Cryptanalysis of a Markov Chain Based User Authentication Scheme

Session key agreement protocol using smart card is extremely popular in client-server environment for secure communication. Remote user authentication protocol plays a crucial role in our daily life such as e-banking, bill-pay, online games, e-recharge, wireless sensor network, medical system, ubiquitous devices etc. Recently, Djellali et al. proposed a session key agreement protocol using smart card for ubiquitous devices. The main focus of this paper is to analyze security pitfalls of smart card and password based user authentication scheme. We have carefully reviewed Djellali et al.\u27s scheme and found that the same scheme suffers from several security weaknesses such as off-line password guessing attack, privileged insider attack. Moreover, we demonstrated that the Djellali et al.\u27s scheme does not provide proper security protection on the secret key of the server and presents inefficient password change phase

Robust Smart Card based Password Authentication Scheme against Smart Card Security Breach

As the most prevailing two-factor authentication mechanism, smart card based password authentication has been a subject of intensive research in the past decade and hundreds of this type of schemes have been proposed. However, most of them were found severely flawed, especially prone to the smart card loss problem, shortly after they were first put forward, no matter the security is heuristically analyzed or formally proved. In SEC\u2712, Wang pointed out that, the main cause of this issue is attributed to the lack of an appropriate security model to fully identify the practical threats. To address the issue, Wang presented three kinds of security models, namely Type I, II and III, and further proposed four concrete schemes, only two of which, i.e. PSCAV and PSCAb, are claimed to be secure under the harshest model, i.e. Type III security model. However, in this paper, we demonstrate that PSCAV still cannot achieve the claimed security goals and is vulnerable to an offline password guessing attack and other attacks in the Type III security mode, while PSCAb has several practical pitfalls. As our main contribution, a robust scheme is presented to cope with the aforementioned defects and it is proven to be secure in the random oracle model. Moreover, the analysis demonstrates that our scheme meets all the proposed criteria and eliminates several hard security threats that are difficult to be tackled at the same time in previous scholarship

Big Data Security (Volume 3)

After a short description of the key concepts of big data the book explores on the secrecy and security threats posed especially by cloud based data storage. It delivers conceptual frameworks and models along with case studies of recent technology

Impact of denial of service solutions on network quality of service

The Internet has become a universal communication network tool. It has evolved from a platform that supports best-effort traffic to one that now carries different traffic types including those involving continuous media with quality of service (QoS) requirements. As more services are delivered over the Internet, we face increasing risk to their availability given that malicious attacks on those Internet services continue to increase. Several networks have witnessed denial of service (DoS) and distributed denial of service (DDoS) attacks over the past few years which have disrupted QoS of network services, thereby violating the Service Level Agreement (SLA) between the client and the Internet Service Provider (ISP). Hence DoS or DDoS attacks are major threats to network QoS. In this paper we survey techniques and solutions that have been deployed to thwart DoS and DDoS attacks and we evaluate them in terms of their impact on network QoS for Internet services. We also present vulnerabilities that can be exploited for QoS protocols and also affect QoS if exploited. In addition, we also highlight challenges that still need to be addressed to achieve end-to-end QoS with recently proposed DoS/DDoS solutions

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

XSACd—Cross-domain resource sharing & access control for smart environments

Computing devices permeate working and living environments, affecting all aspects of modern everyday lives; a trend which is expected to intensify in the coming years. In the residential setting, the enhanced features and services provided by said computing devices constitute what is typically referred to as a “smart home”. However, the direct interaction smart devices often have with the physical world, along with the processing, storage and communication of data pertaining to users’ lives, i.e. private sensitive in nature, bring security concerns into the limelight. The resource-constraints of the platforms being integrated into a smart home environment, and their heterogeneity in hardware, network and overlaying technologies, only exacerbate the above issues. This paper presents XSACd, a cross-domain resource sharing & access control framework for smart environments, combining the well-studied fine-grained access control provided by the eXtensible Access Control Markup Language (XACML) with the benefits of Service Oriented Architectures, through the use of the Devices Profile for Web Services (DPWS). Based on standardized technologies, it enables seamless interactions and fine-grained policy-based management of heterogeneous smart devices, including support for communication between distributed networks, via the associated MQ Telemetry Transport protocol (MQTT)–based proxies. The framework is implemented in full, and its performance is evaluated on a test bed featuring relatively resource-constrained smart platforms and embedded devices, verifying the feasibility of the proposed approac

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Security Risk Management for the Internet of Things

In recent years, the rising complexity of Internet of Things (IoT) systems has increased their potential vulnerabilities and introduced new cybersecurity challenges. In this context, state of the art methods and technologies for security risk assessment have prominent limitations when it comes to large scale, cyber-physical and interconnected IoT systems. Risk assessments for modern IoT systems must be frequent, dynamic and driven by knowledge about both cyber and physical assets. Furthermore, they should be more proactive, more automated, and able to leverage information shared across IoT value chains. This book introduces a set of novel risk assessment techniques and their role in the IoT Security risk management process. Specifically, it presents architectures and platforms for end-to-end security, including their implementation based on the edge/fog computing paradigm. It also highlights machine learning techniques that boost the automation and proactiveness of IoT security risk assessments. Furthermore, blockchain solutions for open and transparent sharing of IoT security information across the supply chain are introduced. Frameworks for privacy awareness, along with technical measures that enable privacy risk assessment and boost GDPR compliance are also presented. Likewise, the book illustrates novel solutions for security certification of IoT systems, along with techniques for IoT security interoperability. In the coming years, IoT security will be a challenging, yet very exciting journey for IoT stakeholders, including security experts, consultants, security research organizations and IoT solution providers. The book provides knowledge and insights about where we stand on this journey. It also attempts to develop a vision for the future and to help readers start their IoT Security efforts on the right foot

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin