Abstract Botnets are a major security threat to today’s Internet. Therefore, the detection of botnets has become a central task for network administrators. In this paper, we study the detection of botnets by monitoring and analyzing the Command and Control (C2) channels communication traffic. We note that this detection approach is effective as it detects a botnet before it engages in any... Botnets are a major security threat to today’s Internet. Therefore, the detection of botnets has become a central task for network administrators. In this paper, we study the detection of botnets by monitoring and analyzing the Command and Control (C2) channels communication traffic. We note that this detection approach is effective as it detects a botnet before it engages in any harmful activities. We analyze real network traffic captured at King Saud University network by exploiting the periodic behavior in C2 traffic. We use periodograms to study the periodic behavior, and apply Walker’s large sample test to detect whether the traffic has a significant periodic component or not, and, if it does, then it is bot traffic. We apply this test on two different days of KSU traffic. We show that the traffic in those days exhibit periodic behavior and report the source of that traffic as bot. Read More Read Less

Abstract Semantic Web is known as next generation of web it is known as a new collaborative movement toward Web3.0 that led by the World Wide Web Consortium (W3C). It aims at converting the current web of unstructured documents into a “web of data”. The proposed searching strategy for SEO in Semantic Web is a graph structured search (GSS). Search Engine Optimization... Semantic Web is known as next generation of web it is known as a new collaborative movement toward Web3.0 that led by the World Wide Web Consortium (W3C). It aims at converting the current web of unstructured documents into a “web of data”. The proposed searching strategy for SEO in Semantic Web is a graph structured search (GSS). Search Engine Optimization (SEO) is defined as a collection of techniques and practices that allow a site to get more traffic from search engines and it is still one of the biggest challenge in search engines of Semantic Webs. In this paper, I proposed a new type of web page search which is based on the competitive intelligence. It use link-based ranking evolutionary scheme to accommodate users’ references. I implemented the prototype system and demonstrate the feasibility of the proposed web page search scheme. Read More Read Less

Abstract Over the years a critical increase in the mass of the web has been observed. Among that a large part comprises of online subject-specific databases, hidden behind query interface forms known as deep web. Existing search engines are unable to completely index this highly relevant information due to its large volume. To access deep web content, the research community has proposed... Over the years a critical increase in the mass of the web has been observed. Among that a large part comprises of online subject-specific databases, hidden behind query interface forms known as deep web. Existing search engines are unable to completely index this highly relevant information due to its large volume. To access deep web content, the research community has proposed to organize it using machine learning techniques. Clustering is one of the key solutions to organize the deep web databases. Existing clustering methods do not encounter semantic relevance among deep web forms. In this paper, we propose a novel method DWSemClust to cluster deep web databases based on the semantic relevance found among deep web forms by employing a generative probabilistic model Latent Dirichlet Allocation (LDA) for modeling content representative of deep web databases. A document comprises of multiple topics, the task of LDA is to cluster words present in the document into “topics”. The purpose of the parameter estimation process in the underlying model is to discover the document’s topic and tell about its proportionate distribution in documents. Deep web has a sparse topic distribution. Due to this reason we have proposed to use LDA that is supposed to be a good clustering solution for the sparse distribution of topics. Further we employ a rich set of metadata as our content representative that comprises of form contents (single attribute/ multiple attributes) and page contents. Experimental results show that our proposed method clearly outperforms the existing non-semantics based clustering methods. Read More Read Less