The Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address.
物联网（IoT）正在重塑世界，它具有支持医疗保健、自动化、远程监控等领域不断发展的新应用的潜力。在基于物联网的应用迅速普及和增长的同时，针对物联网设备的威胁和恶意软件攻击也大幅增加。此外，由于物联网设备广泛使用基于 Linux 的系统，恶意软件检测成为一项具有挑战性的任务。研究人员和从业人员提出了各种技术来应对物联网生态系统中的这些威胁。研究人员和从业人员都提出了一系列技术来应对物联网生态系统中的这些威胁。然而，尽管提出了大量技术，但仍明显缺乏全面系统的综述，以评估静态和动态分析方法在检测物联网恶意软件方面的功效。这项研究工作是一项系统性文献综述（SLR），旨在简明扼要地总结物联网恶意软件检测领域的最新进展，尤其侧重于静态和动态分析技术的使用。SLR 重点考察了物联网恶意软件检测领域的研究现状、方法和趋势。为此，它综合了在知名学术期刊和会议上发表的大量学术著作的研究成果。此外，SLR 还强调了经验过程的重要性，其中包括选择数据集、准确选择特征和利用机器学习算法在提高检测准确性方面的作用。研究还评估了不同分析技术检测恶意软件的能力，并比较了各种物联网恶意软件检测模型的性能。此外，综述最后还讨论了整个研究界必须解决的几个开放性问题和挑战。
{"title":"IoT malware detection using static and dynamic analysis techniques: A systematic literature review","authors":"Sumit Kumar, Prachi Ahlawat, Jyoti Sahni","doi":"10.1002/spy2.444","DOIUrl":"https://doi.org/10.1002/spy2.444","url":null,"abstract":"The Internet of Things (IoT) is reshaping the world with its potential to support new and evolving applications in areas, such as healthcare, automation, remote monitoring, and so on. This rapid popularity and growth of IoT‐based applications coincides with a significant surge in threats and malware attacks on IoT devices. Furthermore, the widespread usage of Linux‐based systems in IoT devices makes malware detection a challenging task. Researchers and practitioners have proposed a variety of techniques to address these threats in the IoT ecosystem. Both researchers and practitioners have proposed a range of techniques to counter these threats within the IoT ecosystem. However, despite the multitude of proposed techniques, there remains a notable absence of a comprehensive and systematic review assessing the efficacy of static and dynamic analysis methods in detecting IoT malware. This research work is a systematic literature review (SLR) that aims to offer a concise summary of the latest advancements in the field of IoT malware detection, specifically focusing on the utilization of static and dynamic analytic techniques. The SLR focuses on examining the present status of research, methodology, and trends in the area of IoT malware detection. It accomplishes this by synthesizing the findings from a wide range of scholarly works that have been published in well‐regarded academic journals and conferences. Additionally, the SLR highlights the significance of the empirical process that includes the role of selecting datasets, accurate feature selection and the utilization of machine learning algorithms in enhancing the detection accuracy. The study also evaluates the capability of different analysis techniques to detect malware and compares the performance of various models for IoT malware detection. Furthermore, the review concluded by addressing several open issues and challenges that the research community as a whole must address.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The proliferation of digital services and the imperative for secure authentication have necessitated the management of an expanding array of passwords, imposing a significant cognitive burden on users. The predominant method for authentication remains the use of passwords. However, a critical issue with this approach is that individuals frequently forget their passwords, particularly when managing multiple accounts. This often results in users creating similar or easily guessable passwords for different accounts or writing them down, compromising security. This article investigates an innovative method to mitigate cognitive burden using steganography‐embedded quick response (QR) codes for streamlined password management. The proposed model, named MASTER (Multi‐device‐based Authentication using STEgged QR Codes), was evaluated for usability using the system usability scale (SUS) and the subjective mental effort scale. The security of the model is evaluated using attack analysis and comparative analysis with image visibility and robustness. The evaluation results indicate that the MASTER model achieved a SUS score of 75.94, with the majority of participants agreeing that the system reduces cognitive effort.
随着数字服务的激增和安全认证的要求，有必要对越来越多的密码进行管理，这给用户带来了巨大的认知负担。主要的身份验证方法仍然是使用密码。然而，这种方法的一个关键问题是个人经常忘记密码，尤其是在管理多个账户时。这往往会导致用户为不同的账户创建相似或容易猜到的密码，或者把密码写下来，从而影响安全性。本文研究了一种创新方法，利用隐写术嵌入快速反应（QR）代码来减轻认知负担，从而简化密码管理。所提出的模型名为 MASTER（使用 STEgged QR 码的基于多设备的身份验证），使用系统可用性量表（SUS）和主观脑力量表对其可用性进行了评估。通过攻击分析以及与图像可见性和稳健性的比较分析，对该模型的安全性进行了评估。评估结果表明，MASTER 模型的 SUS 得分为 75.94，大多数参与者都认为该系统减少了认知努力。
{"title":"An approach for mitigating cognitive load in password management by integrating QR codes and steganography","authors":"G. Balayogi, Kuppusamy K. S.","doi":"10.1002/spy2.447","DOIUrl":"https://doi.org/10.1002/spy2.447","url":null,"abstract":"The proliferation of digital services and the imperative for secure authentication have necessitated the management of an expanding array of passwords, imposing a significant cognitive burden on users. The predominant method for authentication remains the use of passwords. However, a critical issue with this approach is that individuals frequently forget their passwords, particularly when managing multiple accounts. This often results in users creating similar or easily guessable passwords for different accounts or writing them down, compromising security. This article investigates an innovative method to mitigate cognitive burden using steganography‐embedded quick response (QR) codes for streamlined password management. The proposed model, named MASTER (Multi‐device‐based Authentication using STEgged QR Codes), was evaluated for usability using the system usability scale (SUS) and the subjective mental effort scale. The security of the model is evaluated using attack analysis and comparative analysis with image visibility and robustness. The evaluation results indicate that the MASTER model achieved a SUS score of 75.94, with the majority of participants agreeing that the system reduces cognitive effort.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adi El-Dalahmeh, Moawiah El-Dalahmeh, M. Razzaque, Jie Li
Vehicular ad‐hoc networks (VANETs) support features like comfort, safety, and infotainment, enhancing traffic efficiency. However, traditional VANETs struggle with dynamic and large‐scale networks due to fixed policies and complex architectures, such as constantly changing vehicle positions. Software‐defined networks (SDN) can address these challenges by offering centralized, logical control, making VANETs more flexible and programmable. While SDNs improve VANET efficiency and add security benefits, they also introduce new security risks by incorporating novel technologies and architectural elements. Since VANET services rely heavily on data communication, compromised data (e.g., modified, falsified) could significantly impact driver and vehicle safety, making secure communication vital. Security threats specific to SDNs, like vulnerabilities in centralized control or flow‐based threats exploiting dynamic routing, necessitate robust cryptographic solutions to secure vehicle communications and data exchange. Various cryptographic algorithms, differing in performance, speed, memory requirements, and key sizes, pose challenges in selecting the optimal one for SDN‐based VANETs. This study evaluated seven cryptographic algorithms, including Blowfish, data encryption standard, triple data encryption standard, Rivest–Shamir–Adleman, advanced encryption standard (AES), advanced encryption standard with elliptic curve cryptography (AES‐ECC), and advanced encryption standard with elliptic curve Diffie‐Hellman (AES‐ECDH), in a simulated SDN‐based VANET. The findings show AES‐ECDH as the most effective overall, though the best choice depends on specific deployment scenarios and application needs.
车载 ad-hoc 网络（VANET）支持舒适、安全和信息娱乐等功能，提高了交通效率。然而，由于固定的策略和复杂的架构（如不断变化的车辆位置），传统的 VANET 难以应对动态和大规模网络。软件定义网络（SDN）可通过提供集中的逻辑控制来应对这些挑战，使 VANET 更灵活、更可编程。虽然 SDN 提高了 VANET 的效率并增加了安全方面的优势，但它们也因采用了新技术和架构元素而带来了新的安全风险。由于 VANET 服务在很大程度上依赖于数据通信，受损数据（如修改、伪造）可能会严重影响驾驶员和车辆的安全，因此安全通信至关重要。SDN 特有的安全威胁（如集中控制中的漏洞或利用动态路由的基于流的威胁）需要强大的加密解决方案来确保车辆通信和数据交换的安全。各种加密算法在性能、速度、内存要求和密钥大小方面各不相同，为基于 SDN 的 VANET 选择最佳算法带来了挑战。本研究在模拟的基于 SDN 的 VANET 中评估了七种加密算法，包括 Blowfish、数据加密标准、三重数据加密标准、Rivest-Shamir-Adleman、高级加密标准（AES）、椭圆曲线加密高级加密标准（AES-ECC）和椭圆曲线 Diffie-Hellman 高级加密标准（AES-ECDH）。研究结果表明，AES-ECDH 总体上是最有效的，但最佳选择取决于具体的部署场景和应用需求。
{"title":"Cryptographic methods for secured communication in SDN‐based VANETs: A performance analysis","authors":"Adi El-Dalahmeh, Moawiah El-Dalahmeh, M. Razzaque, Jie Li","doi":"10.1002/spy2.446","DOIUrl":"https://doi.org/10.1002/spy2.446","url":null,"abstract":"Vehicular ad‐hoc networks (VANETs) support features like comfort, safety, and infotainment, enhancing traffic efficiency. However, traditional VANETs struggle with dynamic and large‐scale networks due to fixed policies and complex architectures, such as constantly changing vehicle positions. Software‐defined networks (SDN) can address these challenges by offering centralized, logical control, making VANETs more flexible and programmable. While SDNs improve VANET efficiency and add security benefits, they also introduce new security risks by incorporating novel technologies and architectural elements. Since VANET services rely heavily on data communication, compromised data (e.g., modified, falsified) could significantly impact driver and vehicle safety, making secure communication vital. Security threats specific to SDNs, like vulnerabilities in centralized control or flow‐based threats exploiting dynamic routing, necessitate robust cryptographic solutions to secure vehicle communications and data exchange. Various cryptographic algorithms, differing in performance, speed, memory requirements, and key sizes, pose challenges in selecting the optimal one for SDN‐based VANETs. This study evaluated seven cryptographic algorithms, including Blowfish, data encryption standard, triple data encryption standard, Rivest–Shamir–Adleman, advanced encryption standard (AES), advanced encryption standard with elliptic curve cryptography (AES‐ECC), and advanced encryption standard with elliptic curve Diffie‐Hellman (AES‐ECDH), in a simulated SDN‐based VANET. The findings show AES‐ECDH as the most effective overall, though the best choice depends on specific deployment scenarios and application needs.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science.
在深受物联网（IoT）影响的时代，优先考虑强大的安全性和保护用户隐私至关重要。这篇综合评论文章对物联网安全和隐私面临的多方面挑战和风险进行了细致的研究。它涵盖了硬件、软件和传输中的数据领域，揭示了潜在的漏洞和相关威胁。针对这些问题，本文提出了降低这些风险的有效策略建议。为加强物联网环境中的安全和隐私提供了路线图。此外，本综述还全面评估了不同作者提出的多种解决方案，其主要目的是增强物联网环境中的安全性和隐私性。分析深入揭示了这些解决方案的优势和局限性。这有助于全面了解物联网安全和隐私的现状。此外，本文还深入探讨了将新兴技术融入物联网框架的复杂性。它探讨了这一过程中固有的障碍和挑战，并提出了解决这些障碍的潜在策略。通过这样做，该综述提供了一个关于现有安全和隐私增强技术的整体视角，并为在物联网领域内驾驭新兴技术的动态景观提供了指导。本综述收录的出版物包括期刊论文、会议论文和书籍章节，均来自 SCI（科学引文索引）、Scopus 和 Web of Science 索引的知名来源。
{"title":"Exploring security and privacy enhancement technologies in the Internet of Things: A comprehensive review","authors":"Md. Ataullah, Naveen Chauhan","doi":"10.1002/spy2.448","DOIUrl":"https://doi.org/10.1002/spy2.448","url":null,"abstract":"In the era heavily influenced by Internet of Things (IoT), prioritizing strong security and the protection of user privacy is of utmost importance. This comprehensive review paper embarks on a meticulous examination of the multifaceted challenges and risks facing IoT security and privacy. It encompasses the hardware, software, and data‐in‐transit domains, shedding light on potential vulnerabilities and associated threats. In response to these concerns, this paper puts forth recommendations for effective strategies to mitigate these risks. Providing a road‐map for enhancing security and privacy in IoT environments. Furthermore, this review thoroughly assesses a multitude of solutions proposed by various authors, with the primary aim of enhancing security and privacy within the IoT landscape. The analysis provides insights into the strengths and limitations of these solutions. This is aiding in the development of a holistic comprehension of the existing status of IoT security and privacy. Moreover, the paper delves into the complexities surrounding integrating emerging technologies into the IoT framework. It explores the obstacles and challenges inherent in this process and proposes potential strategies to address these hurdles. By doing so, the review provides a holistic perspective on existing security and privacy enhancement technologies and offers guidance on navigating the dynamic landscape of emerging technologies within the IoT domain. Publications included in the review consist of journal articles, conference papers, and book chapters from reputable sources indexed in SCI (Science Citation Index), Scopus, and Web of Science.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.
{"title":"Research on privacy leakage of celebrity's ID card number based on real‐name authentication","authors":"H. Yue, Zebin Song, Mengli Zhao, Lijia Yang","doi":"10.1002/spy2.442","DOIUrl":"https://doi.org/10.1002/spy2.442","url":null,"abstract":"The Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the rapidly‐developing Internet of Things (IoT) ecosystem, safeguarding the privacy and accuracy of linked devices and networks is of utmost importance, with the challenge lying in effective implementation of intrusion detection systems on resource‐constrained IoT devices. This study introduces a differential privacy (DP)‐aided DeepFed architecture for intrusion detection in IoT contexts as a novel approach to addressing these difficulties. To build an intrusion detection model, we combined components of a convolutional neural network with bidirectional long short‐term memory. We apply this approach to the Bot‐IoT dataset, which was rigorously curated by the University of New South Wales (UNSW) and N‐BaIoT dataset. Our major goal is to create a model that delivers high accuracy while protecting privacy, an often‐overlooked aspect of IoT security. Intrusion detection tasks are distributed across multiple IoT devices using federated learning principles to protect data privacy, incorporating the DP framework to gauge and minimize information leakage, all while investigating the intricate relationship between privacy and accuracy in pursuit of an ideal compromise. The trade‐off between privacy preservation and model accuracy is investigated by adjusting the privacy loss and noise multiplier. Our research enhances IoT security by introducing a deep learning model for intrusion detection in IoT devices, explores the integration of DP in federated learning framework for IoT and offers guidance on minimizing the accuracy‐privacy trade‐off based on specific privacy and security needs. Our study explores the privacy‐accuracy trade‐off by examining the effects of varying epsilon values on accuracy for various delta values for a range of clients between 5 and 25. We also investigate the influence of several noise multipliers on accuracy and find a consistent accuracy curve, especially around a noise multiplier value of about 0.5. The findings of this study have the possibilities to enhance IoT ecosystem security and privacy, contributing to the IoT landscape's trustworthiness and sustainability.
{"title":"A differential privacy aided DeepFed intrusion detection system for IoT applications","authors":"Sayeda Suaiba Anwar, Asaduzzaman, Iqbal H. Sarker","doi":"10.1002/spy2.445","DOIUrl":"https://doi.org/10.1002/spy2.445","url":null,"abstract":"In the rapidly‐developing Internet of Things (IoT) ecosystem, safeguarding the privacy and accuracy of linked devices and networks is of utmost importance, with the challenge lying in effective implementation of intrusion detection systems on resource‐constrained IoT devices. This study introduces a differential privacy (DP)‐aided DeepFed architecture for intrusion detection in IoT contexts as a novel approach to addressing these difficulties. To build an intrusion detection model, we combined components of a convolutional neural network with bidirectional long short‐term memory. We apply this approach to the Bot‐IoT dataset, which was rigorously curated by the University of New South Wales (UNSW) and N‐BaIoT dataset. Our major goal is to create a model that delivers high accuracy while protecting privacy, an often‐overlooked aspect of IoT security. Intrusion detection tasks are distributed across multiple IoT devices using federated learning principles to protect data privacy, incorporating the DP framework to gauge and minimize information leakage, all while investigating the intricate relationship between privacy and accuracy in pursuit of an ideal compromise. The trade‐off between privacy preservation and model accuracy is investigated by adjusting the privacy loss and noise multiplier. Our research enhances IoT security by introducing a deep learning model for intrusion detection in IoT devices, explores the integration of DP in federated learning framework for IoT and offers guidance on minimizing the accuracy‐privacy trade‐off based on specific privacy and security needs. Our study explores the privacy‐accuracy trade‐off by examining the effects of varying epsilon values on accuracy for various delta values for a range of clients between 5 and 25. We also investigate the influence of several noise multipliers on accuracy and find a consistent accuracy curve, especially around a noise multiplier value of about 0.5. The findings of this study have the possibilities to enhance IoT ecosystem security and privacy, contributing to the IoT landscape's trustworthiness and sustainability.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a world where technology is rapidly advancing, regulation of dark pattern practices has become a topic of increasing importance. Society has become somewhat desensitized to these deceptive online practices that manipulate users into taking actions, which are not in their best interests, such as difficulty unsubscribing from a service, prominence of consent buttons, and countless other advanced tactics to obscure transparency. However, these ongoing practices harm both the individual user, and society in general, by impeding informed decision‐making. This Article addresses the European Union's leading efforts to tackle dark pattern practices, and in particular, addresses the numerous legislative acts which have been enacted to regulate and eliminate them. The acts explored in this Article include the General Data Protection Regulation, the Uniform Commercial Practices Directive, the Data Act, the Digital Markets Act, the Digital Services Act, the Amendment to the Directive on Financial Services Contracts Concluded at a Distance, and the Artificial Intelligence Act. This Article then discusses the interplay between the numerous acts, and the resulting ambiguities and overlap which have led to a level of regulatory redundancy. This Article examines not only the difficulty in interpretation of the various acts, but additionally, explores the issues which arise in implementation from a jurisdictional perspective. Further, this Article suggests potential solutions to address the fragmented legislation, including a hybrid form of harmonization, as well as methods for consolidation and centralization.
{"title":"Dark patterns: EU's regulatory efforts","authors":"Johanna Herman","doi":"10.1002/spy2.441","DOIUrl":"https://doi.org/10.1002/spy2.441","url":null,"abstract":"In a world where technology is rapidly advancing, regulation of dark pattern practices has become a topic of increasing importance. Society has become somewhat desensitized to these deceptive online practices that manipulate users into taking actions, which are not in their best interests, such as difficulty unsubscribing from a service, prominence of consent buttons, and countless other advanced tactics to obscure transparency. However, these ongoing practices harm both the individual user, and society in general, by impeding informed decision‐making. This Article addresses the European Union's leading efforts to tackle dark pattern practices, and in particular, addresses the numerous legislative acts which have been enacted to regulate and eliminate them. The acts explored in this Article include the General Data Protection Regulation, the Uniform Commercial Practices Directive, the Data Act, the Digital Markets Act, the Digital Services Act, the Amendment to the Directive on Financial Services Contracts Concluded at a Distance, and the Artificial Intelligence Act. This Article then discusses the interplay between the numerous acts, and the resulting ambiguities and overlap which have led to a level of regulatory redundancy. This Article examines not only the difficulty in interpretation of the various acts, but additionally, explores the issues which arise in implementation from a jurisdictional perspective. Further, this Article suggests potential solutions to address the fragmented legislation, including a hybrid form of harmonization, as well as methods for consolidation and centralization.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141671228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.
{"title":"An analytical survey of cyber‐physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions","authors":"Qawsar Gulzar, Khurram Mustafa","doi":"10.1002/spy2.440","DOIUrl":"https://doi.org/10.1002/spy2.440","url":null,"abstract":"Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.
{"title":"Enhancing agricultural wireless sensor network security through integrated machine learning approaches","authors":"Ishu Sharma, Aditya Bhardwaj, Keshav Kaushik","doi":"10.1002/spy2.437","DOIUrl":"https://doi.org/10.1002/spy2.437","url":null,"abstract":"Wireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141687499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jan Bieniek, Mohamed Rahouti, Kaiqi Xiong, Gabriel Ferreira Araujo
The utilization of Internet of Things (IoT)‐based networks in healthcare systems has witnessed a notable increase, particularly in services like remote patient monitoring. However, specific vulnerabilities have become apparent as more individuals connect to these networks. One pressing concern revolves around safeguarding the privacy of users' confidential information. Given the extensive reliance on sensitive data in such services, apprehensions arise regarding the security of this information within the system. Moreover, the substantial volume of real‐time data transmission poses scalability challenges for the network. This work introduces SecureCare, a novel solution for enhancing wearable IoT healthcare by proposing a blockchain‐empowered Wearable Body Area Network (WBAN) framework. Our aim to employ blockchain technology stems from its robust security capabilities, thanks to its tamperproof and decentralized structure that effectively safeguards network data. Finally, SecureCare was evaluated on a public blockchain network, where it demonstrated improvements in efficiency and reliability. This validation confirms its potential as a robust solution for enhancing security in wearable IoT healthcare systems.
{"title":"SecureCare: A blockchain‐assisted wearable body area network for secure and scalable IoT healthcare services","authors":"Jan Bieniek, Mohamed Rahouti, Kaiqi Xiong, Gabriel Ferreira Araujo","doi":"10.1002/spy2.431","DOIUrl":"https://doi.org/10.1002/spy2.431","url":null,"abstract":"The utilization of Internet of Things (IoT)‐based networks in healthcare systems has witnessed a notable increase, particularly in services like remote patient monitoring. However, specific vulnerabilities have become apparent as more individuals connect to these networks. One pressing concern revolves around safeguarding the privacy of users' confidential information. Given the extensive reliance on sensitive data in such services, apprehensions arise regarding the security of this information within the system. Moreover, the substantial volume of real‐time data transmission poses scalability challenges for the network. This work introduces SecureCare, a novel solution for enhancing wearable IoT healthcare by proposing a blockchain‐empowered Wearable Body Area Network (WBAN) framework. Our aim to employ blockchain technology stems from its robust security capabilities, thanks to its tamperproof and decentralized structure that effectively safeguards network data. Finally, SecureCare was evaluated on a public blockchain network, where it demonstrated improvements in efficiency and reliability. This validation confirms its potential as a robust solution for enhancing security in wearable IoT healthcare systems.","PeriodicalId":29939,"journal":{"name":"Security and Privacy","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141367293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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