Effective and Scalable Botnet Detection in Network Traffic 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 Effective and Scalable Botnet Detection in Network Traffic PDF full book. Access full book title Effective and Scalable Botnet Detection in Network Traffic by Junjie Zhang. Download full books in PDF and EPUB format.
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: 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: 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: Sandeep Yadav
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Computer networks are constantly being attacked by malicious entities for various reasons. Network based attacks include but are not limited to, Distributed Denial of Service (DDoS), DNS based attacks, Cross-site Scripting (XSS) etc. Such attacks have exploited either the network protocol or the end-host software vulnerabilities for perpetration. Current network traffic analysis techniques employed for detection and/or prevention of these anomalies suffer from significant delay or have only limited scalability because of their huge resource requirements. This dissertation proposes more scalable techniques for network anomaly detection. We propose using DNS analysis for detecting a wide variety of network anomalies. The use of DNS is motivated by the fact that DNS traffic comprises only 2-3% of total network traffic reducing the burden on anomaly detection resources. Our motivation additionally follows from the observation that almost any Internet activity (legitimate or otherwise) is marked by the use of DNS. We propose several techniques for DNS traffic analysis to distinguish anomalous DNS traffic patterns which in turn identify different categories of network attacks. First, we present MiND, a system to detect misdirected DNS packets arising due to poisoned name server records or due to local infections such as caused by worms like DNSChanger. MiND validates misdirected DNS packets using an externally collected database of authoritative name servers for second or third-level domains. We deploy this tool at the edge of a university campus network for evaluation. Secondly, we focus on domain-fluxing botnet detection by exploiting the high entropy inherent in the set of domains used for locating the Command and Control (C&C) server. We apply three metrics namely the Kullback-Leibler divergence, the Jaccard Index, and the Edit distance, to different groups of domain names present in Tier-1 ISP DNS traces obtained from South Asia and South America. Our evaluation successfully detects existing domain-fluxing botnets such as Conficker and also recognizes new botnets. We extend this approach by utilizing DNS failures to improve the latency of detection. Alternatively, we propose a system which uses temporal and entropy-based correlation between successful and failed DNS queries, for fluxing botnet detection. We also present an approach which computes the reputation of domains in a bipartite graph of hosts within a network, and the domains accessed by them. The inference technique utilizes belief propagation, an approximation algorithm for marginal probability estimation. The computation of reputation scores is seeded through a small fraction of domains found in black and white lists. An application of this technique, on an HTTP-proxy dataset from a large enterprise, shows a high detection rate with low false positive rates. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148330
Author: Will Gragido
Publisher: Newnes
ISBN: 1597496146
Category : Computers
Languages : en
Pages : 270
Get Book Here
Book Description
Cybercrime and Espionage provides a comprehensive analysis of the sophisticated patterns and subversive multi-vector threats (SMTs) associated with modern cybercrime, cyber terrorism, cyber warfare and cyber espionage. Whether the goal is to acquire and subsequently sell intellectual property from one organization to a competitor or the international black markets, to compromise financial data and systems, or undermine the security posture of a nation state by another nation state or sub-national entity, SMTs are real and growing at an alarming pace. This book contains a wealth of knowledge related to the realities seen in the execution of advanced attacks, their success from the perspective of exploitation and their presence within all industry. It will educate readers on the realities of advanced, next generation threats, which take form in a variety ways. This book consists of 12 chapters covering a variety of topics such as the maturity of communications systems and the emergence of advanced web technology; how regulatory compliance has worsened the state of information security; the convergence of physical and logical security; asymmetric forms of gathering information; seven commonalities of SMTs; examples of compromise and presence of SMTs; next generation techniques and tools for avoidance and obfuscation; and next generation techniques and tools for detection, identification and analysis. This book will appeal to information and physical security professionals as well as those in the intelligence community and federal and municipal law enforcement, auditors, forensic analysts, and CIO/CSO/CISO. Includes detailed analysis and examples of the threats in addition to related anecdotal information Authors’ combined backgrounds of security, military, and intelligence, give you distinct and timely insights Presents never-before-published information: identification and analysis of cybercrime and the psychological profiles that accompany them
Author: Pallavi Vardhamane
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
Get Book Here
Book Description
Over the past few years, many cybersecurity incidents were reported worldwide through distributed denial of service attacks. Many of these attacks were conducted through botnet, which usually consists of a group of infected computers, smartphones or IoT devices. Botnets can be used to perform malicious activities, such as launching distributed denial of service attacks, sending spam emails and compromising sensitive information, and so on. The botnet detection in network security becomes important and gains the attention of researchers worldwide. This report proposes a solution to detect botnet traffic using machine learning approach. First, we used datasets from Malware Capture Facility Project. The datasets contain network traffic data that is collected from the victim target machine. The network traffic data includes both botnet traffic and normal traffic. Second, we preprocessed the traffic data and extracted features such as source address, destination address, port, packet size and so on. Third, we applied the machine learning algorithm to classify botnet and normal traffic. The botnet detection module is trained with one large dataset comprised of both botnet and normal traffic records. After gaining good accuracy for the trained model, another dataset is fed to the module for detection purpose. The proposed approach is able to detect the botnet traffic with good accuracy.
Author: Craig Schiller
Publisher: Elsevier
ISBN: 0080500234
Category : Computers
Languages : en
Pages : 481
Get Book Here
Book Description
The book begins with real world cases of botnet attacks to underscore the need for action. Next the book will explain botnet fundamentals using real world examples. These chapters will cover what they are, how they operate, and the environment and technology that makes them possible. The following chapters will analyze botnets for opportunities to detect, track, and remove them. Then the book will describe intelligence gathering efforts and results obtained to date. Public domain tools like OurMon, developed by Jim Binkley of Portland State University, will be described in detail along with discussions of other tools and resources that are useful in the fight against Botnets. This is the first book to explain the newest internet threat - Botnets, zombie armies, bot herders, what is being done, and what you can do to protect your enterprise Botnets are the most complicated and difficult threat the hacker world has unleashed - read how to protect yourself
Author: Aniss Chohra
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Everyday, security experts and analysts must deal with and face the huge increase of cyber security threats that are propagating very fast on the Internet and threatening the security of hundreds of millions of users worldwide. The detection of such threats and attacks is of paramount importance to these experts in order to prevent these threats and mitigate their effects in the future. Thus, the need for security solutions that can prevent, detect, and mitigate such threats is imminent and must be addressed with scalable and efficient solutions. To this end, we propose a scalable framework, called Daedalus, to analyze streams of NIDS (network-based intrusion detection system) logs in near real-time and to extract useful threat security intelligence. The proposed system pre-processes massive amounts of connections stream logs received from different participating organizations and applies an elaborated anomaly detection technique in order to distinguish between normal and abnormal or anomalous network behaviors. As such, Daedalus detects network traffic anomalies by extracting a set of significant pre-defined features from the connection logs and then applying a time series-based technique in order to detect abnormal behavior in near real-time. Moreover, we correlate IP blocks extracted from the logs with some external security signature-based feeds that detect factual malicious activities (e.g., malware families and hashes, ransomware distribution, and command and control centers) in order to validate the proposed approach. Performed experiments demonstrate that Daedalus accurately identifies the malicious activities with an average F_1 score of 92.88\%. We further compare our proposed approach with existing K-Means and deep learning (LSTMs) approaches and demonstrate the accuracy and efficiency of our system.
Author: Georgios Kambourakis
Publisher: CRC Press
ISBN: 1000639975
Category : Computers
Languages : en
Pages : 426
Get Book Here
Book Description
This book provides solid, state-of-the-art contributions from both scientists and practitioners working on botnet detection and analysis, including botnet economics. It presents original theoretical and empirical chapters dealing with both offensive and defensive aspects in this field. Chapters address fundamental theory, current trends and techniques for evading detection, as well as practical experiences concerning detection and defensive strategies for the botnet ecosystem, and include surveys, simulations, practical results, and case studies.
Author: Jordi Domingo-Pascual
Publisher: Springer Science & Business Media
ISBN: 3642207561
Category : Business & Economics
Languages : en
Pages : 492
Get Book Here
Book Description
The two-volume set LNCS 6640 and 6641 constitutes the refereed proceedings of the 10th International IFIP TC 6 Networking Conference held in Valencia, Spain, in May 2011. The 64 revised full papers presented were carefully reviewed and selected from a total of 294 submissions. The papers feature innovative research in the areas of applications and services, next generation Internet, wireless and sensor networks, and network science. The first volume includes 36 papers and is organized in topical sections on anomaly detection, content management, DTN and sensor networks, energy efficiency, mobility modeling, network science, network topology configuration, next generation Internet, and path diversity.
Author: Sudeep Tanwar
Publisher: Springer Nature
ISBN: 9819914795
Category : Technology & Engineering
Languages : en
Pages : 920
Get Book Here
Book Description
This book features selected research papers presented at the Fourth International Conference on Computing, Communications, and Cyber-Security (IC4S 2022), organized in Ghaziabad India, during October 21–22, 2022. The conference was hosted at KEC Ghaziabad in collaboration with WSG Poland, SFU Russia, & CSRL India. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.
