Artificial Intelligence and Data Mining Approaches in Security Frameworks

Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to Artificial Intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalize security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and Data Mining and several other computing technologies to deploy such system in an effective manner.

This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice.

This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library.

Keywords:

Artificial Intelligence; Data Mining Security; Machine Learning; NLP; AI with data mining; Adaptive Computation; Deep Learning Algorithms; Text Support System; Cognitive Computing; Privacy; Preserving data; preserving data mining; dimensionality reduction; Data clustering; dynamic sampling; Feature selection, extraction, and construction; IDS in telecommunication network; discriminative mining approach; framework; Heterogeneous distributed systems; Enhanced Data Mining; Malware Detection, ; Windows Application Programming; Gain Ratio; Internet Security Threat; Hybrid malicious code detection; network intrusion detection; Intrusion detection techniques; Text data; Threat; KDD; neural network; Interface (API) Calls; Cyber security; knowledge sharing; Digital Signatures; NIDSs (Network intrusion detection systems); NDAEs (Non-symmetric Deep Auto Encode); pattern recognition; Network; Fuzzy logic; Fuzzy set; Classification; Clustering; Association; Gini Index; Business ethics; Texture Analysis; Web Mining