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Supply-chains (SC) present performance bottlenecks that contribute to a high level of costs, infiltration of product quality, and impact productivity. Examples of such inhibitors include the bullwhip effect, new product lines, high inventory, and restrictive data flows. These bottlenecks can force manufacturers to source more raw materials and increase production significantly. Also, restrictive data flow in a complex global SC network generally slows down the movement of goods and services. The use of Distributed Ledger Technologies (DLT) in supply chain management (SCM) demonstrates the potentials to to reduce these bottlenecks through transparency, decentralization, and optimizations in data management. These technologies promise to enhance the trustworthiness of entities within the supply chain, ensure the accuracy of data-driven operations, and enable existing SCM processes to migrate from a linear to a fully circular economy. This paper presents a comprehensive review of 111 articles published in the public domain in the use and efficacy of DLT in SC. It acts as a roadmap for current and future researchers who focus on SC Security Management to better understand the integration of digital technologies such as DLT. We clustered these articles using standard descriptors linked to trustworthiness, namely, immutability, transparency, traceability, and integrity.

The initial access achieved by cyber adversaries conducting a systematic attack against a targeted network is unlikely to be an asset of interest. Therefore, it is necessary to use lateral movement techniques to expand access to different devices within the network to accomplish the strategic attack’s objectives. The pivot attack technique is widely used in this context; the attacker creates an indirect communication tunnel with the target and uses traffic forwarding methods to send and receive commands. Recognising and classifying this technique in large corporate networks is a complex task, due to the number of different events and traffic generated. In this paper, we present a pivot attack classification criteria based on perceived indicators of attack (IoA) to identify the level of connectivity achieved by the adversary. Additionally, an automatic pivot classifier algorithm is proposed to include a classification attribute to introduce a novel capability for the APIVADS pivot attack detection scheme. The new algorithm includes an attribute to differentiate between types of pivot attacks and contribute to the threat intelligence capabilities regarding the adversary modus operandi. To the best of our knowledge, this is the first academic peer-reviewed study providing a pivot attack classification criteria.

Industrial Control Systems (ICS) are integral parts of smart cities and critical to modern societies. Despite indisputable opportunities introduced by disruptor technologies, they proliferate the cybersecurity threat landscape, which is increasingly more hostile. The quantum of sensors utilised by ICS aided by Artificial Intelligence (AI) enables data collection capabilities to facilitate automation, process streamlining and cost reduction. However, apart from operational use, the sensors generated data combined with AI can be innovatively utilised to model anomalous behaviour as part of layered security to increase resilience to cyber-attacks. We introduce a framework to profile anomalous behaviour in ICS and derive a cyber-risk score. A novel super learner ensemble for one-class classification is developed, using overlapping rolling windows with stratified, k-fold, n-repeat cross-validation applied to each base-learner followed by majority voting to derive the best learner. Our approach is demonstrated on a liquid distribution sensor dataset. The experimental results reveal that the proposed technique achieves an overall F1-score of 99.13%, an anomalous recall score of 99% detecting anomalies lasting only 17 seconds. The key strength of the framework is the low computational complexity and error rate. The framework is modular, generic, applicable to other ICS and transferable to other smart city sectors.

The MedSecurance project focus on identifying new challenges in cyber security with focus on hardware and software medical devices in the context of emerging healthcare architectures. In addition, the project will review best practice and identify gaps in the guidance, particularly the guidance stipulated by the medical device regulation and directives. Finally, the project will develop comprehensive methodology and tooling for the engineering of trustworthy networks of inter-operating medical devices, that shall have security-for-safety by design, with a strategy for device certification and certifiable dynamic network composition, ensuring that patient safety is safeguarded from malicious cyber actors and technology “accidents”.

Advanced cyber attackers often “pivot” through several devices in such complex infrastructure to obfuscate their footprints and overcome connectivity restrictions. However, prior pivot attack detection strategies present concerning limitations. This paper addresses an improvement of cyber defence with APIVADS, a novel adaptive pivoting detection scheme based on traffic flows to determine cyber adversaries’ presence based on their pivoting behaviour in simple and complex interconnected networks. Additionally, APIVADS is agnostic regarding transport and application protocols. The scheme is optimized and tested to cover remotely connected locations beyond a corporate campus’s perimeters. The scheme considers a hybrid approach between decentralized host-based detection of pivot attacks and a centralized approach to aggregate the results to achieve scalability. Empirical results from our experiments show the proposed scheme is efficient and feasible. For example, a 98.54% detection accuracy near real-time is achievable by APIVADS differentiating ongoing pivot attacks from regular enterprise traffic as TLS, HTTPS, DNS and P2P over the internet.

Modern network infrastructures host converged applications that demand rapid elasticity of services, increased security, and ultra-fast reaction times. The Tactile Internet promises to facilitate the delivery of these services while enabling new economies of scale for high fidelity of machine-to-machine and human-to-machine interactions. Unavoidably, critical mission systems served by the Tactile Internet manifest high demands not only for high speed and reliable communications but equally, the ability to rapidly identify and mitigate threats and vulnerabilities. This article proposes a novel Multi-Agent Data Exfiltration Detector Architecture (MADEX), inspired by the mechanisms and features present in the human immune system. MADEX seeks to identify data exfiltration activities performed by evasive and stealthy malware that hides malicious traffic from an infected host in low-latency networks. Our approach uses cross-network traffic information collected by agents to effectively identify unknown illicit connections by an operating system subverted. MADEX does not require prior knowledge of the characteristics or behavior of the malicious code or a dedicated access to a knowledge repository. We tested the performance of MADEX in terms of its capacity to handle real-time data and the sensitivity of our algorithm’s classification when exposed to malicious traffic. Experimental evaluation results show that MADEX achieved 99.97% sensitivity, 98.78% accuracy, and an error rate of 1.21% when compared to its best rivals. We created a second version of MADEX, called MADEX level 2, that further improves its overall performance with a slight increase in computational complexity. We argue for the suitability of MADEX level 1 in non-critical environments, while MADEX level 2 can be used to avoid data exfiltration in critical mission systems. To the best of our knowledge, this is the first article in the literature that addresses the detection of rootkits real-time in an agnostic way using an artificial immune system approach while it satisfies strict latency requirements.

This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT.

Privacy and security have long been perceived as competing objectives, but they both exist in a state of tension within systems. Engineers may be tempted to consider security as essential, while privacy is a nice-to-have. Prof Carsten Maple, Dr Gregory Epiphaniou and Mirko Bottarelli of the University of Warwick and the Alan Turing Institute explain that, as applications and platforms gather ever more data, we need to move to a ‘privacy by design’ approach while implementing data protection mechanisms with existing security engineering methods. Privacy and security have long been perceived as competing objectives. For example, it is widely acknowledged that national security is often (and should often be) prioritised over any concerns for personal privacy. However, privacy and security are, more generally, in tension within cyber systems. This is most noticeably present in the design and operation of systems where information that can be used to determine and protect the security might have private and personal aspects.

A number of research in Information and Communication Technology (ICT) have shown that usability is an important goal for software-based interactive systems and products for a wider acceptance by their end-users. To evaluate the usability of a system under design, usability evaluation methods predominantly rely on subject matter experts or testers' assessments. However, integrating the Artificial Intelligence (AI) technology and existing usability evaluation methods' processes can aid to evolve more effective user-centered designs. In this paper, we conceptualize an AI-augmented usability evaluation framework (AIUEF) that aims at replacing `end-users' with `personas' to evaluate requirements involving human-computer interaction (HCI) for a given Software Requirements Specification (SRS). We present a blueprint of AIUEF that is the first step to furnishing a theoretical basis for automated usability evaluation of HCI requirements for a given SRS with an aim to improve the usability of a system under design.

The world is experiencing a rapid growth of smart cities accelerated by Industry 4.0, including the Internet of Things (IoT), and enhanced by the application of emerging innovative technologies which in turn create highly fragile and complex cyber–physical–natural ecosystems. This paper systematically identifies peer-reviewed literature and explicitly investigates empirical primary studies that address cyber resilience and digital forensic incident response (DFIR) aspects of cyber–physical systems (CPSs) in smart cities. Our findings show that CPSs addressing cyber resilience and support for modern DFIR are a recent paradigm. Most of the primary studies are focused on a subset of the incident response process, the “detection and analysis” phase whilst attempts to address other parts of the DFIR process remain limited. Further analysis shows that research focused on smart healthcare and smart citizen were addressed only by a small number of primary studies. Additionally, our findings identify a lack of available real CPS-generated datasets limiting the experiments to mostly testbed type environments or in some cases authors relied on simulation software. Therefore, contributing this systematic literature review (SLR), we used a search protocol providing an evidence-based summary of the key themes and main focus domains investigating cyber resilience and DFIR addressed by CPS frameworks and systems. This SLR also provides scientific evidence of the gaps in the literature for possible future directions for research within the CPS cybersecurity realm. In total, 600 papers were surveyed from which 52 primary studies were included and analysed.

The recent proliferation of sensors and actuators, which is related to the Internet of Things (IoT), provide smart living to the general public in many data-critical areas, from homes and healthcare to power grids and transport [...]

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.

The systematic integration of the Internet of Things (IoT) into the supply chain creates opportunities for automation in smart homes from concept to practice. Our research shows that residential burglary remains a problem. Despite the paradigm shift in ubiquitous computing, the maturity of the physical security controls integrating IoT in residential physical security measures such as burglar alarm systems within smart homes is weak. Sensors utilised by burglar alarm systems aided by IoT enable real-time reporting capabilities and facilitate process automation which can be innovatively employed to increase security resilience and improve response to a burglary in smart homes. We research key-related methods of proposed security of home alarm systems and introduce an IoT Burglar Intrusion Detection (I-BID) solution, a new privacy-preserving alarms system with multi-recipient real-time reporting of intrusion in smart homes. Our approach is demonstrated on a developed and tested prototype artefact. The experimental results reveal that the proposed technique reliably detects intrusion, achieves real-time reporting of a home intrusion to multiple recipients autonomously and simultaneously with a high degree of accuracy. The key strength of our technique is its scalability to a community-cluster model as a burglary security mechanism.

The rise of NewSpace provides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites, NewSpace provides the opportunity for delivering computing platforms in space. However, computational resources within space are usually expensive and satellites may not be able to compute all computational tasks locally. Computation Offloading (CO), a popular practice in Edge/Fog computing, could prove effective in saving energy and time in this resource-limited space ecosystem. However, CO alters the threat and risk profile of the system. In this paper, we analyse security issues in space systems and propose a security-aware algorithm for CO. Our method is based on the reinforcement learning technique, Deep Deterministic Policy Gradient (DDPG). We show, using Monte-Carlo simulations, that our algorithm is effective under a variety of environment and network conditions and provide novel insights into the challenge of optimised location of computation.

The rise of new digital economies and data-driven supply-chains seeks to revolutionalise the ways information is transferred, processed and analysed across different industry segments in the value-creation. This data-driven manufacturing revolution promises to increase productivity, democratise data sharing capabilities and foster industrial growth in scales never seen before. The traditional transactional models are to be re-visited, and distributed data storage architectures are to be re-designed to accommodate for optimised data flows across different organisation units. Data is increasingly becoming a strategic business resource that through innovation in existing sharing and processing approaches can decompose business bottlenecks in existing production lines and processes and disrupt traditional supply-chain models. This work seeks to articulate a state-of-the-art review of the application and impact of ML techniques and distributed Ledger technologies to further disrupt supply-chain capabilities with regards to data accuracy and completeness.

Precision healthcare is an emerging concept that will see technology-driven digital transformation of the health service. It enables customised patient outcomes via the development of novel, targeted medical approaches with a focus on intelligent, data-centric smart healthcare models. Currently, precision healthcare is seen as a challenging model to apply due to the complexity of the healthcare ecosystem, which is a multi-level and multifaceted environment with high real-time interactions among disciplines, practitioners, patients and discrete computer systems. Digital Twins (DT) pairs individual physical artefacts with digital models reflecting their status in real-time. Creating a live-model for healthcare services introduces new opportunities for patient care including better risk assessment and evaluation without disturbing daily activities. In this article, to address design and management in this complexity, we examine recent work in Digital Twins (DT) to investigate the goals of precision healthcare at a patient and healthcare system levels. We further discuss the role of DT to achieve precision healthcare, proposed frameworks, the value of active participation and continuous monitoring, and the cyber-security challenges and ethical implications for this emerging paradigm.

Internet of the Things (IoT) is being integrated into applications that are continuing to reshape many elements of our daily life. One of the major application areas is the Internet of Vehicles which can enhance existing capabilities, such as efficient vehicle route planning. Such systems usually rely on real-time traffic information that includes the temporal location feed of a vehicle. Despite offering clear advantages (such as overcoming congestion, saving fuel/energy/time, and reducing C O 2 emission), privacy concerns emerge due to the use of location data. Motivated by this, a privacy-preserving vehicular location (e.g. positioning) sharing scheme is developed for edge cloud-assisted vehicles. In addition, data utility bounds are theoretically analysed, and vehicle routing efficacy is empirically analysed to evaluate the impact of the proposed scheme. Rather than sharing perturbed location on two-dimensional space, we propose a graph-based differential privacy solution for sharing location. The novelty of this work relies on translating the vehicular geospatial data to the graph-structured data for its higher applicability on the road network, designing a real-time application, and empirical analysis of privacy-efficacy optimality.

Big data enables the optimization of complex supply chains through Machine Learning (ML)-based data analytics. However, data analytics comes with challenges such as the loss of control and privacy leading to increased risk of data breaches. Federated Learning (FL) is an approach in the ML arena that promises privacy-preserving and distributed model training. However, recent attacks on FL algorithms have raised concerns about the security of this approach. In this article, we advocate using Blockchain to mitigate attacks on FL algorithms operating in Internet of Things (IoT) systems. Integrating Blockchain and FL allows securing the trained models' integrity, thus preventing model poisoning attacks. This research presents a practical approach for the integration of Blockchain with FL to provide privacy-preserving and secure big data analytics services. To protect the security of user data and the trained models, we propose utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks. The proposed solution is evaluated via simulating attack modes in a quasi-simulated environment. 2021 This publication was made possible by NPRP grant NPRP 10-0125-170250 from the Qatar National Research Fund (a member of Qatar Foundation). Scopus

Context Online harassment and stalking have been identified with growing accordance as anti-social behaviours, potentially with extreme consequences including indirect or direct physical injury, emotional distress and/or financial loss. Objective As part of our ongoing work to research and establish better understanding of cyberstalking, this study aims to investigate the role of Police, Mobile Operators, Internet Service Providers (ISPs) and owners/administrators of online platforms (e.g. websites, chatrooms) in terms of intervention in response to offences. We ask to what different authorities do people report incidents of cyberstalking? Do these authorities provide satisfactory responses or interventions? And how can this be improved? Furthermore, we discuss the role of Corporate Social Responsibility (CSR) to encourage the implementation of cyberstalking-aware schemes by service providers to support victims. In addition, CSR can be used as a means to measure the effects of externality factor in dictating the relationship between the impact of a given individuals’ privacy loss and strategic decisions on investment to security controls in an organisational context. Method A mixed method design has been used in this study. Data collection took place by means of an online survey made available for three years to record both qualitative and quantitative data. Overall, 305 participants responded from which 274 identified themselves as victims of online harassment. Result Our results suggest that most offences were communicated through private channels such as emails and/or mobile texts/calls. A significant number of victims did not report this to their service provider because they did not know they could. While Police were recognised as the first-point-of-contact in such cases, 41.6% of our sample did not contact the Police due to reasons such as fear of escalation, guilt/sympathy and self-blaming. Experiences from those who have reported offences to service providers demonstrate that no or very little support was offered. Overall, the majority of participants shared the view that third-party intervention is required on their behalf in order to mitigate risks associated with cyberstalking. An independent specialist anti-stalking organisation was a popular choice to act on their behalf followed by the Police and network providers. Conclusion Incidents are taking place on channels owned and controlled by large, cross-border international companies providing mobile services, webmail and social networking. The lack of support offered to victims in many cases of cyberstalking can be identified as Corporate Social Irresponsibility (CSI). We anticipate that awareness should be raised as regarding service providers’ liability and social responsibility towards adopting better strategies.

Big Data has fast become one of the most adopted computer paradigms within computer science and is considered an equally challenging paradigm for forensics investigators. The Hadoop Distributed File System (HDFS) is one of the most favourable big data platforms within the market, providing an unparalleled service with regards to parallel processing and data analytics. However, HDFS is not without its risks, having been reportedly targeted by cyber criminals as a means of stealing and exfiltrating confidential data. Using HDFS as a case study, we aim to detect remnants of malicious users’ activities within the HDFS environment. Our examination involves a thorough analysis of different areas of the HDFS environment, including a range of log files and disk images. Our experimental environment was comprised of a total of four virtual machines, all running Ubuntu. This HDFS research provides a thorough understanding of the types of forensically relevant artefacts that are likely to be found during a forensic investigation.

The systematic integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into the supply chain to increase operational efficiency and quality has also introduced new complexities to the threat landscape. The myriad of sensors could increase data collection capabilities for businesses to facilitate process automation aided by Artificial Intelligence (AI) but without adopting an appropriate Security-by-Design framework, threat detection and response are destined to fail. The emerging concept of Smart Workplace incorporates many CPS (e.g. Robots and Drones) to execute tasks alongside Employees both of which can be exploited as Insider Threats. We introduce and discuss forensic-readiness, liability attribution and the ability to track moving Smart SPS Objects to support modern Digital Forensics and Incident Response (DFIR) within a defence-in-depth strategy. We present a framework to facilitate the tracking of object behaviour within Smart Controlled Business Environments (SCBE) to support resilience by enabling proactive insider threat detection. Several components of the framework were piloted in a company to discuss a real-life case study and demonstrate anomaly detection and the emerging of behavioural patterns according to objects' movement with relation to their job role, workspace position and nearest entry or exit. The empirical data was collected from a Bluetooth-based Proximity Monitoring Solution. Furthermore, a key strength of the framework is a federated Blockchain (BC) model to achieve forensic-readiness by establishing a digital Chain-of-Custody (CoC) and a collaborative environment for CPS to qualify as Digital Witnesses (DW) to support post-incident investigations.

Security in online gaming is a growing target for hackers due to the amount of money involved in online gaming. Components are used in the security measures implemented for these games, but no single security component is 100% effective. Our research aims to investigate methods of game security and the components used in them, as well as the hacks used to exploit and circumvent online gaming security mechanisms. As a result, our study arrives to some interesting points, and outlines a number of recommendations and potential research directions. This, in turn, can pave the way for facilitating and empowering future research in this domain to assist game engineers and testers in security management.

Blockchain technology can be incorporated into new systems to facilitate modern Digital Forensics and Incident Response (DFIR). For example, it is widely acknowledged that the Internet-of-Things (IoT) has introduced complexity to the cyberspace, however, incident responders should also realise the advantages presented by these new “Digital Witnesses” (DW) to support their investigation. Logs generated by IoT devices can help in the process of event reconstruction, but their integrity -and therefore admissibility- can be achieved only if a Chain-of-Custody (CoC) is maintained within the wider context of an on-going digital investigation. Likewise, the transition to electronic documentation improves data availability, legibility, the utility of notes, and therefore enhances the communication between stakeholders. However, without a proof of validity, these data could be falsified. For example, in an application area such as eHealth, there is a requirement to maintain various existing (and new) rules and regulations concerning authorship, auditing, and the integrity of medical records. Lacking data control could lead to system abuse, fraud and severe compromise of service quality. These concerns can be resolved by implementing an online CoC. In this paper, we discuss the value and means of utilising Blockchain in modern systems to support DFIR. we demonstrate the value of Blockchain to improve the implementation of Digital Forensic Models and discuss why law enforcement and incident responders need to understand Blockchain technology. Furthermore, the admissibility of a Digital Evidence to a Court of Law requires chronological documentation. Hence, we discuss how the CoC can be sustained based on a distributed ledger. Finally, we provide a practical scenario related to eHealth to demonstrate the value of this approach to introduce forensic readiness to computer systems and enable better Police interventions.

We present an algorithm that allows two users to establish a symmetric cryptographic key by incorporating the most important features of the wireless channel in vehicle-to-vehicle (V2V) communication. Non-reciprocity compensation is combined with turbo codes (TCs) for error reconciliation purposes. For fair comparisons, the indexing technique is applied in conjunction with the non-reciprocity compensation technique. A series of simulations are run to calculate key performance indicators (KPIs). High entropy values are obtained throughout all rounds of simulation during the key extraction process. Furthermore, simulation results indicate a significant improvement in bit mismatch rate (BMR) and key generation rate (KGR) when TCs are used. Increasing the number of iterations in the TC can significantly improve the Bit Error Rate (BER), thus generating more symmetric keys. The key generation rate was reported high ranging from 17 to 19 for the 256-bit symmetric keys per minute with TCs, while it is ranging from 2 to 5 when compared with a sample indexing technique published in the public domain. Finally, simulations proved also improvements for different key lengths as part of the error reconciliation process when TCs are used with an almost regular permutation (ARP) instead of a random permutation.

Distributed Ledger Technologies are promised to be genuinely disruptive in a variety of industries and sectors, from the food supply, pharmaceutical, real estate, financial services, academia and healthcare. Blockchain technologies vastly improve the flow of data within and among companies and people and transform the way that the Internet is used to exchange value. The use of distributed applications can revolutionise and simplify complex system tasks and create new substructures in a fully developing distributed economy as part of Industry 4.0. Blockchain technologies have the enormous potential to transform healthcare and existing security, privacy and interoperability issues allowing more flexibility and freedom to patients and their data. New economies of scale are emerging for health information exchange that makes the management of electronic records easier while eliminating fictions and costs associated with current intermediaries. This chapter seeks to explore the idiosyncrasies of Blockchain technology and its adaptation in healthcare with an emphasis to its core features and applications. We expand to the key risks and opportunities of Blockchain and briefly discuss this pilot studies in healthcare.

Cloud storage forensics is an active research area, and this is unsurprising due to the increasing popularity of cloud storage services (e.g., Dropbox, Google Drive, and iCloud). Existing research generally focuses on public cloud forensics (e.g., client device forensics), rather than private cloud forensics (e.g., both client and server forensics). In this paper, we aim to address the gap by proposing a framework for forensics investigations of private cloud storage services. The proposed framework elaborates on procedures and artefact categories integral to the collection, preservation, analysis, and presentation of key evidential data from both client and server environments. Using the proposed framework to guide the investigation of Seafile, a popular open-source private cloud storage service, we demonstrate the types of client and server side artefacts that can be forensically recovered.

Cloud computing is becoming increasingly popular mainly because it offers more affordable technology and software solutions to start-ups and small and medium enterprises (SMEs). Depending on the business requirements there are various Cloud solution providers and services, yet because of this it becomes increasingly difficult for a digital investigator to collect and analyse all the relevant data when there is a need. Due to the complexity and increasing amounts of data, forensic investigation of Cloud is turning into a very complex and laborious endeavour. Ceph is a filesystem that provides a very high availability and data self-healing features, which ensure that data is always accessible without getting damaged or lost. Because of such features, Ceph is becoming a favourite file system for many cloud service providers. Hence, understanding the remnants of malicious users activities is become a priority in Ceph file system. In this paper, we are presenting residual evidences of users’ activities on Ceph file system on Linux Ubuntu 12.4 operating system and discuss the forensics relevance and importance of detected evidences. This research follows a well-known cloud forensics framework in collection, preservation and analysis of CephFS remnants on both client and server sides.

The world of online advertising is directly dependent on data collection of the online browsing habits of individuals to enable effective advertisement targeting and retargeting. However, these data collection practices can cause leakage of private data belonging to website visitors (end-users) without their knowledge. The growing privacy concern of end-users is amplified by a lack of trust and understanding of what and how advertisement trackers are collecting and using their data. This paper presents an investigation to restore the trust or validate the concerns. We aim to facilitate the assessment of the actual end-user related data being collected by advertising platforms (APs) by means of a critical discussion but also the development of a new tool, AdPExT (Advertising Parameter Extraction Tool), which can be used to extract third-party parameter key-value pairs at an individual key-value level. Furthermore, we conduct a survey covering mostly United Kingdom-based frequent internet users to gather the perceived sensitivity sentiment for various representative tracking parameters. End-users have a definite concern with regards to advertisement tracking of sensitive data by global dominating platforms such as Facebook and Google.

It is well known that government agencies have had the capability to eavesdrop on public switched telephone networks for many decades. 1 However, with the growing use of the Internet and the increasing technical capabilities of agencies to conduct mass surveillance, an individual's right to privacy is of far greater concern in recent years. The ethical issues surrounding privacy, anonymity and mass-surveillance are complicated, with compelling arguments for and against, due in part to the fact that privacy and anonymity are desired by criminals and terrorists, not just individuals who care about their privacy. 2 , 3 , 4

With the virtual world having become part of the social lives of adults and minors alike, new attack vectors emerged to increase the severity of human-related attacks to a level the community has not experienced before. This article investigates this phenomenon and shares an outline of how technology could develop further to counteract and mitigate the damage caused by online perpetrators. Cyber-stalking and online bullying can have devastating effects on individuals, but it's affecting the workplace too. In response, relevant technology could be used to counteract and mitigate the damage caused by online perpetrators. Haider M al-Khateeb and Gregory Epiphaniou of the University of Bedfordshire approach this issue using an incident response methodology and discuss the role of machine learning to identify and classify such attacks. They also examine how digital forensic investigations can be carried out in order to analyse the nature of the offence and preserve evidence.

We present an algorithm that allows two users to establish a symmetric cryptographic key by incorporating the most important features of the wireless channel in vehicle-to-vehicle (V2V) communication. The proposed model includes surrounding scatterers' mobility by considering other vehicles; it also includes three-dimensional (3D) multipath propagation. These temporal variability attributes are incorporated into the key generation process where non-reciprocity compensation is combined with turbo codes (TCs). For fair comparisons, the indexing technique is applied in conjunction with the non-reciprocity compensation technique. A series of simulations are run to calculate key performance indicators (KPIs). The entropy values were high throughout all rounds of simulation and estimated around 0.94 to 0.99 bits per sample. Furthermore, simulation results reveal a decrease in bit mismatch rate (BMR) and an increase key generation rate (KGR) when TCs are used. The estimated BMR is nearly the same for different key lengths, and it is estimated to only 0.02 with TCs, compared to 0.22 obtained with the indexing technique. Finally, the key generation rate was also reported high ranging from 35 to 39 for the 128-bit symmetric keys per minute with TCs, while it is ranging from 3 to 7 when compared with a sample indexing technique published in the public domain.

The rise of interconnected “intelligent” objects that move their capabilities from sensing and data processing to decision making will be a disruptive phenomenon that further widens the gaps between legal, regulatory and technological approaches. This research sets out to establish a guided road map through the maze of regulation by incorporating the fragmented governance efforts into a single focus where security and privacy gaps unique to machine-to-machine communication (M2M) are identified against key performance metrics. We use privacy, ethics, trust, legality, data sharing, operational integration and device and communication protocols as our key performance metrics to highlight areas of significant overlap and gaps in a comprehensive list of standards to assist policymakers and researchers in the field. Results also indicate that policy concerns and diffused responses from existing standards raise unacceptable risks for the cyber and physical spheres in the IoT preventing their integration with existing hierarchical security architectures and reducing the opportunities for mass-market economies of scale.