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Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation...
With an enormous increase in number of mobile users, mobile threats are also growing rapidly. Mobile malwares can lead to several cybersecurity threats i.e. stealing sensitive information, installing backdoors, ransomware attacks and sending premium SMSs etc. Previous studies have shown that due to the sophistication of threats and tailored techniques to avoid detection, not every antivirus system...
Modern web services face an increasing number of new threats. Logs are collected from almost all web servers, and for this reason analyzing them is beneficial when trying to prevent intrusions. Intrusive behavior often differs from the normal web traffic. This paper proposes a framework to find abnormal behavior from these logs. We compare random projection, principal component analysis and diffusion...
Attacks against web servers and web-based applications remain a serious global network security threat. Attackers are able to compromise web services, collect confidential information from web data bases, interrupt or completely paralyze web servers. In this study, we consider the analysis of HTTP logs for the detection of network intrusions. First, a training set of HTTP requests which does not contain...
In this research, online detection of anomalous HTTP requests is carried out with Growing Hierarchical Self-Organizing Maps (GHSOMs). By applying an n-gram model to HTTP requests from network logs, feature matrices are formed. GHSOMs are then used to analyze these matrices and detect anomalous requests among new requests received by the webserver. The system proposed is self-adaptive and allows detection...
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