AI and Machine Learning for Network and Security Management

AI and Machine Learning for Network and Security Management

Author: Yulei Wu

Publisher: John Wiley & Sons

Published: 2022-11-08

Total Pages: 308

ISBN-13: 1119835879

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AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.


Book Synopsis AI and Machine Learning for Network and Security Management by : Yulei Wu

Download or read book AI and Machine Learning for Network and Security Management written by Yulei Wu and published by John Wiley & Sons. This book was released on 2022-11-08 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

Author: Nur Zincir-Heywood

Publisher: John Wiley & Sons

Published: 2021-10-12

Total Pages: 402

ISBN-13: 1119675502

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COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based ­management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud ­systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic ­generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.


Book Synopsis Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning by : Nur Zincir-Heywood

Download or read book Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning written by Nur Zincir-Heywood and published by John Wiley & Sons. This book was released on 2021-10-12 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: COMMUNICATION NETWORKS AND SERVICE MANAGEMENT IN THE ERA OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Discover the impact that new technologies are having on communication systems with this up-to-date and one-stop resource Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning delivers a comprehensive overview of the impact of artificial intelligence (AI) and machine learning (ML) on service and network management. Beginning with a fulsome description of ML and AI, the book moves on to discuss management models, architectures, and frameworks. The authors also explore how AI and ML can be used in service management functions like the generation of workload profiles, service provisioning, and more. The book includes a handpicked selection of applications and case studies, as well as a treatment of emerging technologies the authors predict could have a significant impact on network and service management in the future. Statistical analysis and data mining are also discussed, particularly with respect to how they allow for an improvement of the management and security of IT systems and networks. Readers will also enjoy topics like: A thorough introduction to network and service management, machine learning, and artificial intelligence An exploration of artificial intelligence and machine learning for management models, including autonomic management, policy-based management, intent based ­management, and network virtualization-based management Discussions of AI and ML for architectures and frameworks, including cloud ­systems, software defined networks, 5G and 6G networks, and Edge/Fog networks An examination of AI and ML for service management, including the automatic ­generation of workload profiles using unsupervised learning Perfect for information and communications technology educators, Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning will also earn a place in the libraries of engineers and professionals who seek a structured reference on how the emergence of artificial intelligence and machine learning techniques is affecting service and network management.

AI and Machine Learning for Network and Security Management

AI and Machine Learning for Network and Security Management

Author: Yulei Wu

Publisher: John Wiley & Sons

Published: 2022-10-28

Total Pages: 308

ISBN-13: 1119835895

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AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.


Book Synopsis AI and Machine Learning for Network and Security Management by : Yulei Wu

Download or read book AI and Machine Learning for Network and Security Management written by Yulei Wu and published by John Wiley & Sons. This book was released on 2022-10-28 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

Network Security Empowered by Artificial Intelligence

Network Security Empowered by Artificial Intelligence

Author: Yingying Chen

Publisher: Springer

Published: 2024-06-26

Total Pages: 0

ISBN-13: 9783031535093

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This book introduces cutting-edge methods on security in spectrum management, mobile networks and next-generation wireless networks in the era of artificial intelligence (AI) and machine learning (ML). This book includes four parts: (a) Architecture Innovations and Security in 5G Networks, (b) Security in Artificial Intelligence-enabled Intrusion Detection Systems. (c) Attack and Defense in Artificial Intelligence-enabled Wireless Systems, (d) Security in Network-enabled Applications. The first part discusses the architectural innovations and security challenges of 5G networks, highlighting novel network structures and strategies to counter vulnerabilities. The second part provides a comprehensive analysis of intrusion detection systems and the pivotal role of AI and machine learning in defense and vulnerability assessment. The third part focuses on wireless systems, where deep learning is explored to enhance wireless communication security. The final part broadens the scope, examining the applications of these emerging technologies in network-enabled fields. The advancement of AI/ML has led to new opportunities for efficient tactical communication and network systems, but also new vulnerabilities. Along this direction, innovative AI-driven solutions, such as game-theoretic frameworks and zero-trust architectures are developed to strengthen defenses against sophisticated cyber threats. Adversarial training methods are adopted to augment this security further. Simultaneously, deep learning techniques are emerging as effective tools for securing wireless communications and improving intrusion detection systems. Additionally, distributed machine learning, exemplified by federated learning, is revolutionizing security model training. Moreover, the integration of AI into network security, especially in cyber-physical systems, demands careful consideration to ensure it aligns with the dynamics of these systems. This book is valuable for academics, researchers, and students in AI/ML, network security, and related fields. It serves as a resource for those in computer networks, AI, ML, and data science, and can be used as a reference or secondary textbook.


Book Synopsis Network Security Empowered by Artificial Intelligence by : Yingying Chen

Download or read book Network Security Empowered by Artificial Intelligence written by Yingying Chen and published by Springer. This book was released on 2024-06-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces cutting-edge methods on security in spectrum management, mobile networks and next-generation wireless networks in the era of artificial intelligence (AI) and machine learning (ML). This book includes four parts: (a) Architecture Innovations and Security in 5G Networks, (b) Security in Artificial Intelligence-enabled Intrusion Detection Systems. (c) Attack and Defense in Artificial Intelligence-enabled Wireless Systems, (d) Security in Network-enabled Applications. The first part discusses the architectural innovations and security challenges of 5G networks, highlighting novel network structures and strategies to counter vulnerabilities. The second part provides a comprehensive analysis of intrusion detection systems and the pivotal role of AI and machine learning in defense and vulnerability assessment. The third part focuses on wireless systems, where deep learning is explored to enhance wireless communication security. The final part broadens the scope, examining the applications of these emerging technologies in network-enabled fields. The advancement of AI/ML has led to new opportunities for efficient tactical communication and network systems, but also new vulnerabilities. Along this direction, innovative AI-driven solutions, such as game-theoretic frameworks and zero-trust architectures are developed to strengthen defenses against sophisticated cyber threats. Adversarial training methods are adopted to augment this security further. Simultaneously, deep learning techniques are emerging as effective tools for securing wireless communications and improving intrusion detection systems. Additionally, distributed machine learning, exemplified by federated learning, is revolutionizing security model training. Moreover, the integration of AI into network security, especially in cyber-physical systems, demands careful consideration to ensure it aligns with the dynamics of these systems. This book is valuable for academics, researchers, and students in AI/ML, network security, and related fields. It serves as a resource for those in computer networks, AI, ML, and data science, and can be used as a reference or secondary textbook.

AI, Machine Learning and Deep Learning

AI, Machine Learning and Deep Learning

Author: Fei Hu

Publisher: CRC Press

Published: 2023-06-05

Total Pages: 420

ISBN-13: 1000878899

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Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered


Book Synopsis AI, Machine Learning and Deep Learning by : Fei Hu

Download or read book AI, Machine Learning and Deep Learning written by Fei Hu and published by CRC Press. This book was released on 2023-06-05 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society, many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices, they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i.e., securing the intelligent systems themselves), AI/ML/DL models and algorithms can actually also be used for cyber security (i.e., the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field, many researchers and industry professionals cannot yet obtain a detailed, comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then, the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both "securing the AI system itself" and "using AI to achieve security" It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered

Practical AI for Cybersecurity

Practical AI for Cybersecurity

Author: Ravi Das

Publisher: CRC Press

Published: 2021-02-26

Total Pages: 395

ISBN-13: 1000349454

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The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.


Book Synopsis Practical AI for Cybersecurity by : Ravi Das

Download or read book Practical AI for Cybersecurity written by Ravi Das and published by CRC Press. This book was released on 2021-02-26 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.

Advanced Computer Science Applications

Advanced Computer Science Applications

Author: Karan Singh

Publisher: CRC Press

Published: 2023-09-15

Total Pages: 410

ISBN-13: 1000839516

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This new book brings together the most recent trends related to AI, machine learning, and network security. The chapters cover diverse topics on machine learning algorithms and security analytics, AI and machine learning, and ntework security applications. The volume presents a survey of speculative parallelism techniques, performance reviews, and efficient power consumption. The book also covers the concepts of IoT, security early detection for COVID-19, multimetric geoprahpical routing in VANETs, V2X communication in VANET, and optimization of congestion control scheme for VANETs. This book is a comprehensive take on recent applications and advancement in the field of computer science and will be of value to scientists, researchers, faculty, and students involved in research in the area of AI, machine learning, and network security.


Book Synopsis Advanced Computer Science Applications by : Karan Singh

Download or read book Advanced Computer Science Applications written by Karan Singh and published by CRC Press. This book was released on 2023-09-15 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book brings together the most recent trends related to AI, machine learning, and network security. The chapters cover diverse topics on machine learning algorithms and security analytics, AI and machine learning, and ntework security applications. The volume presents a survey of speculative parallelism techniques, performance reviews, and efficient power consumption. The book also covers the concepts of IoT, security early detection for COVID-19, multimetric geoprahpical routing in VANETs, V2X communication in VANET, and optimization of congestion control scheme for VANETs. This book is a comprehensive take on recent applications and advancement in the field of computer science and will be of value to scientists, researchers, faculty, and students involved in research in the area of AI, machine learning, and network security.

Artificial Intelligence and Security Challenges in Emerging Networks

Artificial Intelligence and Security Challenges in Emerging Networks

Author: Abassi, Ryma

Publisher: IGI Global

Published: 2019-01-25

Total Pages: 293

ISBN-13: 1522573542

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The recent rise of emerging networking technologies such as social networks, content centric networks, Internet of Things networks, etc, have attracted significant attention from academia as well as industry professionals looking to utilize these technologies for efficiency purposes. However, the allure of such networks and resultant storage of high volumes of data leads to increased security risks, including threats to information privacy. Artificial Intelligence and Security Challenges in Emerging Networks is an essential reference source that discusses applications of artificial intelligence, machine learning, and data mining, as well as other tools and strategies to protect networks against security threats and solve security and privacy problems. Featuring research on topics such as encryption, neural networks, and system verification, this book is ideally designed for ITC procurement managers, IT consultants, systems and network integrators, infrastructure service providers, computer and software engineers, startup companies, academicians, researchers, managers, and students.


Book Synopsis Artificial Intelligence and Security Challenges in Emerging Networks by : Abassi, Ryma

Download or read book Artificial Intelligence and Security Challenges in Emerging Networks written by Abassi, Ryma and published by IGI Global. This book was released on 2019-01-25 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent rise of emerging networking technologies such as social networks, content centric networks, Internet of Things networks, etc, have attracted significant attention from academia as well as industry professionals looking to utilize these technologies for efficiency purposes. However, the allure of such networks and resultant storage of high volumes of data leads to increased security risks, including threats to information privacy. Artificial Intelligence and Security Challenges in Emerging Networks is an essential reference source that discusses applications of artificial intelligence, machine learning, and data mining, as well as other tools and strategies to protect networks against security threats and solve security and privacy problems. Featuring research on topics such as encryption, neural networks, and system verification, this book is ideally designed for ITC procurement managers, IT consultants, systems and network integrators, infrastructure service providers, computer and software engineers, startup companies, academicians, researchers, managers, and students.

Hands-On Artificial Intelligence for Cybersecurity

Hands-On Artificial Intelligence for Cybersecurity

Author: Alessandro Parisi

Publisher: Packt Publishing Ltd

Published: 2019-08-02

Total Pages: 331

ISBN-13: 1789805171

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Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook 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 learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho 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.


Book Synopsis Hands-On Artificial Intelligence for Cybersecurity by : Alessandro Parisi

Download or read book Hands-On Artificial Intelligence for Cybersecurity written by Alessandro Parisi and published by Packt Publishing Ltd. This book was released on 2019-08-02 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets Key FeaturesIdentify and predict security threats using artificial intelligenceDevelop intelligent systems that can detect unusual and suspicious patterns and attacksLearn how to test the effectiveness of your AI cybersecurity algorithms and toolsBook 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 learnDetect email threats such as spamming and phishing using AICategorize APT, zero-days, and polymorphic malware samplesOvercome antivirus limits in threat detectionPredict network intrusions and detect anomalies with machine learningVerify the strength of biometric authentication procedures with deep learningEvaluate cybersecurity strategies and learn how you can improve themWho 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.

Machine Learning Security Principles

Machine Learning Security Principles

Author: John Paul Mueller

Publisher: Packt Publishing Ltd

Published: 2022-12-30

Total Pages: 450

ISBN-13: 1804615404

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Thwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day Key Features Discover how hackers rely on misdirection and deep fakes to fool even the best security systems Retain the usefulness of your data by detecting unwanted and invalid modifications Develop application code to meet the security requirements related to machine learning Book DescriptionBusinesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning. As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references. The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies. This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks. By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.What you will learn Explore methods to detect and prevent illegal access to your system Implement detection techniques when access does occur Employ machine learning techniques to determine motivations Mitigate hacker access once security is breached Perform statistical measurement and behavior analysis Repair damage to your data and applications Use ethical data collection methods to reduce security risks Who this book is forWhether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.


Book Synopsis Machine Learning Security Principles by : John Paul Mueller

Download or read book Machine Learning Security Principles written by John Paul Mueller and published by Packt Publishing Ltd. This book was released on 2022-12-30 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thwart hackers by preventing, detecting, and misdirecting access before they can plant malware, obtain credentials, engage in fraud, modify data, poison models, corrupt users, eavesdrop, and otherwise ruin your day Key Features Discover how hackers rely on misdirection and deep fakes to fool even the best security systems Retain the usefulness of your data by detecting unwanted and invalid modifications Develop application code to meet the security requirements related to machine learning Book DescriptionBusinesses are leveraging the power of AI to make undertakings that used to be complicated and pricy much easier, faster, and cheaper. The first part of this book will explore these processes in more depth, which will help you in understanding the role security plays in machine learning. As you progress to the second part, you’ll learn more about the environments where ML is commonly used and dive into the security threats that plague them using code, graphics, and real-world references. The next part of the book will guide you through the process of detecting hacker behaviors in the modern computing environment, where fraud takes many forms in ML, from gaining sales through fake reviews to destroying an adversary’s reputation. Once you’ve understood hacker goals and detection techniques, you’ll learn about the ramifications of deep fakes, followed by mitigation strategies. This book also takes you through best practices for embracing ethical data sourcing, which reduces the security risk associated with data. You’ll see how the simple act of removing personally identifiable information (PII) from a dataset lowers the risk of social engineering attacks. By the end of this machine learning book, you'll have an increased awareness of the various attacks and the techniques to secure your ML systems effectively.What you will learn Explore methods to detect and prevent illegal access to your system Implement detection techniques when access does occur Employ machine learning techniques to determine motivations Mitigate hacker access once security is breached Perform statistical measurement and behavior analysis Repair damage to your data and applications Use ethical data collection methods to reduce security risks Who this book is forWhether you’re a data scientist, researcher, or manager working with machine learning techniques in any aspect, this security book is a must-have. While most resources available on this topic are written in a language more suitable for experts, this guide presents security in an easy-to-understand way, employing a host of diagrams to explain concepts to visual learners. While familiarity with machine learning concepts is assumed, knowledge of Python and programming in general will be useful.