Study On Unsupervised Session-Based P2P Botnet Detection PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Study On Unsupervised Session-Based P2P Botnet Detection PDF full book. Access full book title Study On Unsupervised Session-Based P2P Botnet Detection by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
Get Book Here
Book Description
Author: Suyu Gu
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 174
Get Book Here
Book Description
A botnet, which is created to conduct large-scale illegal activities, has become a serious threat to the Internet. Recently, botnets started to utilize a decentralized structure in their command and control channel, which is a more robust and resilient communication infrastructure. P2P botnets, created based on a variety of P2P protocols, are the most representative decentralized botnets and have caused great loss to Internet users. Although a lot of botnet detection techniques have been developed, the existing P2P botnet detection methods are still limited. In this thesis, we present a novel P2P botnet detection system based on an analysis of network behavior. The proposed detection system consists of three main components: Network Packets Capturing, Node Feature Extraction, and Online Classifier. In this thesis, we explain the proposed algorithms and implementation methods for each component in detail. Moreover, in this thesis we also present two novel combined classifiers that integrate supervised machine learning and unsupervised machine learning techniques. One, called Sequential Combined Classifier aims at further enhancing the detection rate; the other one, called Parallel Combined Classifier aims at detecting unknown P2P botnet traffic. Based on three real-world network traffic trace sets (i.e. Storm trace, Waledac trace, and normal traffic trace), a series of evaluation experiments are conducted and their results are reported in this thesis. Several contributions from the evaluation results include (1) identification of an appropriate time window size that allows to provide a better detection performance when used in system's packets capturing module; (2) optimized configuration for system's online classifier in each time window size; and (3) evaluated the effectiveness of two proposed combined classifiers and verified their ability to improve detection rate or detect unknown botnet traffic. According experimental results, we obtain the detection accuracy of 99.0% and the false positive rate of 0.1%.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Shaojun Zhang
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 122
Get Book Here
Book Description
"Botnets have been identified as one of the most dangerous threats through the Internet. A botnet is a collection of compromised computers called zombies or bots controlled by malicious machines called botmasters through the command and control (C&C) channel. Botnets can be used for plenty of malicious behaviours, including DDOS, Spam, stealing sensitive information to name a few, all of which could be very serious threats to parts of the Internet. In this thesis, we propose a peer-to-peer (P2P) botnet detection approach based on 30-second conversation. To the best of our knowledge, this is the first time conversation-based features are used to detect P2P botnets. The features extracted from conversations can differentiate P2P botnet conversations from normal conversations by applying machine learning techniques. Also, feature selection processes are carried out in order to reduce the dimension of the feature vectors. Decision tree (DT) and support vector machine (SVM) are applied to classify the normal conversations and the P2P botnet conversations. Finally, the results from different classifiers are combined based on the probability models in order to get a better result."--Page ii.
Author: Chuan-Yu Chang
Publisher: Springer
ISBN: 9811391904
Category : Computers
Languages : en
Pages : 795
Get Book Here
Book Description
The present book includes extended and revised versions of papers presented during the 2018 International Computer Symposium (ICS 2018), held in Yunlin, Republic of China (Taiwan), on December 20-22, 2018. The 86 papers presented were carefully reviewed and selected from 263 submissions from 11 countries. The variety of the topics include machine learning, sensor devices and platforms, sensor networks, robotics, embedded systems, networks, operating systems, software system structures, database design and models, multimedia and multimodal retrieval, object detection, image processing, image compression, mobile and wireless security.
Author: 陳品豪
Publisher:
ISBN:
Category :
Languages : en
Pages : 28
Get Book Here
Book Description
Author: Irena Koprinska
Publisher: Springer Nature
ISBN: 3030659658
Category : Computers
Languages : en
Pages : 619
Get Book Here
Book Description
This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.
Author: Pari Delir Haghighi
Publisher: Springer Nature
ISBN: 3031483480
Category : Computers
Languages : en
Pages : 196
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 21st International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM2023, organized in conjunction with the 25th International Conference on Information Integration and Web Intelligence, iiWAS 2023, held in Denpasar, Bali, Indonesia, during December 4-6, 2023. The 10 full papers and 5 short papers presented in this book were carefully reviewed and selected from 37 submissions. The papers are divided into the following topical sections: security in mobile environments; mobile computing and wireless sensors; and image and video processing.
Author: Mamoun Alazab
Publisher: Springer
ISBN: 3030130576
Category : Computers
Languages : en
Pages : 246
Get Book Here
Book Description
Cybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points.
Author: Wenke Lee
Publisher: Springer Science & Business Media
ISBN: 0387687688
Category : Computers
Languages : en
Pages : 178
Get Book Here
Book Description
Botnets have become the platform of choice for launching attacks and committing fraud on the Internet. A better understanding of Botnets will help to coordinate and develop new technologies to counter this serious security threat. Botnet Detection: Countering the Largest Security Threat consists of chapters contributed by world-class leaders in this field, from the June 2006 ARO workshop on Botnets. This edited volume represents the state-of-the-art in research on Botnets.
