Leslie F. Sikos – författare
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18 produkter
18 produkter
Del 151 - Intelligent Systems Reference Library
AI in Cybersecurity
Häftad, Engelska, 2018
2 226 kr
Skickas inom 10-15 vardagar
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.
Del 177 - Intelligent Systems Reference Library
Data Science in Cybersecurity and Cyberthreat Intelligence
Inbunden, Engelska, 2020
2 186 kr
Skickas inom 10-15 vardagar
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.
E-bok
Engelska, 20202 904 kr
Läs direkt efter köp
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.
Del 177 - Intelligent Systems Reference Library
Data Science in Cybersecurity and Cyberthreat Intelligence
Häftad, Engelska, 2021
2 377 kr
Skickas inom 10-15 vardagar
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.
Inbunden, Engelska, 2021
1 671 kr
Skickas inom 10-15 vardagar
RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
E-bok
Engelska, 20212 110 kr
Läs direkt efter köp
RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Häftad, Engelska, 2022
1 671 kr
Skickas inom 10-15 vardagar
RDF-based knowledge graphs require additional formalisms to be fully context-aware, which is presented in this book. This book also provides a collection of provenance techniques and state-of-the-art metadata-enhanced, provenance-aware, knowledge graph-based representations across multiple application domains, in order to demonstrate how to combine graph-based data models and provenance representations. This is important to make statements authoritative, verifiable, and reproducible, such as in biomedical, pharmaceutical, and cybersecurity applications, where the data source and generator can be just as important as the data itself. Capturing provenance is critical to ensure sound experimental results and rigorously designed research studies for patient and drug safety, pathology reports, and medical evidence generation. Similarly, provenance is needed for cyberthreat intelligence dashboards and attack mapsthat aggregate and/or fuse heterogeneous data from disparate data sources to differentiate between unimportant online events and dangerous cyberattacks, which is demonstrated in this book. Without provenance, data reliability and trustworthiness might be limited, causing data reuse, trust, reproducibility and accountability issues.This book primarily targets researchers who utilize knowledge graphs in their methods and approaches (this includes researchers from a variety of domains, such as cybersecurity, eHealth, data science, Semantic Web, etc.). This book collects core facts for the state of the art in provenance approaches and techniques, complemented by a critical review of existing approaches. New research directions are also provided that combine data science and knowledge graphs, for an increasingly important research topic.
Inbunden, Engelska, 2023
1 577 kr
Skickas inom 10-15 vardagar
This book collects state-of-the-art curriculum development considerations, training methods, techniques, and best practices, as well as cybersecurity lab requirements and aspects to take into account when setting up new labs, all based on hands-on experience in teaching cybersecurity in higher education.In parallel with the increasing number and impact of cyberattacks, there is a growing demand for cybersecurity courses in higher education. More and more educational institutions offer cybersecurity courses, which come with unique and constantly evolving challenges not known in other disciplines. For example, step-by-step guides may not work for some of the students if the configuration of a computing environment is not identical or similar enough to the one the workshop material is based on, which can be a huge problem for blended and online delivery modes. Using nested virtualization in a cloud infrastructure might not be authentic for all kinds of exercises, because some of itscharacteristics can be vastly different from an enterprise network environment that would be the most important to demonstrate to students. The availability of cybersecurity datasets for training and educational purposes can be limited, and the publicly available datasets might not suit a large share of training materials, because they are often excessively documented, but not only by authoritative websites, which render these inappropriate for assignments and can be misleading for online students following training workshops and looking for online resources about datasets such as the Boss of the SOC (BOTS) datasets. The constant changes of Kali Linux make it necessary to regularly update training materials, because commands might not run the same way they did a couple of months ago.The many challenges of cybersecurity education are further complicated by the continuous evolution of networking and cloud computing, hardware and software, which shapes student expectations: what is acceptable and respected today might be obsolete or even laughable tomorrow.
E-bok
Engelska, 20231 999 kr
Läs direkt efter köp
This book collects state-of-the-art curriculum development considerations, training methods, techniques, and best practices, as well as cybersecurity lab requirements and aspects to take into account when setting up new labs, all based on hands-on experience in teaching cybersecurity in higher education.In parallel with the increasing number and impact of cyberattacks, there is a growing demand for cybersecurity courses in higher education. More and more educational institutions offer cybersecurity courses, which come with unique and constantly evolving challenges not known in other disciplines. For example, step-by-step guides may not work for some of the students if the configuration of a computing environment is not identical or similar enough to the one the workshop material is based on, which can be a huge problem for blended and online delivery modes. Using nested virtualization in a cloud infrastructure might not be authentic for all kinds of exercises, because some of itscharacteristics can be vastly different from an enterprise network environment that would be the most important to demonstrate to students. The availability of cybersecurity datasets for training and educational purposes can be limited, and the publicly available datasets might not suit a large share of training materials, because they are often excessively documented, but not only by authoritative websites, which render these inappropriate for assignments and can be misleading for online students following training workshops and looking for online resources about datasets such as the Boss of the SOC (BOTS) datasets. The constant changes of Kali Linux make it necessary to regularly update training materials, because commands might not run the same way they did a couple of months ago. The many challenges of cybersecurity education are further complicated by the continuous evolution of networking and cloud computing, hardware and software, which shapes student expectations: what is acceptable and respected today might be obsolete or even laughable tomorrow.
Engelska, 2023
629 kr
Skickas inom 5-8 vardagar
Häftad, Engelska, 2024
1 577 kr
Skickas inom 10-15 vardagar
This book collects state-of-the-art curriculum development considerations, training methods, techniques, and best practices, as well as cybersecurity lab requirements and aspects to take into account when setting up new labs, all based on hands-on experience in teaching cybersecurity in higher education.In parallel with the increasing number and impact of cyberattacks, there is a growing demand for cybersecurity courses in higher education. More and more educational institutions offer cybersecurity courses, which come with unique and constantly evolving challenges not known in other disciplines. For example, step-by-step guides may not work for some of the students if the configuration of a computing environment is not identical or similar enough to the one the workshop material is based on, which can be a huge problem for blended and online delivery modes. Using nested virtualization in a cloud infrastructure might not be authentic for all kinds of exercises, because some of itscharacteristics can be vastly different from an enterprise network environment that would be the most important to demonstrate to students. The availability of cybersecurity datasets for training and educational purposes can be limited, and the publicly available datasets might not suit a large share of training materials, because they are often excessively documented, but not only by authoritative websites, which render these inappropriate for assignments and can be misleading for online students following training workshops and looking for online resources about datasets such as the Boss of the SOC (BOTS) datasets. The constant changes of Kali Linux make it necessary to regularly update training materials, because commands might not run the same way they did a couple of months ago.The many challenges of cybersecurity education are further complicated by the continuous evolution of networking and cloud computing, hardware and software, which shapes student expectations: what is acceptable and respected today might be obsolete or even laughable tomorrow.
Häftad, Engelska, 2025
506 kr
Skickas inom 10-15 vardagar
This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting.
E-bok
Engelska, 2025652 kr
Läs direkt efter köp
This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting.
Inbunden, Engelska, 2017
1 227 kr
Skickas inom 10-15 vardagar
This book illustrates how to use description logic-based formalisms to their full potential in the creation, indexing, and reuse of multimedia semantics. To do so, it introduces researchers to multimedia semantics by providing an in-depth review of state-of-the-art standards, technologies, ontologies, and software tools. It draws attention to the importance of formal grounding in the knowledge representation of multimedia objects, the potential of multimedia reasoning in intelligent multimedia applications, and presents both theoretical discussions and best practices in multimedia ontology engineering. Readers already familiar with mathematical logic, Internet, and multimedia fundamentals will learn to develop formally grounded multimedia ontologies, and map concept definitions to high-level descriptors. The core reasoning tasks, reasoning algorithms, and industry-leading reasoners are presented, while scene interpretation via reasoning is also demonstrated.Overall, this book offers readers an essential introduction to the formal grounding of web ontologies, as well as a comprehensive collection and review of description logics (DLs) from the perspectives of expressivity and reasoning complexity. It covers best practices for developing multimedia ontologies with formal grounding to guarantee decidability and obtain the desired level of expressivity while maximizing the reasoning potential. The capabilities of such multimedia ontologies are demonstrated by DL implementations with an emphasis on multimedia reasoning applications.
E-bok
PDF, Engelska, 20171 575 kr
Läs direkt efter köp
This book illustrates how to use description logic-based formalisms to their full potential in the creation, indexing, and reuse of multimedia semantics. To do so, it introduces researchers to multimedia semantics by providing an in-depth review of state-of-the-art standards, technologies, ontologies, and software tools. It draws attention to the importance of formal grounding in the knowledge representation of multimedia objects, the potential of multimedia reasoning in intelligent multimedia applications, and presents both theoretical discussions and best practices in multimedia ontology engineering.
Readers already familiar with mathematical logic, Internet, and multimedia fundamentals will learn to develop formally grounded multimedia ontologies, and map concept definitions to high-level descriptors. The core reasoning tasks, reasoning algorithms, and industry-leading reasoners are presented, while scene interpretation via reasoning is also demonstrated.
Overall, this book offers readers an essential introduction to the formal grounding of web ontologies, as well as a comprehensive collection and review of description logics (DLs) from the perspectives of expressivity and reasoning complexity. It covers best practices for developing multimedia ontologies with formal grounding to guarantee decidability and obtain the desired level of expressivity while maximizing the reasoning potential. The capabilities of such multimedia ontologies are demonstrated by DL implementations with an emphasis on multimedia reasoning applications.
Häftad, Engelska, 2018
1 227 kr
Skickas inom 10-15 vardagar
This book illustrates how to use description logic-based formalisms to their full potential in the creation, indexing, and reuse of multimedia semantics. To do so, it introduces researchers to multimedia semantics by providing an in-depth review of state-of-the-art standards, technologies, ontologies, and software tools. It draws attention to the importance of formal grounding in the knowledge representation of multimedia objects, the potential of multimedia reasoning in intelligent multimedia applications, and presents both theoretical discussions and best practices in multimedia ontology engineering. Readers already familiar with mathematical logic, Internet, and multimedia fundamentals will learn to develop formally grounded multimedia ontologies, and map concept definitions to high-level descriptors. The core reasoning tasks, reasoning algorithms, and industry-leading reasoners are presented, while scene interpretation via reasoning is also demonstrated.Overall, this book offers readers an essential introduction to the formal grounding of web ontologies, as well as a comprehensive collection and review of description logics (DLs) from the perspectives of expressivity and reasoning complexity. It covers best practices for developing multimedia ontologies with formal grounding to guarantee decidability and obtain the desired level of expressivity while maximizing the reasoning potential. The capabilities of such multimedia ontologies are demonstrated by DL implementations with an emphasis on multimedia reasoning applications.
Del 151 - Intelligent Systems Reference Library
AI in Cybersecurity
Inbunden, Engelska, 2018
2 161 kr
Skickas inom 10-15 vardagar
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.
E-bok
Engelska, 20182 925 kr
Läs direkt efter köp
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.