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

Because the Internet is so widespread in modern life and because of the expansion of technologies that are tied to it, such as smart cities, self-driving cars, health monitoring via wearables, and mobile banking, a growing number of people are becoming reliant on and addicted to the Internet. In spite of the fact that these technologies provide a great deal of improvement to individuals and communities, they are not without their fair share of concerns. By way of illustration, hackers have the ability to steal from or disrupt companies, therefore inflicting damage to people all across the world, if they exploit weaknesses. As a consequence of cyberattacks, businesses can face financial losses as well as damage to their reputation. Consequently, the security of the network has become a significant concern as a result. Organizations place a significant amount of reliance on tried-and-true technologies such as firewalls, encryption, and antivirus software when it comes to securing their network infrastructure. Unfortunately, these solutions are not completely infallible; they are merely a first line of security against malware and other sophisticated threats. Therefore, it is possible that certain persons who have not been sanctioned may still get access, which might result in a breach of security. For the purpose of preventing intrusion detection, computer systems need to be safeguarded against both illegal users, such as hackers, and legitimate users, such as insiders. A breach of a computer system may result in a number of undesirable results, including the loss of data, restricted access to internet services, the loss of sensitive data, and the exploitation of private resources. an initial version of the Intrusion Detection System (IDS) was constructed. In light of the fact that it is a that is essential for the protection of computer networks, it has therefore become a subject of study that is widely pursued. Given the current condition of cybercrime, it is impossible to deny the significance of the intrusion detection system (IDS). A possible example of how the IDS taxonomy is arranged may be found here. The intrusion detection system, often known as an IDS, is a piece of software or hardware that monitors a computer or network environment, searches for indications of intrusion, and then notifies the user of any potential threats. Utilizing this warning report is something that the administrator or user may do in order to repair the vulnerability that exists inside the system or network. In the aftermath of an intrusion, it may be purposeful or unlawful to attempt to access the data

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry. By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

This book highlights the state-of-the-art research on data usage, security, and privacy in the scenarios of the Internet of Things (IoT), along with related applications using Machine Learning and Big Data technologies to design and make efficient Internet-compatible IoT systems. ICT and Data Sciences brings together IoT and Machine Learning and provides the careful integration of both, along with many examples and case studies. It illustrates the merging of two technologies while presenting basic to high-level concepts covering different fields and domains such as the Hospitality and Tourism industry, Smart Clothing, Cyber Crime, Programming, Communications, Business Intelligence, all in the context of the Internet of Things. The book is written for researchers and practitioners, working in Information Communication Technology and Computer Science.

CYBER SECURITY AND DIGITAL FORENSICS Cyber security is an incredibly important issue that is constantly changing, with new methods, processes, and technologies coming online all the time. Books like this are invaluable to professionals working in this area, to stay abreast of all of these changes. Current cyber threats are getting more complicated and advanced with the rapid evolution of adversarial techniques. Networked computing and portable electronic devices have broadened the role of digital forensics beyond traditional investigations into computer crime. The overall increase in the use of computers as a way of storing and retrieving high-security information requires appropriate security measures to protect the entire computing and communication scenario worldwide. Further, with the introduction of the internet and its underlying technology, facets of information security are becoming a primary concern to protect networks and cyber infrastructures from various threats. This groundbreaking new volume, written and edited by a wide range of professionals in this area, covers broad technical and socio-economic perspectives for the utilization of information and communication technologies and the development of practical solutions in cyber security and digital forensics. Not just for the professional working in the field, but also for the student or academic on the university level, this is a must-have for any library. Audience: Practitioners, consultants, engineers, academics, and other professionals working in the areas of cyber analysis, cyber security, homeland security, national defense, the protection of national critical infrastructures, cyber-crime, cyber vulnerabilities, cyber-attacks related to network systems, cyber threat reduction planning, and those who provide leadership in cyber security management both in public and private sectors

This book shows how machine learning (ML) methods can be used to enhance cyber security operations, including detection, modeling, monitoring as well as defense against threats to sensitive data and security systems. Filling an important gap between ML and cyber security communities, it discusses topics covering a wide range of modern and practical ML techniques, frameworks and tools.

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material. The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.

This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.

This book addresses the topics related to artificial intelligence, the Internet of Things, blockchain technology, and machine learning. It brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need for and impact of advanced cybersecurity and forensics and its implementation with multiple smart computational technologies. This objective answers why and how cybersecurity and forensics have evolved as one of the most promising and widely-accepted technologies globally and has widely-accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each reader’s major or interests. It aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich their scholarly knowledge to achieve sustainable development and foster sustainability. Readers will gain valuable knowledge and insights about smart computing technologies using this exciting book. This book: • Includes detailed applications of cybersecurity and forensics for real-life problems • Addresses the challenges and solutions related to implementing cybersecurity in multiple domains of smart computational technologies • Includes the latest trends and areas of research in cybersecurity and forensics • Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchers Prof. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India. Dr. Shubham Tayal is Assistant Professor at SR University, Warangal, India. Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India. Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun, India.

Dr. P. Kavitha, Associate Professor, Department of Computer Science, Sri Ramakrishna College of Arts & Science, Coimbatore, Tamil Nadu, India. Mr. P. Jayasheelan, Assistant Professor, Department of Computer Science, Sri Krishna Aditya College of arts and Science, Coimbatore, Tamil Nadu, India. Ms. C. Karpagam, Assistant Professor, Department of Computer Science with Data Analytics, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, India. Dr. K. Prabavathy, Assistant Professor, Department of Data Science and Analytics, Sree Saraswathi Thyagaraja College, Pollachi, Coimbatore, Tamil Nadu, India.