Originally designed as neutral entities, computerized bots are increasingly being used maliciously by online criminals in mass spamming events, fraud, extortion, identity theft, and software theft. Malicious Bots: An Inside Look into the Cyber-Criminal Underground of the Internet explores the rise of dangerous bots and exposes the nefarious methods of “botmasters”. This valuable resource assists information security managers in understanding the scope, sophistication, and criminal uses of bots.
With sufficient technical detail to empower IT professionals, this volume provides in-depth coverage of the top bot attacks against financial and government networks over the last several years. The book presents exclusive details of the operation of the notorious Thr34t Krew, one of the most malicious bot herder groups in recent history. Largely unidentified by anti-virus companies, their bots spread globally for months, launching massive distributed denial of service (DDoS) attacks and warez (stolen software distributions). For the first time, this story is publicly revealed, showing how the botherders got arrested, along with details on other bots in the world today. Unique descriptions of the criminal marketplace – how criminals make money off of your computer – are also a focus of this exclusive book!
With unprecedented detail, the book goes on to explain step-by-step how a hacker launches a botnet attack, providing specifics that only those entrenched in the cyber-crime investigation world could possibly offer.
Authors Ken Dunham and Jim Melnick serve on the front line of critical cyber-attacks and countermeasures as experts in the deployment of geopolitical and technical bots. Their work involves advising upper-level government officials and executives who control some of the largest networks in the world. By examining the methods of Internet predators, information security managers will be better able to proactively protect their own networks from such attacks.
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With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.
From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques.
- Unveils cutting-edge techniques for detecting new attacks
- Contains in-depth discussions of machine learning solutions to detection problems
- Categorizes methods for detecting, scanning, and profiling intrusions and anomalies
- Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions
- Details privacy-preserving data mining methods
This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.
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