Rough clustering for web transactions

Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions by given threshold. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. In this study, an alternative technique for grouping web transactions using rough set theory is proposed. It is based on the two similarity classes which is nonvoid intersection. The technique is implemented in MATLAB ® version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from: http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the simulation processes. The simulation reveals that the proposed technique significantly requires lower response time up to 62.69 % and 66.82 % as compared to the rough approximation-based clustering, severally. Meanwhile, for cluster purity it performs better until 2.5 % and 14.47%, respectively

Profiling Users by Modeling Web Transactions

Users of electronic devices, e.g., laptop, smartphone, etc. have characteristic behaviors while surfing the Web. Profiling this behavior can help identify the person using a given device. In this paper, we introduce a technique to profile users based on their web transactions. We compute several features extracted from a sequence of web transactions and use them with one-class classification techniques to profile a user. We assess the efficacy and speed of our method at differentiating 25 users on a dataset representing 6 months of web traffic monitoring from a small company network.Comment: Extended technical report of an IEEE ICDCS 2017 publicatio

Cross-Site Scripting (XSS) Detection Integrating Evidences in Multiple Stages

As Cross-Site Scripting (XSS) remains one of the top web security risks, people keep exploring ways to detect such attacks efficiently. So far, existing solutions only focus on the payload in a web request or a response, a single stage of a web transaction. This work proposes a new approach that integrates evidences from both a web request and its response in order to better characterize XSS attacks and separate them from normal web transactions. We first collect complete payloads of XSS and normal web transactions from two databases and extract features from them using the Word2vec technique. Next, we train two Gaussian mixture models (GMM) with these features, one for XSS transaction and one for normal web transactions. These two models can generate two probability scores for a new web transaction, which indicate how similar this web transaction is to XSS and normal traffics respectively. Finally, we put together these two GMM models in classification by combining these two probabilities to further improve detection accuracy

Privacy-preserving Transactions on the Web

There is a rapid growth in the number of applications using sensitive and personal information on the World Wide Web. This growth creates an urgent need to maintain the anonymity of the participants in many web transactions and to preserve the privacy of their sensitive data during data dissemination over the web. First, maintaining the anonymity of users on the World Wide Web is essential for a number of web applications. Anonymity cannot be assured by single interested individuals or an organization but requires participation from other web nodes owned by other entities. Second, preserving the privacy of sensitive data is another very important issue in web transactions. Today, exchanging and sharing personal data between various participants in web transactions endangers privacy. In this article, we discuss various research directions and challenges that need to be addressed while trying to accomplish our goal of maintaining the anonymity of participants and preserving the privacy of sensitive data in web transactions. To maintain anonymity of participants in a web transaction, we propose a method based on the modi fied form of the club mechanism with economic incentives, a solution which rests upon the Prisoner’s Dilemma approach. We compare our approach to other well-known dat a-sharing approaches such as Crowds, Tor, Tarzan and LPWA. To maintain the privacy of sensitive data, we propose a solution based on privacy-preserving data dissemination (P2D2). We also present a solution to implement our approach using Semantic Web Rule Languages and Jena—a Java-based inference engine

Rough Sets Clustering and Markov model for Web Access Prediction

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Trust in Crowds: probabilistic behaviour in anonymity protocols

The existing analysis of the Crowds anonymity protocol assumes that a participating member is either ‘honest’ or ‘corrupted’. This paper generalises this analysis so that each member is assumed to maliciously disclose the identity of other nodes with a probability determined by her vulnerability to corruption. Within this model, the trust in a principal is defined to be the probability that she behaves honestly. We investigate the effect of such a probabilistic behaviour on the anonymity of the principals participating in the protocol, and formulate the necessary conditions to achieve ‘probable innocence’. Using these conditions, we propose a generalised Crowds-Trust protocol which uses trust information to achieves ‘probable innocence’ for principals exhibiting probabilistic behaviour

Global eT@axation : competing visions

The Organization for Economic Cooperation and Development (OECD) places the issue of taxation of cross-border e-commerce transaction as one of its top four projects for investigation. In January 2001, some member countries agreed to a series of guidelines on how to apply existing tax treaties to Web transactions. After discussing potential threats and challenges facing etax administrators and possible solutions, the researchers report on the global momentum towards Extranets for collaborative knowledge management. Previous research indicated a need for a global etaxation Extranet for knowledge management based on the principles of the Cochrane Collaboration in the health sciences. These preliminary findings have been strengthened by parallel moves by the International Organization for Standardization (ISO) in setting up a global Extranet. The global imperative for harmonization of Internet commerce tax initiatives is reflected in contemporary interest in Europe in the redefinition of business requirements and processes related to corporate tax obligations.<br /