Data Science In Cybersecurity And Cyberthreat Intelligence
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📒Data Science In Cybersecurity And Cyberthreat Intelligence ✍ Leslie F. Sikos
✏Data Science in Cybersecurity and Cyberthreat Intelligence Book Summary : This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.
📒Cyber Threat Intelligence ✍ Ali Dehghantanha
✏Cyber Threat Intelligence Book Summary : This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.
📒Data Analytics And Decision Support For Cybersecurity ✍ Iván Palomares Carrascosa
✏Data Analytics and Decision Support for Cybersecurity Book Summary : The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.
📒Data Science And Intelligent Applications ✍ Ketan Kotecha
✏Data Science and Intelligent Applications Book Summary :
📒Machine Learning For Cyber Security ✍ Xiaofeng Chen
✏Machine Learning for Cyber Security Book Summary : This book constitutes the proceedings of the Second International Conference on Machine Learning for Cyber Security, ML4CS 2019, held in Xi’an, China in September 2019. The 23 revised full papers and 3 short papers presented were carefully reviewed and selected from 70 submissions. The papers detail all aspects of machine learning in network infrastructure security, in network security detections and in application software security.
📒Big Data Analytics In Cybersecurity ✍ Onur Savas
✏Big Data Analytics in Cybersecurity Book Summary : Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.
📒Darkweb Cyber Threat Intelligence Mining ✍ John Robertson
✏Darkweb Cyber Threat Intelligence Mining Book Summary : The important and rapidly emerging new field known as 'cyber threat intelligence' explores the paradigm that defenders of computer networks gain a better understanding of their adversaries by understanding what assets they have available for an attack. In this book, a team of experts examines a new type of cyber threat intelligence from the heart of the malicious hacking underworld - the dark web. These highly secure sites have allowed anonymous communities of malicious hackers to exchange ideas and techniques, and to buy/sell malware and exploits. Aimed at both cybersecurity practitioners and researchers, this book represents a first step toward a better understanding of malicious hacking communities on the dark web and what to do about them. The authors examine real-world darkweb data through a combination of human and automated techniques to gain insight into these communities, describing both methodology and results.
📒Machine Learning And Cognitive Science Applications In Cyber Security ✍ Khan, Muhammad Salman
✏Machine Learning and Cognitive Science Applications in Cyber Security Book Summary : In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.
📒Ai In Cybersecurity ✍ Leslie F. Sikos
✏AI in Cybersecurity Book Summary : This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures. The current variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterday’s security incidents no longer enables experts to predict and prevent tomorrow’s attacks, which necessitates approaches that go far beyond identifying known threats. Nevertheless, there are promising avenues: complex behavior matching can isolate threats based on the actions taken, while machine learning can help detect anomalies, prevent malware infections, discover signs of illicit activities, and protect assets from hackers. In turn, knowledge representation enables automated reasoning over network data, helping achieve cybersituational awareness. Bringing together contributions by high-caliber experts, this book suggests new research directions in this critical and rapidly growing field.
📒Cyber Threat Intelligence For The Internet Of Things ✍ Elias Bou-Harb
✏Cyber Threat Intelligence for the Internet of Things Book Summary : This book reviews IoT-centric vulnerabilities from a multidimensional perspective by elaborating on IoT attack vectors, their impacts on well-known security objectives, attacks which exploit such vulnerabilities, coupled with their corresponding remediation methodologies. This book further highlights the severity of the IoT problem at large, through disclosing incidents of Internet-scale IoT exploitations, while putting forward a preliminary prototype and associated results to aid in the IoT mitigation objective. Moreover, this book summarizes and discloses findings, inferences, and open challenges to inspire future research addressing theoretical and empirical aspects related to the imperative topic of IoT security. At least 20 billion devices will be connected to the Internet in the next few years. Many of these devices transmit critical and sensitive system and personal data in real-time. Collectively known as “the Internet of Things” (IoT), this market represents a $267 billion per year industry. As valuable as this market is, security spending on the sector barely breaks 1%. Indeed, while IoT vendors continue to push more IoT devices to market, the security of these devices has often fallen in priority, making them easier to exploit. This drastically threatens the privacy of the consumers and the safety of mission-critical systems. This book is intended for cybersecurity researchers and advanced-level students in computer science. Developers and operators working in this field, who are eager to comprehend the vulnerabilities of the Internet of Things (IoT) paradigm and understand the severity of accompanied security issues will also be interested in this book.