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In the contemporary digital landscape, security has become a vital element of our existence. The growing volume of sensitive information being stored and transmitted over networks necessitates the implementation of robust security measures. Cryptographic algorithms, which are critical for protecting user data privacy, rely on cryptographic keys to ensure data security. True Random Number Generators (TRNGs) are essential to numerous vital security applications. In this paper, we propose a novel Braided and Hybrid Cross-Coupled Entropy Source (B+HCCES) TRNG module. The proposed B+HCCES TRNG module generates random numbers based on the race hazard and jitter of braided and cross-coupled combinational logic gates. The B+HCCES architecture has been designed using VHDL, and the targeted Field-Programmable Gate Array (FPGA) is the Intel Cyclone V 5CGXFC9D6F27C7 chip. The B+HCCES module operates at a fixed sampling frequency of 300 MHz, generated by an embedded phase-locked loop. The B+HCCES module demonstrates an enhanced throughput of 3.33 times compared to the state-of-the-art, while still maintaining a comparably lightweight architecture. The experimental results demonstrate that the generated random sequence successfully passes the NIST SP800-90B and BSI AIS-31 tests.

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head selection where detection of such attacks is challenging. The dependency of WSN on batteries elevates the concern posed by these threats, making detection and isolation crucial, especially within the framework of energy-efficient clustering protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). Various approaches have been proposed in prior research to deal with such attacks. However, the use of memory-efficient data structures has yet to be effectively addressed. In this article, considering the limitations of WSN, we utilize memory-efficient data structures named Bloom filters, count-min (CM) sketch, and cellular automata (CA) to address abnormal energy drainage. A CA-based trust model is used to choose the legitimate node as the cluster head. CM sketch is used to control the frequency of a node selected as a cluster head, achieving fairness in the cluster head selection process, and Bloom filters maintain the record of malicious nodes blocked from participating in the communication or cluster head selection process. CA and trust functions collectively keep a record of neighbors’ energy and their trust in the network. Grayhole, blackhole, and scheduling attacks are three well-known threats that lead to abnormal energy drainage in legitimate nodes. The proposed solution effectively detects and addresses abnormal energy drainage in WSN. Its impact is simulated and observed using ns2 IEEE 802.15.4 medium access control (MAC) and LEACH clustering protocols, specifically in the context of the mentioned attacks. The effectiveness of the proposed model was rigorously analysed, and it was observed that it reduces the energy consumption of WSN by approximately 16.66%, 48.33%, and 43.33% in the cases of grayhole, blackhole, and scheduling attacks, respectively. In terms of space/time complexity, its growth is linear O(n). The proposed solution also consumes 0.08-0.10 J more energy compared to the original LEACH as a cost of the solution, which is not more than 2% of the total initial energy. The trade-off of implementing heightened security is worthwhile, as the proposed approach outperforms the original LEACH and related methods, effectively mitigating abnormal energy drainage in WSN and extending network lifetime, especially in challenging environments with persistent battery recharging challenges.

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweight, and fast (having less inference time), designed particularly to detect botnet attacks in resource constrained IoT devices. This paper proposes SkipGateNet, a novel deep learning model designed for detecting Mirai and Bashlite botnet attacks in resource constrained IoT and fog computing environments. SkipGateNet is a lightweight, fast model combining 1D-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) layers. The novelty of this model lies in the integration of ‘Learnable Skip Connections’. These connections feature gating mechanisms that enhance detection by focusing on relevant features and ignoring irrelevant ones. They add adaptability to the architecture, performing feature selection and propagating only essential features to deeper layers. Tested on the N-BaIoT dataset, SkipGateNet efficiently detects ten types of botnet attacks, with a remarkable test accuracy of 99.91%. It is also compact (2596.87 KB) and demonstrates a quick inference time of 8.0 milliseconds, suitable for real-time implementation in resource-limited settings. While evaluating its performance, parameters like precision, recall, accuracy, and F1 score were considered, along with statistical reliability measures like Cohen’s Kappa Coefficient and Matthews Correlation Coefficient. These highlight its reliability and effectiveness in IoT security challenges. The paper also compares SkipGateNet to existing models and four other deep learning architectures, including two sequential CNN architectures, a simple CNN+LSTM architecture, and a CNN+LSTM with standard skip connections. SkipGateNet surpasses all in accuracy and inference time, demonstrating its superiority in addressing IoT security issues.

Abstract For the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner.

Hexavalent chromium (Cr[VI]) is a widespread environmental pollutant in air and water that is primarily attributed to industrial pollution. The current maximum contaminant levels (MCLs) for drinking water from the World Health Organization and the U.S. Environmental Protection Agency (0.05 and 0.1 mg/L, respectively) were set based on contact dermatitis and warrant further toxicological investigation. While Cr(VI) is neurotoxic and accumulates in the brain, most animal studies only report whole-brain Cr, leaving large knowledge gaps. Few studies consider differences between ages or sexes, and fewer consider essential metal dyshomeostasis. We sought to investigate where Cr accumulates in the brain, considering sex and age differences, following a 90-day drinking water exposure to current MCLs. Here, we report Cr levels in six brain regions of rats exposed to drinking water Cr(VI). We observed Cr only accumulated in the hippocampus, and only in older females. We further assessed changes to essential metals in the hippocampus, observing opposite effects across sexes and between young rats compared to older rats. In sum, our data indicate drinking water Cr(VI) selectively targeted the hippocampus, with geriatric females accumulating the most Cr, and induced significant essential metal dyshomeostasis even in tissues lacking evident Cr accumulation.

We are facing a critical aging crisis, with geriatric populations (65+) growing to unprecedented proportions and ~4 million people (a 6.5-fold increase) expected to become centenarians by 2050. This is compounded by environmental pollution, which affects individuals of all ages and contributes to age-related diseases. As we have a limited understanding of how environmental pollutants affect older populations distinctly from younger populations, these longer-lived geriatric populations present a key knowledge gap. To address this knowledge gap, we employ a “Toxic Aging Coin” approach: heads consider how age impacts chemical toxicity, and tails consider how chemicals act as gerontogens—or how they accelerate biological aging. We employed this approach to investigate hexavalent chromium (Cr[VI]) impacts on female rats exposed to 0.05 or 0.1 mg Cr(VI)/L in drinking water for 90 days; these are the maximum contaminant levels (i.e., the highest levels permitted) from the World Health Organization and U.S. Environmental Protection Agency, respectively. During exposure, rats performed a battery of behavior assays to assess grip strength, locomotor coordination, anxiety, spatial memory, sociability, and social novelty preference. We observed age differences in Cr(VI) neurotoxicity, with grip strength, locomotor function, and spatial memory in middle-aged females being particularly affected. We further compared these results in females to results in males, noting many sex differences, especially in middle-aged rats. These data emphasize the need to consider age and sex as variables in toxicology and to revisit drinking water regulations for Cr(VI).

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability.

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often prohibited. Luckily, privacy-preserving technologies have been developed to overcome this hurdle by distributing the computation of the training and ensuring the data privacy to their owners. The distribution of the computation to multiple participating entities introduces new privacy complications and risks. In this paper, we present a privacy-preserving decentralised workflow that facilitates trusted federated learning among participants. Our proof-of-concept defines a trust framework instantiated using decentralised identity technologies being developed under Hyperledger projects Aries/Indy/Ursa. Only entities in possession of Verifiable Credentials issued from the appropriate authorities are able to establish secure, authenticated communication channels authorised to participate in a federated learning workflow related to mental health data.

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods. Resource constraints and security are often the two major concerns of IIoT (Industrial IoT applications and big data generation at the present time. The strict security measures are often not significantly resource-managed and therefore, negotiation normally takes place between these. Following this, various light-weight versions of generic security primitives have been developed for IIoT and other resource-constrained sustainability. In this paper, we address the authentication concerns for resource-constrained environments by designing an efficient authentication protocol. Our authentication scheme is based on LiSP (light-weight Signcryption Protocol); however, some further customization has been performed on it to make it more suitable for IIoT-like resource-constrained environments. We use Keccack as the hash function in the process and Elli for light-weight public-key cryptography. We name our authentication scheme: Extended light-weight Signcryption Protocol with Keccack (LiSP-XK). The paper outlines a comparative analysis on our new design of authentication against a range of state-of-the-art schemes. We find the suitability of LiSP-XK for IIoT like environments due to its lesser complexity and less energy consumption. Moreover, the signcryption process is also beneficial in enhancing security. Overall the paper shows that LiSP-XK is overall 35% better in efficiency as compared to the other signcryption approaches.

Blockchain is a technology used with the series of users in peer-to-peer transactions to utilize the usability properties of the immutable data records. The distributed nature of this technology has given the wide acceptance to its range of applications in various sectors. Seeing the prospect of this new technology, we have chosen the field of human resource management as these data needs to be privacy preserving and confidential along with significant research value. Distributed ledger approach is a novel idea in this field of work specifically for the application of human resource records management. We have used a privacy preserving framework that provides a transparent system for human resource record management. Wallets are generated with organization id and outputting with public-private key pair along with privacy parameter mapping with hash. Keys are used to provide confidentiality, integrity and authentication. Smart contract uses distributed but converged decision with privacy level classification. Performance of the proposed work has been measured based on time, memory consumptions, failure point identification and read-write latencies. The analysis of the results confirms the efficiency of the system.

The increasing number of devices in the age of Internet-of-Thing (IoT) has arisen a number of problems related to security. Cryptographic processes, more precisely the signatures and the keys, increase and generate an overhead on the network resources with these huge connections. Therefore, in this paper we present a signcryption framework to address the above problems. The solution highlights the use of aggregate signcryption and certificaless approach based on bilinear pairings. The use of signcryption with aggregation and certificateless authentication reduces the time consumption, overhead and complexity. The solution is also able to solve the key staling problems. Experimental results and comparative analysis based on key parameters, memory utilization and bandwidth utilization have been measured. It confirms that the presented work is efficient for IoT infrastructure.

Destination Advertisement Objects (DAOs) are sent upward by RPL nodes toward the DODAG root, to build the downward routing paths carrying traffic from the root to its associated nodes. This routing mechanism can be exploited by a malicious node periodically transmitting a large volume of DAO messages towards its parent, which in turn will forward such messages to its own parent and so on, until they arrive at the Direction-Oriented Directed Acyclic Graph (DODAG) root. This ultimately results in a negative effect on network performance in terms of energy consumption, latency and reliability. The first objective of this paper is to evaluate the effect of such a DAO attack in the context of an RPL IoT network. In particular, identifying the particular performance metrics and network resources affected most greatly. The second objective is the proposal of mitigating security mechanisms in relation to DAO attacks and to evaluate their effectiveness. The simulation results have shown how the attack can damage the network performance by significantly increasing the DAO overhead and power consumption. It also demonstrated that the DAO attack affect the reliability of the downward traffic under specific conditions. The proposed mechanisms showed a good capacity in restoring the optimal performance of the network by up to 205%, 181%, 87% and 6%, in terms of overhead, latency, power consumption and packet delivery ratio respectively.

Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyribonucleic Acid (DNA) and plaintext image. The proposed scheme results in chaotic visual selective encryption of image data. In order to make and ensure that this new scheme is robust and secure against various kinds of attacks, the initial conditions of the chaotic maps utilized are generated from a random DNA sequence as well as plaintext image via an SHA-512 hash function. To increase the key space, three different single dimension chaotic maps are used. In the proposed scheme, these maps introduce diffusion in a plain image by selecting a block that have greater correlation and then it is bitwise XORed with the random matrix. The other two chaotic maps break the correlation among adjacent pixels via confusion (row and column shuffling). Once the ciphertext image has been divided into the respective units of Most Significant Bits (MSBs) and Least Significant Bit (LSBs), the host image is passed through lifting wavelet transformation, which replaces the low-frequency blocks of the host image (i.e., HL and HH) with the aforementioned MSBs and LSBs of ciphertext. This produces a final visual selective encrypted image and all security measures proves the robustness of the proposed scheme.

The evolution of wireless and mobile communication from 0G to the upcoming 5G gives rise to data sharing through the Internet. This data transfer via open public networks are susceptible to several types of attacks. Encryption is a method that can protect information from hackers and hence confidential data can be secured through a cryptosystem. Due to the increased number of cyber attacks, encryption has become an important component of modern-day communication. In this article, a new image encryption algorithm is presented using chaos theory and dynamic substitution. The proposed scheme is based on two-dimensional Henon, Ikeda chaotic maps, and substitution box (S-box) transformation. Through Henon, a random S-Box is selected and the image pixel is substituted randomly. To analyze security and robustness of the proposed algorithm, several security tests such as information entropy, histogram investigation, correlation analysis, energy, homogeneity, and mean square error are performed. The entropy values of the test images are greater than 7.99 and the key space of the proposed algorithm is 2798. Furthermore, the correlation values of the encrypted images using the proposed scheme are close to zero when compared with other conventional schemes. The number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed scheme are higher than 99.50% and 33, respectively. The simulation results and comparison with the state-of-the-art algorithms prove the efficiency and security of the proposed scheme.

Traditional password authentication methods have raised many issues in the past, including insecure practices, so it comes as no surprise that the evolution of authentication should arrive in the form of password-less solutions. This research aims to explore the problems that password authentication and password policies present and aims to deploy Windows Hello for Business (WHFB) on-premises. This includes creating three virtual machines (VMs) and evaluating WHFB as a password-less solution and showing how an attacker with privileged access may retrieve the end user’s domain password from the computer’s memory using Mimikatz and describing the possible results. The conducted research tests are in the form of two attack methods. This was feasible by the creation of three VMs operating in the following way. The first VM will act as a domain controller (DC) and certificate authority server (CA server). The second VM will act as an Active Directory Federation Service (ADFS). The third VM will act as the end-user device. The test findings research summarized that password-less authentication is far more secure than the traditional authentication method; this is evidenced throughout the author’s tests. Within the first test, it was possible to retrieve the password from an enrolled device for WHFB while it was still in the second phase of the deployment. The second test was a brute-force attack on the PIN of WHFB; since WHFB has measures to prevent such attacks, the attack was unsuccessful. However, even though the retrieval of the password was successful, there are several obstacles to achieving this outcome. It was concluded that many organizations still use password authentication as their primary authentication method for accessing devices and applications. Larger organizations such as Microsoft and Google support the adoption of password-less authentication for end-users, and the current usage of password-less authentication shared by both organizations is encouraged. This usually leads organizations to adopt this new solution for their IT infrastructure. This is because it has been used and tested by millions of people and has proven to be safe. This supports the findings of increased usage and the need for password-less authentication by today’s users.

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation.

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target’s data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted files being written to disk, often using a file’s entropy as an indicator of encryption. However, often in the description of these techniques, little or no discussion is made as to why a particular entropy calculation technique is selected or any justification given as to why one technique is selected over the alternatives. The Shannon method of entropy calculation is the most commonly-used technique when it comes to file encryption identification in crypto-ransomware detection techniques. Overall, correctly encrypted data should be indistinguishable from random data, so apart from the standard mathematical entropy calculations such as Chi-Square (χ2), Shannon Entropy and Serial Correlation, the test suites used to validate the output from pseudo-random number generators would also be suited to perform this analysis. The hypothesis being that there is a fundamental difference between different entropy methods and that the best methods may be used to better detect ransomware encrypted files. The paper compares the accuracy of 53 distinct tests in being able to differentiate between encrypted data and other file types. The testing is broken down into two phases, the first phase is used to identify potential candidate tests, and a second phase where these candidates are thoroughly evaluated. To ensure that the tests were sufficiently robust, the NapierOne dataset is used. This dataset contains thousands of examples of the most commonly used file types, as well as examples of files that have been encrypted by crypto-ransomware. During the second phase of testing, 11 candidate entropy calculation techniques were tested against more than 270,000 individual files—resulting in nearly three million separate calculations. The overall accuracy of each of the individual test’s ability to differentiate between files encrypted using crypto-ransomware and other file types is then evaluated and each test is compared using this metric in an attempt to identify the entropy method most suited for encrypted file identification. An investigation was also undertaken to determine if a hybrid approach, where the results of multiple tests are combined, to discover if an improvement in accuracy could be achieved.

In this research paper, blockchain-based trust management model is proposed to enhance trust relationship among beacon nodes and to eradicate malicious nodes in Wireless Sensor Networks (WSNs). This composite trust evaluation involves behavioral-based trust as well as data-based trust. Various metrics such as closeness, honesty, intimacy and frequency of interaction are taken into account to compute behavioral-based trust of beacon nodes. Further, the composite (behavior and data) trust value of each beacon nodes is broadcast to Base Stations (BS) to generate a blockchain of trust values. Subsequently, the management model discards the beacon node with least trust value and that ensures reliability and consistency of localization in WSNs. The simulated results of the proposed algorithm are compared with the existing ones in terms of detection accuracy, False Positive Rate (FPR) and False Negative Rate (FNR) and Average Energy Consumption (AEC).

The Rivest Cipher 4 (RC4) has been one of the most popular stream ciphers for providing symmetric key encryption, and is now proposed as an efficient cipher within light-weight cryptography. As an algorithm it has been considered to be one of the fastest stream ciphers and one of the easiest to implement. Unfortunately, despite its simplicity of usage, a number of attacks on it have been found. Therefore, various improvements of this algorithm exist in cryptography, but none of them use proper randomness. This paper outlines modified version of RC4 and which has the desirable features of an efficient stream cipher algorithm, and which integrates the Symmetric Random Function Generator (SRFG) method. Though RC4 uses pseudorandom features with an initialisation vector and a seed value, the use of true randomness in RC4 is novel in this domain. Therefore, this paper proposes a modified RC4 as MRC4, and which then evaluates the statistical features of MRC4 based upon parameters such as non-linearity, resiliency, balancedness, propagation and immunity. Further, we have compared the security features and confusion-diffusion attributes with some recent variants of RC4 and have found that MRC4 is efficient in withstanding against attacks. The experimental results show that MRC4 supports a 60% better confusion property and 50% better diffusion as compared to the original RC4 method.

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and improve overall organisational security. An organisation’s vulnerabilities to attacks might change over time. It is utterly important to find a balance among a current threat, the potential countermeasures, their consequences and costs, and the estimation of the overall risk that this provides to the organisation. For enhancing organisational security and automation, applying threat intelligence technology is critical for detecting, classifying, analysing, and sharing new cyberattack tactics. Trusted partner organisations can then share newly identified threats to improve their defensive capabilities against unknown attacks. On this basis, organisations can help reduce the risk of a cyberattack by providing access to past and current cybersecurity events through blockchain smart contracts and the Interplanetary File System (IPFS). The suggested combination of technologies can make organisational systems more reliable and secure, improving system automation and data quality. This paper outlines a privacy-preserving mechanism for threat information sharing in a trusted way. It proposes a reliable and secure architecture for data automation, quality, and traceability based on the Hyperledger Fabric private-permissioned distributed ledger technology and the MITRE ATT&CK threat intelligence framework. This methodology can also be applied to combat intellectual property theft and industrial espionage.

E-governance is a process that aims to enhance a government’s ability to simplify all the processes that may involve government, citizens, businesses, and so on. The rapid evolution of digital technologies has often created the necessity for the establishment of an e-Governance model. There is often a need for an inclusive e-governance model with integrated multiactor governance services and where a single market approach can be adopted. e-Governance often aims to minimise bureaucratic processes, while at the same time including a digital-by-default approach to public services. This aims at administrative efficiency and the reduction of bureaucratic processes. It can also improve government capabilities, and enhances trust and security, which brings confidence in governmental transactions. However, solid implementations of a distributed data sharing model within an e-governance architecture is far from a reality; hence, citizens of European countries often go through the tedious process of having their confidential information verified. This paper focuses on the sinGLe sign-on e-GovernAnce Paradigm based on a distributed file-exchange network for security, transparency, cost-effectiveness and trust (GLASS) model, which aims to ensure that a citizen can control their relationship with governmental agencies. The paper thus proposes an approach that integrates a permissioned blockchain with the InterPlanetary File System (IPFS). This method demonstrates how we may encrypt and store verifiable credentials of the GLASS ecosystem, such as academic awards, ID documents and so on, within IPFS in a secure manner and thus only allow trusted users to read a blockchain record, and obtain the encryption key. This allows for the decryption of a given verifiable credential that stored on IPFS. This paper outlines the creation of a demonstrator that proves the principles of the GLASS approach.

Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

Web browsers are one of the most used applications on every computational device in our days. Hence, they play a pivotal role in any forensic investigation and help determine if nefarious or suspicious activity has occurred on that device. Our study investigates the usage of private mode and browsing artefacts within four prevalent web browsers and is focused on analyzing both hard disk and random access memory. Forensic analysis on the target device showed that using private mode matched each of the web browser vendors’ claims, such as that browsing activity, search history, cookies and temporary files that are not saved in the device’s hard disks. However, in volatile memory analysis, a majority of artefacts within the test cases were retrieved. Hence, a malicious actor performing a similar approach could potentially retrieve sensitive information left behind on the device without the user’s consent.

A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset.

Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.

To review blockchain lessons learned in 2018 and near-future predictions for blockchain in healthcare, Blockchain in Healthcare Today (BHTY) asked the world's blockchain in healthcare experts to share their insights. Here, our internationally-renowned BHTY peer-review board discusses their major predictions. Based on their responses, presented in detail below, ten major themes (Table ) for the future of blockchain in healthcare will emerge over the 12 months. CORRIGENDUM: This following paragraph has been corrected (page 3, first paragraph) from: "Fourth, with over 1000 insurance companies in the country, filling out paperwork to document provider training and licensure is a nightmare. The Synaptic Health Alliance aims to simplify this process by putting all credentialing information on a distributed public ledger for all stakeholders to access.1" To: "Fourth, keeping health care provider directories maintained by health plans up-to-date is a critical, complex issue facing organizations across the health care system. The first project of the Synaptic Health Alliance aims to simplify this process by putting provider demographic information on a permissioned blockchain for Alliance members to access and maintain.1"

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers’ activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches often face risks that may expose medical record storage solutions to common security attack vectors. However, healthcare-oriented blockchain solutions can provide a decentralized, anonymous and secure EHR handling approach. This paper presents PREHEALTH, a privacy-preserving EHR management solution that uses distributed ledger technology and an Identity Mixer (Idemix). The paper describes a proof-of-concept implementation that uses the Hyperledger Fabric’s permissioned blockchain framework. The proposed solution is able to store patient records effectively whilst providing anonymity and unlinkability. Experimental performance evaluation results demonstrate the scheme’s efficiency and feasibility for real-world scale deployment.

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g., for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records.

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.

The Public Key Infrastructure (PKI) is crumbling, partially due to the lack of a strong understanding of how encryption actually works, but also due to weaknesses in its implementation. This paper outlines an Internet storage technique using secret sharing methods which could be used to overcome the problems inherent with PKI, while supporting new types of architectures incorporating such things as automated failover and break-glass data recovery. The paper outlines a novel architecture: SECRET, which supports a robust cloud-based infrastructure with in-built privacy and failover. In order to understand the performance overhead of SECRET, the paper outlines a range of experiments that investigate the overhead of this and other secret share methods.