A. Jeyasekar ¹, Deepti Bisht ¹, Akshit Dua ¹

Affiliations
1 SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Background/Objectives: Steganography is a technique that hides the secret information in an image and makes it imperceptible. The purpose of steganography is not only to keep others from knowing the hidden information but also to keep others from thinking that the information even exists. Hence the attacker takes this opportunity to hide the exploit code in the image and perform the desired attacks on a system. This is based on a tool named Stegosploit. It also explores the aberrance of images when these images are encoded with JavaScript code and analyzes the impact of this technique. It also provides the proposition of mitigation method for this attack. Methods/Statistical Analysis: In this attack, a malicious code is encoded in the image and when an image is downloaded, the malicious code is executed which performs the intended attack. Since the consequences of the attack depend on the malicious code, there is no predefined signature or behaviour of the attack. Therefore, it is difficult to identify this attack. This paper makes use of steganography as a tool for hiding the JavaScript code which exploits a system by deleting a file, starting a key logger in the background, stealing the sensitive information, damaging the resources of the system etc. Findings: It is observed that a potentially harmful code can be easily hidden in an image but few restrictions can be observed. Sometimes the encoding process is not proper which can lead to loss of code. Moreover, the difference in weighted average of pixels can act as a good mechanism to detect the presence of such an attack at end user system. Applications/Improvement: It helps the users especially students about the new technology that can endangered the privacy as well as their important data. Moreover, it depicts how modern day technology can be used by terrorists for secretly broadcasting messages. This raises a flag for security agencies to stay ahead in the game.

Keywords

Exploit Delivery Technique, Image Hacking, Least Significant Bit, Malicious Code, Steganography, Stegosploit.

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A. Jeyasekar ¹, K. Akshay ¹, Karan ¹

Affiliations
1 Department of Computer Science and Engineering, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Objectives: Recommendation engine is a part of information filtering system that tries to predict the ‘preference’ or ‘rating’ of an item in the E-commerce. Recommendation engines have become extremely common in recent days to make an appropriate recommendation rapidly and effectively about any products on which the user is interested. Methods/ Statistical Analysis: One of popular information filtering systems in the recommendation engine is collaborative filtering where the predictions are made based on the usage patterns of the users who are similar to another user. The accuracy of a recommendation engine using collaborative filtering depends on the techniques used to measure the similarity between the user’s preferences. Therefore, in this paper we use two metrics to measure the similarity between the user’s preferences namely KL Divergences and Euclidean distance. The proposed algorithm works by first clustering the users using k means clustering by utilising the similarity metrics and then computing the global Markov matrix for that cluster. Next, the PageRank value for each user is computed and those values are combined with the global Markov matrix to find the recommendations. Findings: We consider the problem of collaborative filtering to recommend potential items of interest to a user already engaged in a session, using past session of the user and other users. Our algorithm leads to the personalized PageRank, where context is captured by the personalization vector. The results show that the collaborative filtering using Euclidean distance metrics for similarity measure performs well than the KL divergence. Application/ Improvements: The proposed recommendation engine can be used in a wide variety of applications such music, movies, books, news, research articles, social media, search queries, and products in general in order to provide a effective recommendation.

Keywords

Collaborative Filtering, Euclidean Distance, Jaccard Metric, Recommendation Engine, Similarity Metrics.

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