Survey on Security Enhancement at the Design Phase

Pattern classification is a branch of machine learning that focuses on recognition of patterns and regularities in data. In adversarial applications like biometric authentication, spam filtering, network intrusion detection the pattern classification systems are used [6]. In this paper, we have to evaluate the security pattern by classifications based on the files uploaded by the users. We have also proposed the method of spam filtering to prevent the attack of the files from other users. We evaluate our approach for security task of uploading word files and pdf files. DOI: 10.17762/ijritcc2321-8169.150314

Web Spam DetectionUsing Fuzzy Clustering

Internet is the most widespread medium to express our views and ideas and a lucrative platform for delivering the products. F or this in tention, search engine plays a key role. The information or data about the web pages are stored in an index database of the search engine for use in later queries. Web spam refers to a host of techniques to challenge the ranking algorithms of web search en gines and cause them to rank their web pages higher or for some other beneficial purpose. Usually, the web spam is irritating the web surfers and makes disruption. It ruins the quality of the web search engine. So, in this paper, we presented an efficient clustering method to detect the spam web pages effectively and accurately. Also, we employed various validation measures to validate our research work by using the clustering methods. The comparison s between the obtained charts and the val idation results clearly explain that the research work we presented produces the better result

Using decoys to block SPIT in the IMS

Includes bibliographical references (leaves 106-111)In recent years, studies have shown that 80-85% of e-mails sent were spam. Another form of spam that has just surfaced is VoIP (Voice over Internet Telephony) spam. Currently, VoIP has seen an increasing numbers of users due to the cheap rates. With the introduction of the IMS (IP Multimedia Subsystem), the number of VoIP users are expected to increase dramatically. This calls for a cause of concern, as the tools and methods that have been used for blocking email spam may not be suitable for real-time voice calls. In addition, VoIP phones will have URI type addresses, so the same methods that were used to generate automated e-mail spam messages can be employed for unsolicited voice calls. Spammers will always be present to take advantage of and adapt to trends in communication technology. Therefore, it is important that IMS have structures in place to alleviate the problems of spam. Recent solutions proposed to block SPIT (Spam over Internet Telephony) have the following shortcomings: restricting the users to trusted senders, causing delays in voice call set-up, reducing the efficiency of the system by increasing burden on proxies which have to do some form of bayesian or statistical filtering, and requiring dramatic changes in the protocols being used. The proposed decoying system for the IMS fits well with the existing protocol structure, and customers are oblivious of its operation

Processing spam: Conducting processed listening and rhythmedia to (re)produce people and territories

This thesis provides a transdisciplinary investigation of ‘deviant’ media categories, specifically spam and noise, and the way they are constructed and used to (re)produce territories and people. Spam, I argue, is a media phenomenon that has always existed, and received different names in different times. The changing definitions of spam, the reasons and actors behind these changes are thus the focus of this research. It brings to the forefront a longer history of the politics of knowledge production with and in media, and its consequences. This thesis makes a contribution to the media and communication field by looking at neglected media phenomena through fields such as sound studies, software studies, law and history to have richer understanding that disciplinary boundaries fail to achieve. The thesis looks at three different case studies: the conceptualisation of noise in the early 20th century through Bell Telephone Company, web metric standardisation in the European Union 2000s legislation, and unwanted behaviours on Facebook. What these cases show is that media practitioners have been constructing ‘deviant’ categories in different media and periods by using seven sonic epistemological strategies: training of the (digital) body, restructuring of territories, new experts, standardising measurements (tools and units), filtering, de-politicising and licensing. Informed by my empirical work, I developed two concepts - processed listening and rhythmedia - offering a new theoretical framework to analyse how media practitioners construct power relations by knowing people in mediated territories and then spatially and temporally (re)ordering them. Shifting the attention from theories of vision allows media researchers to have a better understanding of practitioners who work in multi-layered digital/datafied spaces, tuning in and out to continuously measure and record people’s behaviours. Such knowledge is being fed back in a recursive feedback-loop conducted by a particular rhythmedia constantly processing, ordering, shaping and regulating people, objects and spaces. Such actions (re)configure the boundaries of what it means to be human, worker and medium

Media Distortions

Media Distortions is about the power behind the production of deviant media categories. It shows　the politics behind categories we take for granted such as spam and noise, and what it means to our broader understanding of, and engagement with media. The book synthesizes media theory, sound studies, science and technology studies (STS), feminist technoscience, and software studies into a new composition to explore media power. Media Distortions argues that using sound as a conceptual framework is more useful due to its ability to cross boundaries and strategically move between multiple spaces—which is essential for multi-layered mediated spaces. Drawing on repositories of legal, technical and archival sources, the book amplifies three stories about the construction and negotiation of the ‘deviant’ in media. The book starts in the early 20th century with Bell Telephone’s production of noise, tuning into the training of their telephone operators and their involvement with the Noise Abatement　Commission in New York City. The next story jumps several decades to the early 2000s focusing on web metric standardization in the European Union and shows how the digital advertising industry constructed web-cookies as legitimate communication while making spam illegal. The final story focuses on the recent decade and the way Facebook filters out antisocial behaviors to engineer a sociality that produces more value. These stories show how deviant categories re-draw boundaries between human and non-human, public and private spaces, and importantly, social and antisocial

Personal Email Spam Filtering with Minimal User Interaction

This thesis investigates ways to reduce or eliminate the necessity of user input to learning-based personal email spam filters. Personal spam filters have been shown in previous studies to yield superior effectiveness, at the cost of requiring extensive user training which may be burdensome or impossible. This work describes new approaches to solve the problem of building a personal spam filter that requires minimal user feedback. An initial study investigates how well a personal filter can learn from different sources of data, as opposed to user’s messages. Our initial studies show that inter-user training yields substantially inferior results to intra-user training using the best known methods. Moreover, contrary to previous literature, it is found that transfer learning degrades the performance of spam filters when the source of training and test sets belong to two different users or different times. We also adapt and modify a graph-based semi-supervising learning algorithm to build a filter that can classify an entire inbox trained on twenty or fewer user judgments. Our experiments show that this approach compares well with previous techniques when trained on as few as two training examples. We also present the toolkit we developed to perform privacy-preserving user studies on spam filters. This toolkit allows researchers to evaluate any spam filter that conforms to a standard interface defined by TREC, on real users’ email boxes. Researchers have access only to the TREC-style result file, and not to any content of a user’s email stream. To eliminate the necessity of feedback from the user, we build a personal autonomous filter that learns exclusively on the result of a global spam filter. Our laboratory experiments show that learning filters with no user input can substantially improve the results of open-source and industry-leading commercial filters that employ no user-specific training. We use our toolkit to validate the performance of the autonomous filter in a user study