Ning Hu¹, Shi Yin¹, Shen Su^{1, *}, Xudong Jia¹, Qiao Xiang², Hao Liu³

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1531-1557, 2020, DOI:10.32604/cmc.2020.010949

Abstract The domain name system (DNS) provides a mapping service between memorable names and numerical internet protocol addresses, and it is a critical infrastructure of the Internet. The authenticity of DNS resolution results is crucial for ensuring the accessibility of Internet services. Hundreds of supplementary specifications of protocols have been proposed to compensate for the security flaws of DNS. However, DNS security incidents still occur frequently. Although DNS is a distributed system, for a specified domain name, only authorized authoritative servers can resolve it. Other servers must obtain the resolution result through a recursive or iterative resolving procedure, which renders DNS… More >

Qingyuan Zhou^a, Jianjian Luo^b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 133-138, 2018, DOI:10.1080/10798587.2016.1267444

Abstract Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Most of the challenges of Big Data in Smart Cities are multi-dimensional and can be addressed from different multidisciplinary perspectives. Based on the above considerations, this paper combined the PSR method, the fuzzy logic model and the entropy weight method in an empirical study for feasible urban public… More >

Jie Xu^{1, *}, Qun Wang¹, Yifan Wang¹, Khan Asif²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1819-1844, 2020, DOI:10.32604/cmc.2020.010727

Abstract Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors,… More >

Jianwei Zhang¹, Ze Qin¹, Shunfeng Wang^{1, *}

Journal of Cyber Security, Vol.2, No.1, pp. 25-36, 2020, DOI:10.32604/jcs.2020.06429

Abstract Digital images have been applied to various areas such as evidence in courts. However, it always suffers from noise by criminals. This type of computer network security has become a hot issue that can’t be ignored. In this paper, we focus on noise removal so as to provide guarantees for computer network security. Firstly, we introduce a well-known denoising method called Expected Patch Log Likelihood (EPLL) with Gaussian Mixture Model as its prior. This method achieves exciting results in noise removal. However, there remain problems to be solved such as preserving the edge and meaningful details in image denoising, cause… More >

Liang Ding^1,*, Lunjie Li¹, Jianghong Han¹, Yuqi Fan^2,*, Donghui Hu¹

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1285-1304, 2019, DOI:10.32604/cmc.2019.06160

Abstract Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains. The detection of DGA domains is critical for blocking the C2 server, and for identifying the compromised hosts as well. However, the detection is difficult, because some DGA domain names look normal. Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information, which causes long response time and cannot be used for real-time detection. In addition, when a new family of… More >

Weihong Han¹, Zhihong Tian^1,*, Zizhong Huang², Lin Zhong³, Yan Jia²

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 167-180, 2019, DOI:10.32604/cmc.2019.05192

Abstract Network Security Situation Awareness System YHSAS acquires, understands and displays the security factors which cause changes of network situation, and predicts the future development trend of these security factors. YHSAS is developed for national backbone network, large network operators, large enterprises and other large-scale network. This paper describes its architecture and key technologies: Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis, Knowledge Representation and Management of Super Large-Scale Network Security, Multi-Level, Multi-Granularity and Multi-Dimensional Network Security Index Construction Method, Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology, and so on. The performance tests show that YHSAS… More >

Hongbin Zhang^{1, 2}, Yuzi Yi^{1, *}, Junshe Wang¹, Ning Cao^{3, *}, Qiang Duan⁴

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 381-399, 2018, DOI: 10.3970/cmc.2018.03787

Abstract Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of… More >

Jin Yang¹, Tao Li¹, Gang Liang^1,*, Wenbo He², Yue Zhao³

CMES-Computer Modeling in Engineering & Sciences, Vol.120, No.1, pp. 1-23, 2019, DOI:10.32604/cmes.2019.04727

Abstract Deep Learning presents a critical capability to be geared into environments being constantly changed and ongoing learning dynamic, which is especially relevant in Network Intrusion Detection. In this paper, as enlightened by the theory of Deep Learning Neural Networks, Hierarchy Distributed-Agents Model for Network Risk Evaluation, a newly developed model, is proposed. The architecture taken on by the distributed-agents model are given, as well as the approach of analyzing network intrusion detection using Deep Learning, the mechanism of sharing hyper-parameters to improve the efficiency of learning is presented, and the hierarchical evaluative framework for Network Risk Evaluation of the proposed… More >

Ling Tan^1,*, Chong Li², Jingming Xia², Jun Cao³

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 275-288, 2019, DOI:10.32604/cmc.2019.03735

Abstract Due to the widespread use of the Internet, customer information is vulnerable to computer systems attack, which brings urgent need for the intrusion detection technology. Recently, network intrusion detection has been one of the most important technologies in network security detection. The accuracy of network intrusion detection has reached higher accuracy so far. However, these methods have very low efficiency in network intrusion detection, even the most popular SOM neural network method. In this paper, an efficient and fast network intrusion detection method was proposed. Firstly, the fundamental of the two different methods are introduced respectively. Then, the self-organizing feature… More >