Software security vulnerabilities are one of the critical issues in the realm of computer security. Due to their potential high severity impacts, many different approaches have been proposed in the past decades to mitigate the damages of software vulnerabilities. Machine-learning and data-mining techniques are also among the many approaches to address this issue. In this article, we provide an extensive review of the many different works in the field of software vulnerability analysis and discovery that utilize machine-learning and data-mining techniques. We review different categories of works in this domain, discuss both advantages and shortcomings, and point out challenges and some uncharted territories in the field.
Abstract. Although the prevention of Distributed Denial of Service (DDoS) attacks is not possible, detection of such attacks plays main role in preventing their progress. In the flooding attacks, especially new sophisticated DDoS, the attacker floods the network traffic toward the target computer by sending pseudo-normal packets. Therefore, multi-purpose IDSs do not offer a good performance (and accuracy) in detecting such kinds of attacks.In this paper, a novel method for detection of DDoS attacks has been introduced based on a statistical pre-processor and an unsupervised artificial neural net. In addition, SPUNNID system has been designed based on the proposed method. The statistical pre-processing has been used to extract some statistical features of the traffic, showing the behavior of DDoS attacks. The unsupervised neural net is used to analyze and classify them as either a DDoS attack or normal. Moreover, the method has been more investigated using attacked network traffic, which has been provided from a real environment. The experimental results show that SPUNNID detects DDoS attacks accurately and efficiently.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.