By Mehedy Masud
Although using facts mining for protection and malware detection is instantly at the upward push, so much books at the topic supply high-level theoretical discussions to the close to exclusion of the sensible elements. Breaking the mildew, Data Mining instruments for Malware Detection presents a step by step breakdown of ways to strengthen information mining instruments for malware detection. Integrating conception with functional suggestions and experimental effects, it specializes in malware detection functions for e mail worms, malicious code, distant exploits, and botnets.
The authors describe the structures they've got designed and constructed: e mail malicious program detection utilizing info mining, a scalable multi-level characteristic extraction strategy to become aware of malicious executables, detecting distant exploits utilizing info mining, and flow-based id of botnet site visitors through mining a number of log records. for every of those instruments, they element the method structure, algorithms, functionality effects, and obstacles.
- Discusses info mining for rising functions, together with adaptable malware detection, insider chance detection, firewall coverage research, and real-time information mining
- Includes 4 appendices that offer a company starting place in information administration, safe structures, and the semantic web
- Describes the authors’ instruments for move facts mining
From algorithms to experimental effects, this is often one of many few books that may be both helpful to these in undefined, govt, and academia. it's going to aid technologists make a decision which instruments to choose for particular purposes, managers will the right way to make sure even if to continue with a knowledge mining venture, and builders will locate leading edge substitute designs for more than a few applications.
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Additional info for Data Mining Tools for Malware Detection
Although several developments have been made, there are many challenges that remain. For example, because of the large volumes of data, how can the algorithms determine which technique to select and what type of data mining to do? Furthermore, the data may be incomplete, inaccurate, or both. At times there may be redundant information, and at times there may not be sufficient information. It is also desirable to have data mining tools that can switch to multiple techniques and support multiple outcomes.
Appendix B describes trustworthy systems. Appendix C describes secure data, information, and knowledge management, and Appendix D describes semantic web technologies. The appendices, together with the supporting technologies described in Part I, provide the necessary background to understand the content of this book. We have essentially developed a three-layer framework to explain the concepts in this book. 4. Layer 1 is the data mining techniques layer. Layer 2 is our tools layer. Layer 3 is the applications layer.
Therefore, as databases become larger, it becomes increasingly difficult to support decision making. In addition, the data could be from multiple sources and multiple domains. There is a clear need to analyze the data to support planning and other functions of an enterprise. Some of the data mining techniques include those based on statistical reasoning techniques, inductive logic programming, machine learning, fuzzy sets, and neural networks, among others. The data mining problems include classification (finding rules to partition data into groups), association (finding rules to make associations between data), and sequencing (finding rules to order data).