"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Improving spam filtering in enterprise email systems with blockchain-based token incentive mechanism

Spam has caused serious problems for email systems. To address this issue, numerous spam filter algorithms have been developed, all of which require extensive training on labeled spam datasets to obtain the desired filter performance. However, users\u27 privacy concerns and apathy make it difficult to acquire personalized spam data in real-world applications. When it comes to enterprise email systems, the problem worsens because enterprises are extremely sensitive to the possible disclosure of confidential information during the reporting of spam to the cloud. Targeting these obstacles, this study proposes a blockchain-based token incentive mechanism, with the aim of encouraging users to report spam while protecting business secrets and ensuring the transparency of reward rules. The proposed mechanism also enables a decentralized ecosystem for token circulation, fully utilizing the advantages of blockchain technologies. We developed a prototype of the proposed system, on which we conducted a user experiment to verify our design. Results indicate that the proposed incentive mechanism is effective and can raise the probability of spam reporting by more than 1.4 times

Enabling Social Applications via Decentralized Social Data Management

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially-aware applications. We present Prometheus, a socially-aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing non-trivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of Foundations of Social Computing, ACM Transactions on Internet Technolog

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Web Authoring Tool for Effective Content Management

Internet is the most popular medium to express our views and ideas. With the help of internet, the people publish their information or data in the form of web pages. Static web pages are quick and easy to put together all the pages, but maintenance is diffi cult when the site goes longer. Al so, it is too difficult to maintain the document consistent. Dynamic web pages are created with the help of scripting languages, but it requires minimum knowledge of the programming language used for website creation. Therefore, a web content management system (WCMS) is a software system that provides website authoring and administration tools . It is designed in such a way that it allow users to create and manage websites with little knowledge of web programming languages or markup languages. It offers the users with the ability to manage the documents and output for multiple author editing and participation. It improves the performance by using the server - side caching technique. So, in this paper, we presented that WCMS can be used for college to create t heir own web pages effectively and easily without much knowledge of programming languages . Also, we provide various measures to validate our work by comparing various methods of creating websites . The comparison s between the obtained charts and the val idation results clearly explain that the WCMS we presented produces the better resul

Intelligent Computing for Big Data

Recent advances in artificial intelligence have the potential to further develop current big data research. The Special Issue on ‘Intelligent Computing for Big Data’ highlighted a number of recent studies related to the use of intelligent computing techniques in the processing of big data for text mining, autism diagnosis, behaviour recognition, and blockchain-based storage

A Facebook event collector framework for profile monitoring purposes

Social networks have recently emerged to become vital tools for information and content dissemination among connections. Indeed, the immense increase of number of users of Facebook made it rise to become the largest existing social network with more than 1.2 billion active users. However, these numbers also rose the attention of hackers and attackers who aim at propagating malware and viruses for obtaining confidential information regarding social network users. In this manner, it is crucial that each Facebook user is able to easily access, control and analyse the information shared on the corresponding profile so that profile usage deviations can be more efficiently detected. However, despite the fact that Facebook allows an analysis of all user actions through the Timeline Review, this information is not comprehensively organized and there is no statistical analysis of the user generated data. In this paper, we propose a novel framework comprising a Facebook event collector, which by being provided with an authentication token for a user profile obtained through a Facebook application developed for this purpose, collects all the corresponding posted information and stores it in a relational database for \textit{a posteriori} analysis. Through the graphical interface of the developed application, users can access all stored information in a comprehensible manner, according to the type of event, thus facilitating the analysis of user behaviour. By storing each event with the corresponding timestamp, we are able to perform an efficient and comprehensive analysis of all posted contents and compute statistical models over the obtained data. In this manner, we can create a notion of normal usage profile and detect possible deviations which may be indicative of a compromised user account