Artificial Intelligence and Data Mining Approaches in Security Frameworks. Группа авторов

systems function, but they also enable us to predict system behavior before a system is actually built. They can also accurately analyze systems under varying operating conditions. This book provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. We also explained how to effectively use AI and Data Mining techniques to successfully apply the modeling and simulation techniques presented.

      After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling with practical examples and coding different types of systems using modeling techniques, such as the Pattern Recognition, Automatic Threat detection, Automatic problem solving, etc.

      Preparing both undergraduate and graduate students for advanced modeling and simulation courses, this text helps them carry out effective simulation studies. In addition, graduate students should be able to comprehend and conduct AI and Data Mining research after completing this book.

      This book is organized into fifteen chapters. In Chapter 1, this Chapter discusses about the cyber security needs that can be addressed by AI techniques. It talks about the traditional approach and how AI can be used to modify the multilayered security mechanism used in companies today. Here we propose a system that adds additional layer of security in order to detect any unwanted intrusion. The ever-expanding danger of digital assaults, cybercrimes, and malware attacks has grown exponentially with evolution of artificial intelligence. Conventional ways of cyber-attacks have now taken a turning point, consequently, the attackers resort to more intelligent ways.

      In Chapter 2, we have tried to show the power of intrusion detection is the most important application of data mining by applying different data mining techniques to detect it effectively and report the same in actual time so that essential and required arrangements can be made to stop the efforts made by the trespassery.

      In Chapter 3, we have explained about how Artificial Intelligence (AI) is a popular expression in the digital world. It is as yet a creating science in various features as indicated by the difficulties tossed by 21st century. Usage of artificial intelligence has gotten undefined from human life. Nowadays one can’t imagine a world without AI as it has a ton of gigantic impact on human life. The essential objective of artificial intelligence is to develop the advancement based activities which addresses the human data in order to handle issues. Basically artificial intelligence is examination of how an individual think, work, learn and pick in any circumstance of life, whether or not it may be related to basic reasoning or learning new things or thinking equitably or to appear at an answer, etc.

      In Chapter 4, we have explained further proposed a botnet identification version using optics algorithm that hopes to effectively discover botnets and perceive the type botnet detected by way of addition of latest feature; incorporation of changed traces to pinpoint supply IP of bot master, identification of existence of the kind of services the botnets have get right of entry to to are areas the proposed solution will cater for.

      In Chapter 5, we have explained about models basically ‘learns’ from experience with respect to some task and are capable of finding ‘commonality’ in many different observations. This study discusses various methods of spam filtering using existing Artificial Intelligence techniques and compares their strengths and limitations.

      In Chapter 6, we have explained about how as artificial intelligence people in general to improve, there are risks associated with their utilization, set up in functioning frameworks, tools, calculations, framework the executives, morals and duty, and privacy. The study focuses around the risks and threats of computerized reasoning and how AI can help comprehend network safety or areas of cyber security issues.

      In Chapter 7, we have explained about problem to make privacy in multi-tenant in the single framework. For that problem we use the artificial intelligence concept to improve the security and privacy concept in multitenant based system. Using Artificial intelligence the privacy and security concept make strong because in artificial intelligence work as intelligent human or animal mind it make maximum changes to fulfill the requirement of the concept to achieve the goal. In this chapter describes the issues of privacy and security problems in multi tenancy.

      In Chapter 8, we have provided detailed explanations of a novel approach for biometric recognition has been introduced in which the application of ILBP (Improved Local Binary Pattern) for facial feature detection is discussed which generates the improved features for the facial pattern. It allows only authenticated user to access a system which is better than previous algorithms. Previous research for face detection shows many demerits in terms of false acceptance and rejection rates. In this paper, the extraction of facial features is done from static and dynamic frames using the Haar cascade algorithm.

      In Chapter 9, we have explained about a the developed system consists of a climbing robot, camera for image capturing, IoT modules for transmitting images to cloud, image processing platform, and artificial neural network module intended for decision making. Climbing robot holds the cable with the grooved wheels along with the auto trigger camera and the IoT module. For inspection, the robot ascends along the cables continuously and acquires images of various segments of the cable. Then the captured images have been send to the cloud storage through IoT system. The stored images have been retrieved and their sizes have been reduced through the image processing techniques.

      In Chapter 10, we have a digital security threats results from the character of those omnipresent and at times over the top interchanges interconnections. Digital security isn’t one aspect, yet rather it’s a gaggle of profoundly various issues mentions various arrangements of threats. An Advance Cyber Security System utilizing emblematic rationale might be a framework that comprises of a standard safe and an instrument for getting to and running the standards. The vault is ordinarily built with a lot of related standard sets. Fuzzy improvement manages finding the estimations of information boundaries of a luxurious recreated framework which winds up in wanted yield.

      In Chapter 11, the goal of current chapter is to analyze cyber threats and to demonstrate how artificial intelligence and data mining approaches can be effective to fix cyber-attack issues. The field of artificial intelligence has been increasingly playing a vital role in analyzing cyber threat and improving cyber security as well as safety. Mainly three aspects are discussed in this chapter. First the process of cyber-attack detection which will help to analyses and classify cyber incident, Second task is forecasting upcoming cyber-attack and to control the cyber terrorism. Finally the chapter focus on theoretical background and practical usability of artificial intelligence with data mining approaches for addressing above detection and prediction.

      In Chapter 12, this chapter explores the modern intrusion detection with a distinctive determination perspective of data mining. This discussion focuses on major facets of intrusion detection strategy that is misuse detection. Below content focuses on, to identify attacks, information or data which is present on the network using C4.5 algorithm, which is type of decision tree technique and also it helps to enhance the IDS system to recognize types of attacks in network. For this attack detection, KDD-99 dataset is used, contains several features and different class of general and attack type data.