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Purpose: Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework. Design/methodology/approach: This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance. Findings: The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance. Originality/value: The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.

In the scientific world, models are useful abstractions and sets of rules that can be used to predict hypothetical events. One's first exposure to models is likely in primary science education - models of gravity or chemical interaction. As computational power increases, the availability of models for different purposes continues to grow. For instance, models of climate and weather are more accurate than before. This growth has also grown to encompass the field of medicine. There are now an increasing number of computational models that describe the physiology of a given patient with great accuracy and interaction. The development of these models is a boon to the medical training field and is typically the reason for most of the development of these models. These models could be used to design a customised, multivariate biological threat. This threat would be entirely hypothetical if the medical training models were a singular development. However, the independent rise of low-cost, semi-autonomous biological manipulators gives this hypothetical threat very practicable teeth to combine high-resolution computational data with designer bioagents to deliver the optimal biological agent for a threat. This paper attempts to spur conversation based on the exploration of this distinct possibility and scenarios derived from the ideas proposed within the models described below.

Graphical Abstract: Schematic illustration of PGCMNPs synthesis, characterization and its anticancer potential: PGCMNPs were synthesized by co-precipitation method. The successful synthesis of PGCMNPs was confirmed by physical and chemical characterizations. PGCMNPs were biocompatible and exhibited no toxicity at tested parameters. PGCMNPs demonstrated higher uptake in HepG2 cells. The anticancer therapeutic potential of PGCMNPs in HCC mouse model, was evident from improved tumor statistics, increased low conductivity tumors and increased apoptosis mediated cell death.

Internet of Space Things (IoST), also known as CubeSats, elaborates the uses and functionalities of traditional Internet of Things (IoT) by not only providing a constantly available satellite back-haul network, but also by providing real-time satellite-captured data. IoST can be applied for various applications, like weather forecasting, navigation, satellite phone, satellite TV, satellite Internet, radio, military, and many more. It requires support of mechanisms of Intelligent Transportation System (ITS). In an IoST communication environment, the communication among various users, satellite access points, ground stations, and smart IoT devices occur through insecure channels, i.e., Internet. Due the transmission of data over an insecure channel, various potential attacks are possible. Due to the existence of various attacks, the important data of IoST may be altered or revealed. To mitigate these issues, an authentication and key management framework for secure and intelligent transportation of IoST has been proposed (in short, SAKM-IoST). Through the proposed SAKM-IoST, a legitimate user can access the data of ground station in a secure way. The security analysis reveals that SAKM-IoST is resilient against a variety of potential threats and attacks. Additionally, SAKM-IoST’s performance is compared with other approaches that are similar in nature. It has been noted that SAKM-IoST offers robust security, in addition to extra functional characteristics. Therefore, SAKM-IoST seems to be more suitable for its deployment in the critical applications of the IoST as compared to other competing approaches.

Recent advances in Internet technology and IoT devices have facilitated researchers to foster a wide range of Intelligent Transportation Systems (ITS) that improve the quality of automated transportation by addressing real-time safety and traffic management issues. The participating ITS agents, such as smart cars and roadside equipment, are required to communicate urgently through an open (unsecured) wireless channel in an unattended setting. To address the security issues, several vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) authentication and access control protocols have been proposed in recent times. However, fast-moving vehicles need to set up frequent authentication with different roadside units, which induces high computation and communication overheads. Consequently, it becomes a bottleneck for the resource-limited vehicle onboard unit devices. As the blockchain supports decentralized storage with data integrity and transparency, in this article, we design a secure and lightweight Internet of Vehicles (IoV)-enabled blockchain-based access control protocol with a handover authentication facility (we call it BACHP-IoV, in short). The handover authentication mechanism exploits no computation-costly cryptographic primitives. Once the transactions or messages have been securely gathered by a roadside unit (RSU), $RSU_{j}$, residing in a group of vehicles $Vh_{i}$, will form a partial block, which is later forwarded to a cloud server node in the Peer-to-Peer (P2P) cloud servers blockchain network for converting it into a full block. Next, the full blocks are mined using a voting-based consensus algorithm. In addition, the in-charge trusted authority $\mathcal {TA}$ uploads information about the registered vehicles, such as randomized masked passwords and random secrets, to the blockchain. Thus, an $RSU_{j}$ can check the authenticity of a particular vehicle as well. We prove the security strength of the proposed BACHP-IoV by using the well-known Real-or-Random (ROR)-based random oracle model, the ProVerif 2.03 simulation tool, and informal security analysis. We have implemented the proposed BACHP-IoV through network simulator 3 (NS-3) and blockchain, and the simulation results demonstrate that BACHP-IoV is practical in a real-life scenario. A detailed comparative analysis also shows that BACHP-IoV provides significantly better security and efficiency than the existing competing schemes.

This paper studies artificial intelligence (AI) aided communication and computing resource allocation in a vehicular network that supports blockchain-enabled video streaming. Our study aims to improve the operating efficiency and to maximize the transcoding rewards for blockchain based vehicular networks. Our resource allocation policy considers the vehicular mobility, which is modelled with a highly-realistic Semi-Markov renewal process, as well as the real-time video service delay constraints. We propose a multi-timescale actor-critic-reinforcement learning framework to tackle these grand challenges. We also develop a prediction model for the vehicular mobility by using analysis and classical machine learning, which alleviates the heavy signaling and computation overheads due to the vehicular movement. A mobility-aware reward estimation for the large timescale model is then proposed to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the developed theoretical findings in this paper and the significant performance gains due to our proposed multi-timescale framework.

This study compared the outcomes of immediate PPV and tap and inject in eyes with PCE and vision ≥HM. PPV resulted in earlier recovery, lesser interventions, and greater change in visual acuity than T&I at 6 weeks postintervention.

Ransomware is a type of malicious program or software that encrypts the contents on a hard disc and prevents the users from accessing them unless they pay an amount (called a ransom). Most of the organizations, such as financial institutes and healthcare sectors (i.e., smart healthcare) are targeted by ransomware attacks. Ransomware assaults are among the most frightening types of cyber-attacks, and they are not confined to a specific sector or the countries. Blockchain is a tamper-proof technology, which is more secure, robust and decentralized in nature. Features of blockchain can add more security for detection and mitigation of ransomware more effectively. In this paper, we propose a new blockchain-enabled security framework to detect and defend the ransomware attacks for smart healthcare (in short, BSFR-SH). The conducted security analysis proves the security of the proposed BSFR-SH against the ransomware attacks. The performance of BSFR-SH is significantly better than the other similar existing mechanisms as it achieves better accuracy and F1-score than other compared mechanisms. Furthermore, the practical demonstration of BSFR-SH is provided to estimate the impact on important performance parameters.

Study design: Protocol for a multi-centre randomised controlled trial (the SCI-MT trial). Objectives: To determine whether 10 weeks of intensive motor training enhances neurological recovery in people with recent spinal cord injury (SCI). Setting: Fifteen spinal injury units in Australia, Scotland, England, Italy, Netherlands, Norway, and Belgium. Methods: A pragmatic randomised controlled trial will be undertaken. Two hundred and twenty people with recent SCI (onset in the preceding 10 weeks, American Spinal Injuries Association Impairment Scale (AIS) A lesion with motor function more than three levels below the motor level on one or both sides, or an AIS C or D lesion) will be randomised to receive either usual care plus intensive motor training (12 h of motor training per week for 10 weeks) or usual care alone. The primary outcome is neurological recovery at 10 weeks, measured with the Total Motor Score from the International Standards for Neurological Classification of SCI. Secondary outcomes include global measures of motor function, ability to walk, quality of life, participants’ perceptions about ability to perform self-selected goals, length of hospital stay and participants’ impressions of therapeutic benefit at 10 weeks and 6 months. A cost-effectiveness study and process evaluation will be run alongside the trial. The first participant was randomised in June 2021 and the trial is due for completion in 2025. Conclusions: The findings of the SCI-MT Trial will guide recommendations about the type and dose of inpatient therapy that optimises neurological recovery in people with SCI. Trial registration: ACTRN12621000091808 (1.2.2021).

Many classical cryptographic techniques are breakable due to the quantum computing security threats, and it leads to design public key cryptography based on post-quantum cryptography primitives and security protocols. In recent years, Lattice-Based Cryptography (LBC) becomes a prominent post-quantum cryptographic primitive that can be applied in both traditional and emerging security domains, including encryption, key agreement, digital signature and homomorphic encryption. In this article, we first provide a LBC-based security framework using aggregate signature that can be applied in ambient intelligence-assisted blockchain-based Internet of Things (IoT) applications, called LAS-AIBIoT. In LAS-AIBIoT, the wearable/medical devices deployed in the patients' body securely send the sensing secret data (encrypted messages) with their respective lattice-based signatures to their nearby controller nodes (CN), where the CNs forward these secret messages to the attached aggregator node (Aggr). Each Aggr verifies the individual signature of the devices and constructs the aggregate signature on the received secret messages and signatures, and sends the aggregated secret messages with their aggregate signature to the cloud server(s) for block construction in the blockchain center. Through the consensus protocol, the block is then mined and added into the blokchain. We show the robustness of LAS-AIBIoT against various potential attacks including quantum computing security threats through the threat model discussed in this article. Finally, through the blockchain-based simulation study we show that LAS-AIBIoT can be applied for real-time ambient intelligence-assisted IoT applications.

Authentication for controller area network (CAN) buses in an intra-vehicular network involving electronic control units (ECUs) is a challenging factor. The Society of Automotive Engineers standard (SAE J1939) incorporating the ISO 11898-1 specification for the data link and physical layers of the standard CAN and CAN-flexible data rate (CAN-FD) handles communication among ECUs. The SAE J1939 is vulnerable to replay, masquerading and machine-in-the-middle (MITM) attacks. To prevent such attacks, there exist protocol suites for resource-constrained and resource-unconstrained nodes proposed in the literature which are not formally analysed. We formally analyse one of the comprehensive protocol suites using the state-of-the-art Tamarin automated validation tool. To mitigate the identified attacks, we propose two new authentication protocols. At first, we propose one pass authentication protocol for computationally restricted nodes. For nodes that are not restricted computationally, we present a certificateless signature-based authentication protocol. Additionally, we present a new certificateless key insulated manageable signature (CL-KIMS) scheme for signature-based authentication protocol. CL-KIMS ensures key insulation and random access key update properties, and assures self-healing property. The security of the proposed protocol suite and signature scheme is formally analysed using the random oracle model (ROM). Especially, CL-KIMS scheme is provably secure in the ROM against Type-I and Type-II adversaries. A detailed performance comparison and simulation study show that the proposed protocol suite has lesser communication overhead and ensures robust security as compared to the existing protocol suites.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

Customer credential verification is an ongoing activity at financial institutions. Know Your Customer is one such periodic verification activity. Often, organizations store the collected customer credentials on centralized storage platforms (e.g., cloud storage and central servers) which could result in major privacy breaches. In addition, when a customer has accounts at multiple institutions, this process is repeated at each of the institutions, resulting in wasted resources and inconvenience to the customer. In this paper, we describe Casper, a blockchain and self-sovereign identity-based digital identity platform, to address these issues. Unlike traditional identity systems, here the actual identity credentials of customers are stored on their own mobile wallet applications. The system only stores the proofs of the credentials on its blockchain-based decentralized storage system. Casper employs Zero-Knowledge Proof mechanisms to verify the identity information from the credential proofs. As a proof of concept, we have employed Casper in a banking environment. Preliminary evaluation studies show the system to be scalable and being capable of yielding high transaction throughput.

Agricultural production is something on which the economy significantly relies. Leaf diseases in agriculture are the key issue for every nation, as the food demand is expanding at a rapid speed due to a rise in population. Skin disorders are usually seen in animals and humans, it is a particular sort of illness caused by germs or infection. Early and accurate identification and diagnosis of leaf and skin diseases are vital to keeping them from spreading. Image processing techniques can be used for disease detection which involves mathematical equations and mathematical transformations. For humans eyes image is a mixture of RGB colour, because of these colours we can extract some of the features from the image, but modern computer stores image in a mathematical format which means computer sees the image as numbers, hence after evaluating the image as a number arrays or matrix we will perform various transforms on them, these transforms will extract specific details from the picture, before transforming the image must go under various operation like feature adjustment which is also carried out mathematically. The project is implemented using K-Means Clustering and Support Vector Machine Algorithm in MATLAB through which we can detect and distinguish different types of leaf and skin diseases.

5G network sliced applications enable IoT networks to connect billions of heterogeneous objects, providing high-quality service, network capacity, and enhanced throughput. The blockchain systems which attempt to facilitate 5G network-sliced application requirements present several challenges, such as lack of decentralized governance, reduced transaction throughput/scalability, inability to run on resource-constrained devices, lack of support for real-time/concurrent transaction handling, and non-deterministic Byzantine Fault Tolerance (BFT) consensus models. In this paper, we propose a highly scalable, lightweight blockchain system, “Librum,” for 5G-based network sliced applications. Librum’s lightweight design enables it to run in edge networks, and we have designed low-cost hardware nodes to run the Librum blockchain edge network. The embedded hardware devices contain wifi and cellular modules that allow the Librum blockchain nodes to be run on 5G edge networks. The Librum blockchain, stored on IPFS peer-to-peer decentralized storage, enables any Byzantine node to participate in the network. Librum’s Fungible (ERC20) and Non-Fungible Token (ERC721) smart contracts support concurrent transaction execution with a novel “Validate–Execute” blockchain architecture. We incorporated hierarchical consensus groups to run independent blockchain shards (local consensus groups) on different 5G network slices. The shards can run BFT or CFT (Crash Fault Tolerance) consensus models and reach global consensus via core-blockchain nodes in the network based on connectivity requirements. Core blockchain nodes can also run with BFT (e.g., Tendermint) or CFT (e.g., Proof-of-Authority/Kafka) consensus models, eliminating message-passing overhead and achieving BFT with a deterministic consensus model in Geo-distributed blockchain networks. Dynamic 5G network slice orchestration and data provenance of network slices are implemented with smart contracts. The proposed Librum blockchain is integrated with FreedomeFi and Magma 5G core-based 5G testbed environments.

Study design: Cross-sectional survey. Objectives: To identify common problems across key domains of functioning, health and wellbeing, as well as evaluate self-reported quality of life (QoL) by people with SCI, examining differences by age, gender, injury characteristics and level of mobility. Setting: Data from four state-wide SCI clinical services, one government insurance agency and three not-for-profit consumer organisations. Methods: Participants were 18 years or over with SCI and at least 12 months post-injury, recruited between Mar’18 and Jan’19. The Aus-InSCI questionnaire comprised 193 questions, including socio-demographics, SCI characteristics, body functions and structures, activities and participation, environmental and personal factors, and appraisal of health and well-being. General linear model was used to examine differences in functioning and QoL. Results: Participants (mean age 57 years, range 19–94 years) with tetraplegia and/or complete injuries had more health problems, activity/participation problems and environmental barriers. However, self-rated overall QoL did not differ for injury level or completeness. Participants with more recent injuries exhibited lower independence levels, more mental health problems and poorer satisfaction with self and their living conditions. Major activity/participation problems related to intimate relationships and accessing public transportation. Less than half of the working age population were engaged in paid work. The top two environmental barriers frequently related to accessing public places or homes and unfavourable climatic conditions. Conclusions: This large, comprehensive community survey draws a detailed picture of the lived experience of people with SCI in Australia, identifying priority needs, gaps in services and barriers to achieving a full and satisfying life.

There has been a long-standing interest in Raman spectroscopic investigation of optically trapped single functional cells. Optical trapping using a Gaussian beam has helped researchers for decades to investigate single cells suspended in a physiological medium. However, complete and sensitive probing of single cells demands further advancements in experimental methods. Herein, we propose optical trapping and simultaneous micro-Raman spectroscopy of red blood cells (RBCs) in an unconventional face-on orientation using an optical vortex beam. Using this novel method, we are successful in comparing the conformational state of hemoglobin (Hb) molecules near the RBC membrane and inside the bulk of the cell. This method enabled us to successfully probe the oxy/deoxy ratio of Hb molecules near the RBC membrane and inside the bulk of the cell. Because of the face-on orientation, the Raman spectra of RBCs acquired using a vortex beam have a significant contribution from membrane components compared to that recorded using the Gaussian beam.

The rapid progress in communication technologies and the widespread adoption of connected devices have given rise to various information-centric Internet-of-Things (IoT) systems, typically necessitating timely updates of information. Green IoT (G-IoT) strives to enhance the environment by reducing the power consumption of billions of devices engaged in extensive data exchange, addressing the substantial energy demand in the process. The Age of Correlated Information (AoCI) measures the freshness of information shared among two or more devices that contribute to the same decision-making process. Optimizing AoCI using Deep Reinforcement Learning (DRL) in IoT reduces energy consumption, optimizes resource utilization, and promotes environmentally conscious communication, contributing to the development of a sustainable G-IoT system. This paper focuses on scheduling the transmission of status update packets among interconnected G-IoT devices to minimize the application-specific long-term average AoCI. The problem is modeled as an NP-hard episodic Markov Decision Process (MDP), highlighting its computational complexity. To handle correlations and the curse of dimensionality, a multi-agent deep reinforcement learning algorithm, specifically the Multi-Agent Deep Deterministic Policy Grading (MADDPG) algorithm, is developed. The training progress displays episode rewards, with an environment designed to penalize multiple agents transmitting simultaneously or none at all, promoting cooperative behavior and minimizing the average age of correlated information. We provide comprehensive simulation results, including reward convergence, the learning process of actors and critics, and the resulting average AoCI with the number of episodes, demonstrating the effectiveness of the MADDPG algorithm.

Distributed ledger technology (DLT) provides decentralised services by removing the need of trust among distributed nodes in the distributed system. Transactions across the network are visible to all participants. However, some transactions may contain sensitive information such as business contracts or even personal health records. To protect user privacy, the architecture of distributed multi-channel ledger with membership service as a critical component can be adopted. The proposed multi-channel membership service architecture combines two promising technologies, distributed ledger and Intel Software Guard Extensions (SGX). With SGX remote attestation and isolated execution, each node is enrolled as a trusted entity to the channels, which separate different applications and provide better flexibility. We propose security properties for membership service in distributed ledger and illustrate how SGX help to achieve these properties in each phase. Security analysis and evaluation show that the proposed architecture could enhance the privacy preservation and capabilities against attacks.

Ubiquitous adoption of cloud computing and virtualization technology has necessitated the need for strong security mechanisms. Multiple entities are involved in creating, exchanging, and altering data objects in the cloud environment, making it challenging to track malicious activities and security violations. To address these issues, there is a need for a data provenance framework, with which each data object in the federated cloud environment can be tracked and recorded. Although log-based provenance provides the ability to track operations conducted on digital assets, the provenance data are not transparent and immutable. Blockchain technology offers a promising mechanism for building a tamper-proof information system backed by strong cryptographic primitives. In this article, we propose BlockCloud, a blockchain-empowered data provenance architecture for the cloud computing platform. In addition, we present a proof-of-stake (PoS) consensus mechanism for BlockCloud to alleviate the overhead of computational requirements that the traditional proof-of-work (PoW) consensus needs. Finally, we discuss several research challenges and vulnerabilities that need to be addressed to realize BlockCloud.

Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate and feed different information retrieval requests using tokens specifically generated for each type of request. Providing both traceability of data operations, and unlinkability of end user activities, a robust blockchain-based CPS is prototyped. Evaluation indicates the architecture is capable of assured data provenance validation and user privacy preservation at a low overhead.

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. In this context, this review focuses on the implementation of federated learning (FL) and deep learning (DL) algorithms for IoT security. Unlike conventional ML techniques, FL models can maintain the privacy of data while sharing information with other systems. The study suggests that FL can overcome the drawbacks of conventional ML techniques in terms of maintaining the privacy of data while sharing information with other systems. The study discusses different models, overview, comparisons, and summarization of FL and DL-based techniques for IoT security.

In this paper, we investigate a two-stage Stackelberg game for dynamic spectrum access (DSA) in cognitive radio networks using a leader [spectrum provider (SP)] subgame and a follower [secondary user (SU)] subgame. Our research distinguishes itself in a number of ways. First, a two-stage Stackelberg game has been proposed to provide a unique optimal solution that facilitates a tradeoff between quality of service (QoS) of unlicensed SUs and revenue of SPs. Second, comprehensive budget and QoS constraints imposed by the signal-to-interference-plus-noise ratio are defined to fully take into account QoS requirements. Third, a necessary condition and closed form for the existence of the Stackelberg equilibrium are presented for multiradio multichannel DSA. Analytical and simulation studies are carried out to assess the performance of the proposed game, and the numerical results verify that the proposed approach reaches the unique Stackelberg equilibrium.

Although cannabis use after a first episode of psychosis has been associated with relapse, little is known about the determinants of this most preventable risk factor for relapse of psychosis. Here we aimed to study whether the effects on outcome vary depending on the type of cannabis consumed and usage pattern.

Opportunistic Spectrum Access (OSA) in a Cognitive Radio Network (CRN) is regarded as emerging technology for utilizing the scarce Radio Frequency (RF) spectrum by allowing unlicensed secondary users (SUs) to access licensed spectrum without creating harmful interference to primary users (PUs). The SUs are considerably constrained by their limited power, memory and computational capacity when they have to make decision about spectrum sensing for wide band regime and OSA. The SUs in CRN have the potential to mitigate these constraints by leveraging the vast storage and computational capacity of cloud computing approaches. In this paper, the authors investigate a game theoretic approach for opportunistic spectrum access in cognitive networks. The proposed algorithm leverages the geo-locations of both SUs and spectrum opportunities to facilitate OSA to SUs. The active SUs using game theory adapt their transmit powers in a distributed manner based on the estimated average packet-error rate while satisfying the Quality-of-Service (QoS) in terms of signal-to-interference-noise-ratio (SINR). Furthermore, to control greedy SUs in distributed power control game, the authors introduce a manager/leader through a Stackelberg power adaptation game. The performance of the proposed approaches is investigated using numerical results obtained from simulations.

Cloud subscribers would like to verify the location of outsourced data in the cloud data centres to ensure that the availability of data satisfies the service level agreement (SLA). Cloud users may not have access to their outsourced data in the event of operational failures in data centres or occurrence of natural disasters and/or power outages. Recently, IP geolocation techniques have been proposed to locate data files in cloud data centres. However, these techniques exploit relationships between internet delays and distance and are not extensible to incorporate different network measurements, which may be used along with internet delay to improve accuracy. Also, most of the existing techniques have only been validated with one cloud provider (Amazon Web Services). In this paper, we propose an enhanced learning classier IP geolocation algorithm, which incorporates multiple network measurements to improve the accuracy of geolocating data files in data centres in four commercial cloud providers. To demonstrate the accuracy of our approach, we evaluate the performance on Amazon Web Services, Microsoft Azure, Google App Engine, and Rackspace. Our experimental results demonstrate that our approach is geolocating data files accurately, more closely to the true location and also detecting violation of location restrictions.

Cloud computing allows users to remotely store their data into the cloud and provides on-demand access to applications and services. The security of the outsourced data in the cloud is dependent on the security of the cloud computing system and network. Though there have been numerous efforts on securing data on the cloud computing system, evaluation of security of cloud networks is still a very challenging task. Specifically, the audit of the cloud computing network will provide insights onto the level of protection provided within and outside data centres. This paper presents key technologies for cloud auditing to assess the security level within and outside cloud data centres: cloud data geolocation and cloud network risk assessment. We evaluate their performance on commercial cloud platforms and our experimental results demonstrate that cloud auditing technologies provide users the ability to assess the security of the cloud network.

With the rapid development of cognitive radios, spectrum efficiency in cognitive radio networks (CRN) has increased by secondary users (SU) accessing the licensed spectrum dynamically and opportunistically without creating harmful interference to primary users. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity. In this paper, we propose geolocation-aware radio resource management algorithm for CRN where distributed storage and computing resource in cloud computing platform and geolocation of secondary users are leveraged to store spectrum occupancy information of heterogeneous wireless networks and facilitates the access of spectrum opportunities for secondary users (SU). The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate efficient allocation of radio resources to SU. Furthermore, the secondary users who provide high benefit are admitted while satisfying the quality of service (QoS) requirement of secondary users in terms of data rate and service time. We also propose a scalable mapping method using storm, a real-time distributed processing model in cloud computing platform to dynamically partition the geographical area according to the SU density. Simulation results are presented to demonstrate the performance of the proposed geolocation-aware radio resource management algorithm.

Cognitive radio (CR) technology is regarded as a backbone for the future generation wireless systems. Using CR technology, secondary users (SUs), a.k.a. CR users, are allowed to access RF spectrum opportunistically provided that they are not causing harmful interference to primary users (PUs) and vacate the bands upon the arrival of licensed PUs. In this paper, we investigate waiting probability of SUs to get channel access based on PUs’ ON-OFF activities. Multi-user cognitive radio system is considered where SUs contend for spectrum access using time division multiple access over idle PU channels. Using queue dynamics as Poisson driven stochastic process, we characterise the waiting probability of secondary users. Generally speaking, in practical systems, secondary users of cognitive radio network would have no knowledge of activities of other users, thus the probability of being idle or contention probabilities of SUs’ in cognitive radio network have to be assigned according to the available local information. Our focus is on SUs’ waiting probability analysis, for which a systematic understanding is lacking. Simulation results show that the use of multiple channels and/or multiple slots leads to significant delay reduction and transmission fairness.

There has been a steady increase in the amount of molecular data generated by experiments and computational methods performed on biological networks. There is a growing need to obtain an insight into the organisation and structure of the massive and complex biological networks formed by the interacting molecules. To that end, this paper presents a newly developed plugin for integrated network visualisation and graph analysis within the Cytoscape framework. The plugin is capable of computing and visualising a comprehensive set of node and graph level statistics. The evaluation of the plugin on a range of biological networks and its memory performance is conducted. The plugin, proven to be scalable, is an interactive and highly customisable application that expects no prior knowledge in graph theory from users.

Uncertainty plays decisive role in the confidence of the decisions made about events. For example, in situation awareness, decision-making is faced with two types of uncertainties; information uncertainty and data uncertainty. Data uncertainty exists due to noise in sensor measurements and is classified as randomness. Information uncertainty is due to ambiguity of using (words) to describe events. This uncertainty is known as fuzziness. Typically, these two types of uncertainties are handled separately using two different theories. Randomness is modeled by probability theory, while fuzzy-logic is used to address fuzziness. In this paper we used the Cloud computation theory to treat data randomness and information fuzziness in one single model. First, we described the Cloud theory then used the theory to generate one and two-dimensional Cloud models. Second, we used the Cloud models to capture and process data randomness and fuzziness in information relative to decision-making in situation awareness. Finally, we applied the models to generate security decisions for security monitoring of sensitive area. Testing results are reported at the end of the paper.

One of the most challenging security concerns for network administrators is the presence of rogue access points (RAPs). The challenge is to detect and disable a RAP before it poses a serious security risk. In this paper, we propose a statistical based approach to detect RAPs using a Hidden Markov Model (HMM), which is applied to passively measure packet‐header data collected at a gateway router. The main idea is to process the sequence of packet traces in order to distinguish the normal packets from the abnormal ones. Our approach utilizes variations in packet inter‐arrival time to differentiate between authorized access points and RAPs. We used the inter‐arrival time of a packet as a distinguishing parameter because it varies drastically for a normal activity and an intrusive activity. We developed our HMM by analyzing Denial of Service (DoS) attacks of 802.11 based wireless local area networks. Our trained HMM can detect the presence of a RAP promptly within a second with extreme accuracy (very low false positive and false negative ratios are obtained). The success of our approach lies in the fact that it leverages knowledge about the behavior of the traffic characteristics of 802.11 based wireless local area networks and the properties of DoS attacks. Experiments were also performed to improve the accuracy of our HMM model. Our approach is scalable and non‐intrusive, requiring little deployment cost and effort, and is easy to manage and maintain. Copyright © 2010 John Wiley & Sons, Ltd.

In this article, we provide an introduction to simulation for cybersecurity and focus on three themes: (1) an overview of the cybersecurity domain; (2) a summary of notable simulation research efforts for cybersecurity; and (3) a proposed way forward on how simulations could broaden cybersecurity efforts. The overview of cybersecurity provides readers with a foundational perspective of cybersecurity in the light of targets, threats, and preventive measures. The simulation research section details the current role that simulation plays in cybersecurity, which mainly falls on representative environment building; test, evaluate, and explore; training and exercises; risk analysis and assessment; and humans in cybersecurity research. The proposed way forward section posits that the advancement of collecting and accessing sociotechnological data to inform models, the creation of new theoretical constructs, and the integration and improvement of behavioral models are needed to advance cybersecurity efforts.