Informatics Publishing Limited

R. Mekala ¹, J. Sobana ¹, V. Arul ¹, B. Keerthana ¹

Affiliations
1 Department of Computer Science & Engineering, K. S. Rangasamy College of Technology, Tamilnadu, IN

Abstract

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. The project is proposed to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. It is proposed to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. It formulates the problem of determining the number of attackers as a multi-class detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, the project explores using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. The localization results use a representative set of algorithms that provide strong evidence of high accuracy of localizing multiple adversaries. In addition, a fast and effective mobile replica node detection scheme is proposed using the Sequential Probability Ratio Test. evaluated our techniques through two testbedsusing both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real officebuildings.

Keywords

Wireless Network Security, Spoofing Attack, Attack Detection, Localization.

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S. Umamaheswari ¹, Arun M. Kudtarkar ², T. Hamsapriya ³, R. Mekala ⁴

Affiliations
1 Department of Computer Science in The Oxford College of Science, Bangalore, Karnataka, IN
2 Oxford College of Science at Department of Computer Science, Bangalore, Karnataka, IN
3 Department of Information Technology in PSG College of Technology, Coimbatore, TamilNadu, IN
4 Sasurie Academy of Engineering, Coimbatore, IN

Abstract

The openness of P2P network gives a perfect situation for attackers to spread malicious content in the network. In this paper we investigate the design of a secured Information-Sharing Incentive Scheme (ISIS) for decentralized peer-to-peer network (P2P) to ensure trustworthy file sharing. The dynamically growing ISIS increases the efficiency of a P2P network by means of minimal malicious peers and malicious files by establishing trust among good peers as well as identifying and isolating the malicious peers. The ISIS is robust, predictive and invariant under varying network conditions and peer resource constraints.

Keywords

Free Riders, Malicious Peers, P2P Network, Reputation System.

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S. P. Adarshya ¹, R. Mekala ¹, Raseena Arayakkandiyil ¹, V. Gowri ¹

Affiliations
1 Department of Computer Science and Engineering, K.S. Rangasamy College of Technology, Tamilnadu, IN

Abstract

Image spam is a type of e-mail spam that embeds spam text content into graphical images to bypass traditional text based e-mail spam filters. To effectively detect image spam, it is desirable to leverage image content analysis technologies. A solution to determine spam images is to embed both server-side filtering and client-side detection. On the server-side, spectral clustering algorithm is used and in the client-side detection, active learning principle is used. By using spectral clustering algorithm on the server side, it will cluster the spam images and filter the attack activities of spammers and fast trace back the spam source. By using active learning on client-side filtering, the learner guides the users to label as few images as possible while maximizing the classification accuracy. The server-side filtering identifies large image clusters as suspicious spam sources and filtering method is performed to identify the real sources and block them from beginning.

Keywords

Image Spam, Active Learning, Spectral Clustering, Spam Filtering and Image Recognition.

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M. Elavarasi ¹, R. Pramodhini ², M. Deshmukh Deepak ³, R. Mekala ⁴, Chamandeep Kaur ⁵

Affiliations
1 Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, IN
2 Department of Electronics and Communication, Nitte Meenakshi Institute of Technology, IN
3 Department of Mechanical Engineering, Pravara Rural Engineering College, IN
4 Department of Information Technology, M. Kumarasamy College of Engineering, IN
5 Department of Computer Science and Information Technology, Jazan University,, SA

Abstract

In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method.

Keywords

Anomaly Detection, Deep Learning, Image Analysis, Computer Vision, Video Processing

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