Analysis and Detection of P2P Botnet Based on Node Behaviour 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 Analysis and Detection of P2P Botnet Based on Node Behaviour PDF full book. Access full book title Analysis and Detection of P2P Botnet Based on Node Behaviour by Mohammad Reza Rostami. Download full books in PDF and EPUB format.
Author: Mohammad Reza Rostami
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 93
Get Book Here
Book Description
Author: Mohammad Reza Rostami
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages : 93
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: 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.
Author: Shankar Karuppayah
Publisher: Springer
ISBN: 9811090505
Category : Computers
Languages : en
Pages : 118
Get Book Here
Book Description
This book presents current research in the area of advanced monitoring in P2P botnets, and uses a dual-perspective approach to discuss aspects of botnet monitoring in-depth. First, from the perspective of a defender, e.g. researchers, it introduces advanced approaches to successfully monitor botnets, taking the presence of current botnet anti-monitoring mechanisms into consideration. Then, adopting a botmaster perspective to anticipate the advances in future botnets, it introduces advanced measures to detect and prevent monitoring activities. All the proposed methods were evaluated either using real-world data or in a simulation scenario. In addition to providing readers with an in-depth understanding of P2P botnets, the book also analyzes the implications of the various design choices of recent botnets for effectively monitoring them. It serves as an excellent introduction to new researchers and provides a useful review for specialists in the field.
Author: Areej Al-Bataineh
Publisher:
ISBN:
Category : Computer crimes
Languages : en
Pages : 172
Get Book Here
Book Description
Author: Junjie Zhang
Publisher:
ISBN:
Category : Computer networks
Languages : en
Pages :
Get Book Here
Book Description
Botnets represent one of the most serious threats against Internet security since they serve as platforms that are responsible for the vast majority of large-scale and coordinated cyber attacks, such as distributed denial of service, spamming, and information stolen. Detecting botnets is therefore of great importance and a number of network-based botnet detection systems have been proposed. However, as botnets perform attacks in an increasingly stealthy way and the volume of network traffic is rapidly growing, existing botnet detection systems are faced with significant challenges in terms of effectiveness and scalability. The objective of this dissertation is to build novel network-based solutions that can boost both the effectiveness of existing botnet detection systems by detecting botnets whose attacks are very hard to be observed in network traffic, and their scalability by adaptively sampling network packets that are likely to be generated by botnets. To be specific, this dissertation describes three unique contributions. First, we built a new system to detect drive-by download attacks, which represent one of the most significant and popular methods for botnet infection. The goal of our system is to boost the effectiveness of existing drive-by download detection systems by detecting a large number of drive-by download attacks that are missed by these existing detection efforts. Second, we built a new system to detect botnets with peer-to-peer (P2P) command & control (C & C) structures (i.e., P2P botnets), where P2P C & Cs represent currently the most robust C & C structures against disruption efforts. Our system aims to boost the effectiveness of existing P2P botnet detection by detecting P2P botnets in two challenging scenarios: i) botnets perform stealthy attacks that are extremely hard to be observed in the network traffic; ii) bot-infected hosts are also running legitimate P2P applications (e.g., Bittorrent and Skype). Finally, we built a novel traffic analysis framework to boost the scalability of existing botnet detection systems. Our framework can effectively and efficiently identify a small percentage of hosts that are likely to be bots, and then forward network traffic associated with these hosts to existing detection systems for fine-grained analysis, thereby boosting the scalability of existing detection systems. Our traffic analysis framework includes a novel botnet-aware and adaptive packet sampling algorithm, and a scalable flow-correlation technique.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799853497
Category : Computers
Languages : en
Pages : 655
Get Book Here
Book Description
Our world is increasingly driven by sophisticated networks of advanced computing technology, and the basic operation of everyday society is becoming increasingly vulnerable to these networks’ shortcomings. The implementation and upkeep of a strong network defense is a substantial challenge, beset not only by economic disincentives but also by an inherent logistical bias that grants advantage to attackers. Research Anthology on Combating Denial-of-Service Attacks examines the latest research on the development of intrusion detection systems and best practices for preventing and combatting cyber-attacks intended to disrupt business and user experience. Highlighting a range of topics such as network administration, application-layer protocols, and malware detection, this publication is an ideal reference source for cybersecurity professionals, IT specialists, policymakers, forensic analysts, technology developers, security administrators, academicians, researchers, and students.
Author: Amita Dev
Publisher: CRC Press
ISBN: 1000472906
Category : Computers
Languages : en
Pages : 522
Get Book Here
Book Description
The 2nd International Conference on Artificial Intelligence and Speech Technology (AIST2020) was organized by Indira Gandhi Delhi Technical University for Women, Delhi, India on November 19–20, 2020. AIST2020 is dedicated to cutting-edge research that addresses the scientific needs of academic researchers and industrial professionals to explore new horizons of knowledge related to Artificial Intelligence and Speech Technologies. AIST2020 includes high-quality paper presentation sessions revealing the latest research findings, and engaging participant discussions. The main focus is on novel contributions which would open new opportunities for providing better and low-cost solutions for the betterment of society. These include the use of new AI-based approaches like Deep Learning, CNN, RNN, GAN, and others in various Speech related issues like speech synthesis, speech recognition, etc.
Author: Fariba Haddadi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Botnets represent one of the more aggressive threats against cyber security and botnet traffic analysis is one of the main approaches to study and investigate such threats. Botnets employ different techniques (e.g. fluxing and encryption), topologies (e.g. centralized and de-centralized) and communication protocols (e.g. HTTP and DNS) in different stages of their lifecycle. Therefore, identifying the botnets has become very challenging given that they can upgrade their methodology automatically at any time for one reason or another. To this end, different approaches are proposed for botnet traffic analysis and detection based on various botnet behaviours and structures. Hence, the main focus of this thesis is to investigate various botnet detection approaches based on the technique used and the available data. Specifically, two main categories of solutions are explored: application data analysis-based solutions and network analysis-based solutions. In the application data analysis category, two different approaches are explored: one with a priori knowledge and the other one without any a priori knowledge. On the other hand, flow-based botnet detection approaches are explored in the network analysis-based category focused on using minimum a priori knowledge. In this case, various feature extraction methods, machine learning algorithms, protocol filtering, non-numeric feature representation, normal behaviour representation and time generalization issues are investigated. Finally, a flow-based early warning system is proposed. The effectiveness of the solutions is shown on several botnet data sets from IRC botnets to peer-to-peer botnets. Results indicate that the proposed solutions can detect botnet behaviour with good performances. Moreover, two botnet detection systems from the literature and two publicly available malicious behaviour detection systems are employed for further evaluation of the proposed early warning system. The results indicate that the proposed system outperformed these four systems. Last but not least, the proposed system is evaluated as well on botnets in cellular networks on an exploratory basis. It is shown that the proposed system demonstrates promising performance under such circumstances as well.
Author: Ajith Abraham
Publisher: Springer Nature
ISBN: 303073689X
Category : Technology & Engineering
Languages : en
Pages : 1061
Get Book Here
Book Description
This book highlights the recent research on soft computing and pattern recognition and their various practical applications. It presents 62 selected papers from the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020) and 35 papers from the 16th International Conference on Information Assurance and Security (IAS 2020), which was held online, from December 15 to 18, 2020. A premier conference in the field of artificial intelligence, SoCPaR-IAS 2020 brought together researchers, engineers and practitioners whose work involves intelligent systems, network security and their applications in industry. Including contributions by authors from 40 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.
