Ai In Cybersecurity
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📒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.
📒Implications Of Artificial Intelligence For Cybersecurity ✍ National Academies of Sciences, Engineering, and Medicine
✏Implications of Artificial Intelligence for Cybersecurity Book Summary : In recent years, interest and progress in the area of artificial intelligence (AI) and machine learning (ML) have boomed, with new applications vigorously pursued across many sectors. At the same time, the computing and communications technologies on which we have come to rely present serious security concerns: cyberattacks have escalated in number, frequency, and impact, drawing increased attention to the vulnerabilities of cyber systems and the need to increase their security. In the face of this changing landscape, there is significant concern and interest among policymakers, security practitioners, technologists, researchers, and the public about the potential implications of AI and ML for cybersecurity. The National Academies of Sciences, Engineering, and Medicine convened a workshop on March 12-13, 2019 to discuss and explore these concerns. This publication summarizes the presentations and discussions from the workshop.
📒Hands On Artificial Intelligence For Cybersecurity ✍ Alessandro Parisi
✏Hands On Artificial Intelligence for Cybersecurity Book Summary : Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key Features Identify and predict security threats using artificial intelligence Develop intelligent systems that can detect unusual and suspicious patterns and attacks Learn how to test the effectiveness of your AI cybersecurity algorithms and tools Book Description Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions. This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication. By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI. What you will learn Detect email threats such as spamming and phishing using AI Categorize APT, zero-days, and polymorphic malware samples Overcome antivirus limits in threat detection Predict network intrusions and detect anomalies with machine learning Verify the strength of biometric authentication procedures with deep learning Evaluate cybersecurity strategies and learn how you can improve them Who this book is for If you’re a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you’ll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.
📒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.
📒The Ethics Of Cybersecurity ✍ Markus Christen
✏The Ethics of Cybersecurity Book Summary :
✏ECCWS 2019 18th European Conference on Cyber Warfare and Security Book Summary :
📒Ai Leadership For Boards ✍ Fernanda Torre
✏AI Leadership for Boards Book Summary : It is predicted that artificial intelligence [Al], and the “big data” on which Al is based, will become one of the most challenging board issues in the next ten years. This book, AI Leadership for Boards – The Future of Corporate Governance, offers to provide guidance on how corporate boards should respond to this challenge. The findings indicate that while boards are aware of the importance of Al implementation as a key competitive advantage, they will need to develop two competence areas to successfully steward their firms into an Al-based future:  guiding Al operational capability and  supervising Al governance capability. These areas are combined to create the Boards 4 AI Leadership Matrix – a practical tool for boards to map and develop their Al implementation. The matrix is accompanied by a set of questions to help boards develop a better understanding of where they are today in terms of the development of their Al competences as well as set an ambition for future development. Table of Contents: Acknowledgements Executive Summary Chapter 1. Introduction Chapter 2. Background Chapter 3. Guiding AI operational capability Chapter 3.1. Guiding the gathering, harvesting and analysis of big data in a data strategy Chapter 3.2. Guiding AI-driven innovation strategy Chapter 3.3. Guiding the participation and growth on a business ecosystem Chapter 4. Supervising AI governance Chapter 4.1. Supervising data management, ethics and black box decision-making Chapter 4.2. Supervising AI cybersecurity Chapter 4.3. Supervising business ecosystem participation and leadership Chapter 5. Boards 4 AI leadership matrix– a tool for developing board competence Chapter 6. Beyond competence to the future of board work Chapter 7. Conclusion References The Authors
📒Machine Learning For Cybersecurity Cookbook ✍ Emmanuel Tsukerman
✏Machine Learning for Cybersecurity Cookbook Book Summary : Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering Automate your daily workflow by addressing various security challenges using the recipes covered in the book Book Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learn Learn how to build malware classifiers to detect suspicious activities Apply ML to generate custom malware to pentest your security Use ML algorithms with complex datasets to implement cybersecurity concepts Create neural networks to identify fake videos and images Secure your organization from one of the most popular threats – insider threats Defend against zero-day threats by constructing an anomaly detection system Detect web vulnerabilities effectively by combining Metasploit and ML Understand how to train a model without exposing the training data Who this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.
📒Autonomy And Artificial Intelligence A Threat Or Savior ✍ W.F. Lawless
✏Autonomy and Artificial Intelligence A Threat or Savior Book Summary : This book explores how Artificial Intelligence (AI), by leading to an increase in the autonomy of machines and robots, is offering opportunities for an expanded but uncertain impact on society by humans, machines, and robots. To help readers better understand the relationships between AI, autonomy, humans and machines that will help society reduce human errors in the use of advanced technologies (e.g., airplanes, trains, cars), this edited volume presents a wide selection of the underlying theories, computational models, experimental methods, and field applications. While other literature deals with these topics individually, this book unifies the fields of autonomy and AI, framing them in the broader context of effective integration for human-autonomous machine and robotic systems. The contributions, written by world-class researchers and scientists, elaborate on key research topics at the heart of effective human-machine-robot-systems integration. These topics include, for example, computational support for intelligence analyses; the challenge of verifying today’s and future autonomous systems; comparisons between today’s machines and autism; implications of human information interaction on artificial intelligence and errors; systems that reason; the autonomy of machines, robots, buildings; and hybrid teams, where hybrid reflects arbitrary combinations of humans, machines and robots. The contributors span the field of autonomous systems research, ranging from industry and academia to government. Given the broad diversity of the research in this book, the editors strove to thoroughly examine the challenges and trends of systems that implement and exhibit AI; the social implications of present and future systems made autonomous with AI; systems with AI seeking to develop trusted relationships among humans, machines, and robots; and the effective human systems integration that must result for trust in these new systems and their applications to increase and to be sustained.
📒Ai Vs Hackers ✍ Sarah Fister Gale
✏AI Vs Hackers Book Summary : With cybersecurity, the best defense might just be automated. As hackers grow more sophisticated, cybersecurity teams are increasingly incorporating adaptive tools built around artificial intelligence (AI) and machine learning technology. The need is apparent: According to Cybersecurity Ventures, the annual cost of cyberattacks is expected to increase from US$3 trillion in 2015 to US$6 trillion by 2021.