The Experts below are selected from a list of 2271 Experts worldwide ranked by ideXlab platform

Ioannis Ch. Paschalidis - One of the best experts on this subject based on the ideXlab platform.

  • Botnet Detection using Social Graph Analysis
    arXiv: Social and Information Networks, 2015
    Co-Authors: Jing Wang, Ioannis Ch. Paschalidis
    Abstract:

    Signature-based Botnet Detection methods identify Botnets by recognizing Command and Control (C\&C) traffic and can be ineffective for Botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel Botnet Detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly Detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community Detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world Botnet traffic and compare its performance with other community Detection methods. The results show that our approach works effectively and the refined modularity measure improves the Detection accuracy.

  • Allerton - Botnet Detection using social graph analysis
    2014 52nd Annual Allerton Conference on Communication Control and Computing (Allerton), 2014
    Co-Authors: Jing Wang, Ioannis Ch. Paschalidis
    Abstract:

    Signature-based Botnet Detection methods identify Botnets by recognizing Command and Control (C&C) traffic and can be ineffective for Botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel Botnet Detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly Detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community Detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world Botnet traffic and compare its performance with other community Detection methods. The results show that our approach works effectively and the refined modularity measure improves the Detection accuracy.

Tang Ya-jua - One of the best experts on this subject based on the ideXlab platform.

Jing Wang - One of the best experts on this subject based on the ideXlab platform.

  • Botnet Detection using Social Graph Analysis
    arXiv: Social and Information Networks, 2015
    Co-Authors: Jing Wang, Ioannis Ch. Paschalidis
    Abstract:

    Signature-based Botnet Detection methods identify Botnets by recognizing Command and Control (C\&C) traffic and can be ineffective for Botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel Botnet Detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly Detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community Detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world Botnet traffic and compare its performance with other community Detection methods. The results show that our approach works effectively and the refined modularity measure improves the Detection accuracy.

  • Allerton - Botnet Detection using social graph analysis
    2014 52nd Annual Allerton Conference on Communication Control and Computing (Allerton), 2014
    Co-Authors: Jing Wang, Ioannis Ch. Paschalidis
    Abstract:

    Signature-based Botnet Detection methods identify Botnets by recognizing Command and Control (C&C) traffic and can be ineffective for Botnets that use new and sophisticate mechanisms for such communications. To address these limitations, we propose a novel Botnet Detection method that analyzes the social relationships among nodes. The method consists of two stages: (i) anomaly Detection in an "interaction" graph among nodes using large deviations results on the degree distribution, and (ii) community Detection in a social "correlation" graph whose edges connect nodes with highly correlated communications. The latter stage uses a refined modularity measure and formulates the problem as a non-convex optimization problem for which appropriate relaxation strategies are developed. We apply our method to real-world Botnet traffic and compare its performance with other community Detection methods. The results show that our approach works effectively and the refined modularity measure improves the Detection accuracy.

Suleiman Y. Yerima - One of the best experts on this subject based on the ideXlab platform.

  • Deep Learning Techniques for Android Botnet Detection
    Electronics, 2021
    Co-Authors: Suleiman Y. Yerima, Mohammed K. Alzaylaee, Annette Shajan, Vinod P
    Abstract:

    Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger Botnet are becoming prevalent. This calls for more effective methods for Detection of Android Botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android Botnet Detection. However, studies that extensively investigate the efficacy of various deep learning models for Android Botnet Detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android Botnet Detection using 6802 Android applications consisting of 1929 Botnet applications from the ISCX Botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX Botnet dataset and also outperformed the classical machine learning classifiers.

  • CyberSA - Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks
    2020 International Conference on Cyber Situational Awareness Data Analytics and Assessment (CyberSA), 2020
    Co-Authors: Suleiman Y. Yerima, Mohammed K. Alzaylaee
    Abstract:

    Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger Botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect Botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android Botnet Detection based on Convolutional Neural Networks (CNN). Our proposed Botnet Detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between Botnet apps and normal apps. The trained Botnet Detection model was evaluated on a set of 6,802 real applications containing 1,929 Botnets from the publicly available ISCX Botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android Botnet Detection.

Madihah Mohd Saudi - One of the best experts on this subject based on the ideXlab platform.

  • A Systematic Review Analysis for Mobile Botnet Detection using GPS Exploitation
    2018
    Co-Authors: Muhammad Mat Yusof, Madihah Mohd Saudi, Farida Ridzuan
    Abstract:

    At present, mobile Botnet has become a cyber threat for smartphone users especially on the Android platform. It has the capabilities to exploit the vulnerabilities and steal confidential information in the victim’s smartphone. Zeus, DroidDream and MisoSMS are examples of mobile Botnets that have affected thousands of users worldwide. Therefore, this research paper presents a systematic review analysis on the existing techniques for mobile Botnet Detection techniques. It discusses the strengths and the weaknesses of the existing mobile Botnet Detection techniques and related works of mobile Botnet that exploit GPS. This research paper can be used as a reference and guidance for those with the same interest.

  • A Systematic Review Analysis of Root Exploitation for Mobile Botnet Detection
    Lecture Notes in Electrical Engineering, 2015
    Co-Authors: Hasan Al-banna Hashim, Madihah Mohd Saudi, Nurlida Basir
    Abstract:

    Nowadays, mobile Botnet has become as one of the most dangerous threats for smartphone. It has the capabilities of committing many criminal activities, such as remote access, Denial of Service (DoS), phishing, spreading malwares, stealing information and building mobile devices for illegitimate exchange of information and it is crucial to have an efficient mobile Botnet Detection mechanism. Therefore, this research paper presents a systematic review analysis of root exploitation for mobile Botnet Detection and a proof of concept how the mobile Botnet attacks. This proof of concept includes analysis of mobile Botnet sample using reverse engineering technique and static analysis.

  • Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm
    Lecture Notes in Electrical Engineering, 2014
    Co-Authors: M. Z. A. Rahman, Madihah Mohd Saudi
    Abstract:

    Nowadays smart phone has been used all over the world and has become as one of the most targeted platforms of mobile Botnet to steal confidential information especially related with online banking. It is seen as one of the most dangerous cyber threat. Therefore in this research paper, a systematic analysis on mobile Botnet Detection techniques is further investigated and evaluated. A case study was carried out to reverse engineering the mobile Botnet codes. Based on the findings, this mobile Botnet has successfully posed itself as a fake anti-virus and has the capability to steal important data such as username and password from the Android-based devices. Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile Botnet Detection techniques.