheFace, which caches the outgoing interface. We conduct extensive simulation experiments for performance evaluation and comparison with prior schemes. The simulation results show that the can improve Interest satisfaction ratio and Interest satisfaction latency as well as reduce hop count and Content Store utilization ratio.
Towards a Scalable and Adaptive Learning Approach for Network Intrusion Detection
This paper introduces a new integrated learning approach towards developing a new network intrusion detection model that is scalable and adaptive nature of learning. The approach can improve the existing trends and difficulties in intrusion detection. An integrated approach of machine learning with knowledge-based system is proposed for intrusion detection. While machine learning algorithm is used to construct a classifier model, knowledge-based system makes the model scalable and adaptive. It is empirically tested with NSL-KDD dataset of 40,558 total instances, by using ten-fold cross validation. Experimental result shows that 99.91% performance is registered after connection. Interestingly, significant knowledge rich learning for intrusion detection differs as a fundamental feature of intrusion detection and prevention techniques. Therefore, security experts are recommended to integrate intrusion detection in their network and computer systems, not only for well-being of their computer systems but also for the sake of improving their working process.
Scalable THz Network-On-Chip Architecture for Multichip Systems
While THz wireless network-on-chip (WiNoC) introduces considerably high bandwidth, due to the high path loss, it cannot be used for communication between far apart nodes, especially in a multichip architecture. In this paper, we introduce a cellular and scalable architecture to reuse the frequencies of the chips. Moreover, we use a novel structure called parallel-plate waveguide (PPW) that is suitable for interchip communication. The low-loss property of this waveguide lets us increase the number of chips. Each chip has a wireless node as a gateway for communicating with other chips. To shorten the length of intra- and interchip THz links, the optimum configuration is determined by leveraging the multiobjective simulating annealing (SA) algorithm. Finally, we compare the performance of the proposed THz multichip NoC with a conventional millimeter-wave one. Our simulation results indicate that when the system scales up from four to sixteen chips, the throughput of our design is decreased about , while for millimeter-wave NoC, this reduction is about . Furthermore, the average latency growth of our system is only compared with about increase for the millimeter-wave NoC.
FSB-DReViSeR: Flow Splitting-Based Dynamic Replacement of Virtual Service Resources for Mobile Users in Virtual Heterogeneous Networks
Virtual networks are sets of virtual devices that are interconnected through a physical network to provide services to end users. These services are usually heterogeneous (VOIP, VoD, streaming, etc.), exploit various amounts of resources (bandwidth, computing power, servers, etc.), and have topologies different from those of the substrate network. These variations in requirements are traditionally known as the architectural flexibility of virtual networks. Each virtual service is provided through a server called a virtual service resource. When a virtual service resource can no longer provide a good quality of service to end users due to the traffic variation generated by their mobility, two approaches are commonly implemented: provisioning the virtual network with resources or replacing the virtual service resource by migrating the service to another node that offers the most suitable amount of resource to satisfy the quality of service (QoS). In this paper, we propose a flow splitting-based dynamic virtual service resource replacement approach that allows for virtual service replacement across multiple virtual paths. Our approach is based on a graph topology that differs from those in the literature, which are based on tree topologies. The simulations performed in this study show that our approach significantly reduces the virtual service resource replacement time compared to other approaches.
A Use of Fuzzy TOPSIS to Improve the Network Selection in Wireless Multiaccess Environments
Constantly faster, mobile terminals are developing, as well as wireless networks, to satisfy the growth of “Always Best Connected” demand. Users nowadays want to access the best available wireless network, either from 3GPP or IEEE group technologies, wherever they are, without losing their sessions. Consequently, mobile terminals must seamlessly transfer the communications to another access technology (vertical handover) if needed, as they often move into heterogeneous wireless environments. This work aims to optimize the network selection step in the vertical handover process. Multiattribute Decision-Making methods naturally fit this context. Nevertheless, they make wrong handover decisions sometimes, due to imprecise data collected from the metrics. This manuscript presents the use of a hybrid method, combining the fuzzy technique for order preference by similarity to the ideal situation and fuzzy analytic network process, in the network selection, to improve the quality of service and avoid, as much as possible, unnecessary handovers. The results demonstrate that this combination is the best, compared to the other methods of the same type in the network selection context.