Malicious Attack Propagation And Source Identification

This book covers and makes four major contributions: 1) analyzing and surveying the pros and cons of current approaches for identifying rumor sources on complex networks; 2) proposing a novel approach to identify rumor sources in time-varying networks; 3) developing a fast approach to identify multiple rumor sources; 4) proposing a community-based method to overcome the scalability issue in this research area. These contributions enable rumor source identification to be applied effectively in real-world networks, and eventually diminish rumor damages, which the authors rigorously illustrate in this book. In the modern world, the ubiquity of networks has made us vulnerable to various risks. For instance, viruses propagate throughout the Internet and infect millions of computers. Misinformation spreads incredibly fast in online social networks, such as Facebook and Twitter. Infectious diseases, such as SARS, H1N1 or Ebola, have spread geographically and killed hundreds of thousands people. In essence, all of these situations can be modeled as a rumor spreading through a network, where the goal is to find the source of the rumor so as to control and prevent network risks. So far, extensive work has been done to develop new approaches to effectively identify rumor sources. However, current approaches still suffer from critical weaknesses. The most serious one is the complex spatiotemporal diffusion process of rumors in time-varying networks, which is the bottleneck of current approaches. The second problem lies in the expensively computational complexity of identifying multiple rumor sources. The third important issue is the huge scale of the underlying networks, which makes it difficult to develop efficient strategies to quickly and accurately identify rumor sources. These weaknesses prevent rumor source identification from being applied in a broader range of real-world applications. This book aims to analyze and address these issues to make rumor source identification more effective and applicable in the real world. The authors propose a novel reverse dissemination strategy to narrow down the scale of suspicious sources, which dramatically promotes the efficiency of their method. The authors then develop a Maximum-likelihood estimator, which can pin point the true source from the suspects with high accuracy. For the scalability issue in rumor source identification, the authors explore sensor techniques and develop a community structure based method. Then the authors take the advantage of the linear correlation between rumor spreading time and infection distance, and develop a fast method to locate the rumor diffusion source. Theoretical analysis proves the efficiency of the proposed method, and the experiment results verify the significant advantages of the proposed method in large-scale networks. This book targets graduate and post-graduate students studying computer science and networking. Researchers and professionals working in network security, propagation models and other related topics, will also be interested in this book.

This book constitutes the refereed proceedings of the second International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2005, held in Vienna, Austria, in July 2005. The 14 revised full papers presented were carefully reviewed and selected from 51 submissions. The papers are organized in topical sections on obfuscated code detection, honeypots, vulnerability assessment and exploit analysis, anomaly detection, misuse detection, and distributed intrusion detection and IDS testing.

"This book addresses security risks involved with RFID technologies, and gives insight on some possible solutions and preventions in dealing with these developing technologies"--

This book constitutes the proceedings of the 12th International Conference on Information Security and Practice and Experience, ISPEC 2016, held in Zhangjiajie, China, in November 2016. The 25 papers presented in this volume were carefully reviewed and selected from 75 submissions. They cover multiple topics in information security, from technologies to systems and applications.

Businesses in today’s world are adopting technology-enabled operating models that aim to improve growth, revenue, and identify emerging markets. However, most of these businesses are not suited to defend themselves from the cyber risks that come with these data-driven practices. To further prevent these threats, they need to have a complete understanding of modern network security solutions and the ability to manage, address, and respond to security breaches. The Handbook of Research on Intrusion Detection Systems provides emerging research exploring the theoretical and practical aspects of prominent and effective techniques used to detect and contain breaches within the fields of data science and cybersecurity. Featuring coverage on a broad range of topics such as botnet detection, cryptography, and access control models, this book is ideally designed for security analysts, scientists, researchers, programmers, developers, IT professionals, scholars, students, administrators, and faculty members seeking research on current advancements in network security technology.

The rapidly increasing sophistication of cyber intrusions makes them nearly impossible to detect without the use of a collaborative intrusion detection network (IDN). Using overlay networks that allow an intrusion detection system (IDS) to exchange information, IDNs can dramatically improve your overall intrusion detection accuracy. Intrusion Detection Networks: A Key to Collaborative Security focuses on the design of IDNs and explains how to leverage effective and efficient collaboration between participant IDSs. Providing a complete introduction to IDSs and IDNs, it explains the benefits of building IDNs, identifies the challenges underlying their design, and outlines possible solutions to these problems. It also reviews the full-range of proposed IDN solutions—analyzing their scope, topology, strengths, weaknesses, and limitations. Includes a case study that examines the applicability of collaborative intrusion detection to real-world malware detection scenarios Illustrates distributed IDN architecture design Considers trust management, intrusion detection decision making, resource management, and collaborator management The book provides a complete overview of network intrusions, including their potential damage and corresponding detection methods. Covering the range of existing IDN designs, it elaborates on privacy, malicious insiders, scalability, free-riders, collaboration incentives, and intrusion detection efficiency. It also provides a collection of problem solutions to key IDN design challenges and shows how you can use various theoretical tools in this context. The text outlines comprehensive validation methodologies and metrics to help you improve efficiency of detection, robustness against malicious insiders, incentive-compatibility for all participants, and scalability in network size. It concludes by highlighting open issues and future challenges.

This book constitutes revised selected papers of the Second International Conference on Computing Science, Communication and Security, COMS2 2021, held in Gandhinagar, India, in February 2021. Due to the COVID-19 pandemic the conference was held virtually. The 19 full papers were thoroughly reveiwed and selected from 105 submissions. Papers are organised according to the topical sections on artificial intelligence and machine learning; networking and communications.

This book constitutes the refereed proceedings of the 31st Annual IFIP WG 11.3 International Working Conference on Data and Applications Security and Privacy, DBSec 2017, held in Philadelphia, PA, USA, in July 2017. The 21 full papers and 9 short papers presented were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on access control, privacy, cloud security, secure storage in the cloud, secure systems, and security in networks and Web.