Cybersecurity in IoT Ecosystems Managing Device Vulnerabilities and Data Exposure

Advances in information security, privacy, and ethics book series, Год журнала: 2025, Номер unknown, С. 63 - 98

Опубликована: Фев. 14, 2025

The proliferation of IoT devices and sensors leads to greater efficiency but also greatly increased attack surfaces. In this part the series, we delve into some key security issues that plague ecosystems with a solution- understanding device vulnerabilities how they led data exposure. are frequently built without strong defences in place, lack secure firmware combined weak authentication making them top list for attackers. We will turn real-world threats, such as distributed denial service (DDoS) attacks launched by botnets breaches disclose personal information or sensitive business information. Although chapter certainly warns risks, it is no means doom-and-gloom line reasoning. paper outlines best practices systems, from security-by-design principles leveraging AI blockchain technologies.

Язык: Английский

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Current Security Issues and Vulnerabilities Associated With Mobile Application

Advances in information security, privacy, and ethics book series, Год журнала: 2025, Номер unknown, С. 41 - 62

Опубликована: Фев. 14, 2025

Mobile applications have become a crucial part of modern life, facilitating everything from social interactions to financial transactions. However, this ubiquity also presents significant security challenge. The landscape mobile app is fraught with vulnerabilities and threats that can compromise user data, privacy, the overall integrity applications. This paper delves into issues affect It provides an in-depth analysis various attack methods used by cybercriminals extract data explores how users protect their information being compromised. discussion will cover common tactics employed attackers, potential consequences losing application these threats, critical measures necessary for safeguarding against such attacks.

Язык: Английский

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Securing the Supply Chain

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 300 - 365

Опубликована: Фев. 2, 2024

The exponential growth of digital connectivity in the logistics landscape has heightened significance cybersecurity. This chapter delves into intricate fabric securing supply chains against evolving cyber threats, aiming to equip professionals with actionable strategies for resilience. Beginning analysing prevailing threat landscape, it illuminates common vulnerabilities and highlights recent impactful attacks targeting chains. Understanding nexus between cybersecurity resilience becomes pivotal, emphasizing need continuous operations amidst adversities. To fortify this resilience, meticulously navigates through risk assessment methodologies, mitigation strategies, imperative role chain visibility. It elaborates on vendor partner management protocols, advocating stringent considerations within contractual agreements. Moreover, outlines robust incident response plans recovery essential mitigating incidents' ramifications.

Язык: Английский

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Sustainability in the Digital Age

Advances in logistics, operations, and management science book series, Год журнала: 2024, Номер unknown, С. 89 - 132

Опубликована: Янв. 19, 2024

This chapter explores the convergence of Industry 4.0 technologies and sustainable supply chain practices, presenting a comprehensive overview these digital advancements' transformative potential. The begins by defining intersection between sustainability 4.0, emphasizing pivotal role in fostering environmentally socially responsible chains. With clear objectives mind, exploration delves into impact key on practices. discussion spans utilization internet things (IoT) for real-time monitoring, big data analytics informed decision-making, integration robotics to enhance ethical manufacturing.

Язык: Английский

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Sustainability Metrics and Measurement in Industry 4.0-Enabled SCM

Advances in logistics, operations, and management science book series, Год журнала: 2024, Номер unknown, С. 176 - 233

Опубликована: Янв. 19, 2024

The rapid advancement of Industry 4.0 technologies has ushered in a new era supply chain management (SCM) that places sustainability at its core. This chapter goes deep into the critical intersection metrics and measurement 4.0-enabled SCM, exploring dynamic landscape where cutting-edge technology meets environmental, social, economic responsibility. Beginning with an introduction to 4.0's transformative role authors elucidate importance this context. research's scope objectives are outlined, emphasizing need decipher intricacies ecosystem. provide comprehensive elucidation key concepts, definitions, unique contributions research endeavour. A central focus is alignment SCM United Nations' Sustainable Development Goals (SDGs), illuminating how emerging can act as catalysts for achieving these global objectives.

Язык: Английский

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Generative AI for Threat Hunting and Behaviour Analysis

Advances in digital crime, forensics, and cyber terrorism book series, Год журнала: 2024, Номер unknown, С. 235 - 286

Опубликована: Сен. 12, 2024

Cyber threats are becoming more advanced, and so is cybersecurity, which getting intellectual better at hiding its presence. The requirement to achieve the balance between proactive resistive threat-hunting measures in this dynamic environment very high. Part four outlines how new AI techniques enable design of existing processes for hunting potential threats. main objective digress into core principles threat hunting, starting from being including scenarios deducing clues based on hypothesis. Then, authors will highlight limitations conventional methods detecting gimmicks that fool even skilled hunters with an unseen smoking hiddenly a never-ending evolutionary process. Two well-studied approaches tackling these challenges generative models like adversarial networks (GANs) variational autoencoders (VAEs).

Язык: Английский

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Generative AI for Threat Intelligence and Information Sharing

Advances in digital crime, forensics, and cyber terrorism book series, Год журнала: 2024, Номер unknown, С. 191 - 234

Опубликована: Сен. 12, 2024

Collaboration in providing threat intelligence and disseminating information enables cyber security professionals to embrace digital most successfully, whose risks are ever-changing. This article dwells on the capacity of machine change by categorising indicators compromise (IOC) actors, then highlights limits traditional methods. Among Artificial tools such as generative adversarial networks (GANs) Variational autoencoders (VAEs), which key innovators, one can create synthetic or fake data that emulates real attack scenarios past. allows cyber-related be analysed differently from before. In addition, this feature secure stakeholder collaborations. It is also meant mainly for factual protects private but exchange helpful information. clear fact showcasing real-world examples demonstrates Al's automation through cybersecurity detection.

Язык: Английский

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Generative AI in Network Security and Intrusion Detection

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 77 - 124

Опубликована: Июль 26, 2024

Protecting virtual assets from cyber threats is essential as we live in a digitally advanced world. Providing responsible emphasis on proper network security and intrusion detection imperative. On the other hand, traditional strategies need supportive tool to adapt transforming threat space. New generative AI techniques like adversarial networks (GANs) variational autoencoders (VAEs) are mainstream technologies required meet gap. This chapter deals with how these models can enhance by inspecting traffic for anomalies malicious behaviors detected through unsupervised learning, which considers strange or emerging phenomena. survey features innovations fault detection, behavior control, deep packet inspection, classification, examples of real-world intrusions GAN-based systems. Furthermore, focuses challenges attacks that require development solid defense mechanisms, such networks. Ethics becomes following matter our list discussions, given privacy transparency accountability be observed when working security. Finally, authors examine trends determine cyber-attacks dealt comprehensively.

Язык: Английский

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Overview of Generative AI Techniques for Cybersecurity

Advances in information security, privacy, and ethics book series, Год журнала: 2024, Номер unknown, С. 1 - 52

Опубликована: Июль 26, 2024

Generative AI techniques have been popular since they can generate data or content that could be hardly distinguished from genuine ones. This chapter comprehensively reviews generative for cybersecurity and its definition, history, applications in different fields. It covers basic ideas such as models, probability distributions, latent spaces. Also, it goes into more detail on some of the approaches like GANs, VAEs, combination RL. The explores structure training processes GANs VAEs demonstrates their application tasks image synthesis, enhancement, novelty detection. interaction between RL models challenges, including exploration-exploitation trade-off. focuses development with help DL analyses benefits deep usage various Evaluation measures problems measuring are discussed, focusing methods improving measurement accuracy. Finally, new directions, transformer-based self-supervised learning, to look at future AI. emphasis is made understanding these due versatility, about possible further developments findings other fields studies provided.

Язык: Английский

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Risk Assessment and Mitigation With Generative AI Models

Advances in digital crime, forensics, and cyber terrorism book series, Год журнала: 2024, Номер unknown, С. 29 - 82

Опубликована: Сен. 12, 2024

Cybersecurity organisations constantly face a risky environment where threats are present. These dangers can jeopardise information, disrupt business operations, and erode trust. Risk assessment mitigation strategies crucial to tackling these challenges effectively. However, traditional approaches often need help keep pace with the changing landscape of cyber that require judgments based on manual analysis. This section delves into how adoption AI techniques, like generative adversarial networks (GANs) or variational autoencoders (VAEs), transform risk methods by simulating scenarios identify anomalies more efficiently than ever before predicting potential future risks in real-time through unsupervised learning methods. By integrating threat intelligence models, authors improve understanding contextual factors abnormal high-risk behaviours.

Язык: Английский

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