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Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

Abstract The orientation of emerging technologies on the Internet is moving toward decentralisation. Botnets have always been one of the biggest threats to Internet security, and botmasters have adopted the robust concept of decentralisation to develop and improve peer-to-peer botnet tactics. This makes the botnets cleverer and more artful, although bots under the same botnet have symmetrical behaviour, which is what makes them detectable. However, the literature indicates that the last decade has lacked research that explores new behavioural characteristics that could be used to identify peer-to-peer botnets. For the abovementioned reasons, in this study, we propose new two methods to detect peer-to-peer botnets: first, we explored a new set of behavioural characteristics based on network traffic flow analyses that allow network administrators to more easily recognise a botnet’s presence, and second, we developed a new anomaly detection approach by adopting machine-learning and deep-learning techniques that have not yet been leveraged to detect peer-to-peer botnets using only the five-tuple static indicators as selected features. The experimental analyses revealed new and important behavioural characteristics that can be used to identify peer-to-peer botnets, whereas the experimental results for the detection approach showed a high detection accuracy of 99.99% with no false alarms. Graphical Abstract

Abstract Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli’s Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

Amid the exponential rise of Internet of Things (IoT) devices, the Message Queue Telemetry Transport (MQTT) protocol has gained prominence due to its efficiency in facilitating device-cloud interactions. Yet, the surge in IoT device usage and MQTT’s popularity has spotlighted potential security risks. Vulnerabilities in this realm can lead to substantial disturbances and financial setbacks. While there is a noticeable increase in IoT-related attacks, comprehensive reviews on MQTT security remain scarce. Existing studies often exhibit shortcomings, such as a broad but superficial discussion of MQTT attacks and countermeasures. Additionally, many essential components and roles in building or implementing MQTT-based applications have not been adequately addressed. This research fills this void by offering a contemporary analysis of MQTT ecosystem security challenges, encompassing prevalent attacks, their repercussions, mitigation strategies, and prospective areas for further research. This study presents a comprehensive taxonomy of security attacks within the MQTT ecosystem, offering a systematic framework to guide researchers, businesses, and end-users in mitigating these risks. As a result, this work serves as a crucial resource for enhancing the security of IoT devices utilizing MQTT, marking a significant stride in safeguarding IoT infrastructure.

Present dynamic performance reputation and technical innovations in Internet of Things (IoT) technologies have endowed ultra-inexpensive, energy effcient, smart, and tiny IoT gadgets. IoT gadgets can be easily implanted inside, attached to, or placed around the chronic patient body, and they can be employed in several healthcare monitoring systems such as mobile, wearable, and implantable healthcare monitoring for chronic diseases. Healthcare monitoring for chronic diseases is one of the major applications of IoT and is also a typical challenging area. The rapidly rising proportion of patients with chronic diseases brought enormous pressure on governments and healthcare providers and required up-to-date long-term healthcare service and continuous monitoring. In this survey paper, we extensively review numerous industrial and non-commercial contemporary healthcare monitoring systems (HMS) and applications. We present a design layout of IoT-based HMS for chronic diseases. We presented societal and technological challenges associated with the design of IoT-based HMS and their solutions. To accomplish this, more than 80 different healthcare monitoring systems have been characterized and classied. Moreover, we describe contemporary healthcare monitoring networks and communication technologies. This review also presents the dynamic capabilities of key IoT technologies for chronic diseases healthcare monitoring to show dedicated research pathways to IoT researchers. Last, we deeply analyze different healthcare monitoring systems and describe open issues that will help researchers and healthcare system designers design future systems.

An intrusion attack on the Internet of Things (IoT) is any malicious activity or unauthorized access that jeopardizes the integrity and security of IoT systems, networks, or devices. Regarding IoT, intrusions can result in severe problems, including service disruption, data theft, privacy violations, and even bodily injury. One of the intrusion attacks is a keylogging attack, sometimes referred to as keystroke logging or keyboard capture, which is a type of cyberattack in which the attacker secretly observes and records keystrokes made on a device’s keyboard. In the context of IoT, where connected objects communicate and exchange data, this assault may be especially concerning. Keylogging attacks can have severe repercussions in the IoT ecosystem since they can compromise sensitive information, including login passwords, personal information, financial information, or confidential communications. This paper explored the possibility of using an ensemble classifier to detect keylogging attacks in IoT networks. We built an ensemble classifier consisting of three classifiers: a convolutional neural network (CNN), a recurrent neural network (RNN), and a long-short memory network (LSTM). A proposed model uses the BoT-IoT dataset to detect a keylogging attack. Results show that the ensemble model can improve the model’s performance. The ensemble model had excellent accuracy and a low false positive rate. It also had significantly improved detection rates for keylogging attacks than other classifiers.

Skin cancer is a pervasive and potentially life-threatening disease. Early detection plays a crucial role in improving patient outcomes. Machine learning (ML) techniques, particularly when combined with pre-trained deep learning models, have shown promise in enhancing the accuracy of skin cancer detection. In this paper, we enhanced the VGG19 pre-trained model with max pooling and dense layer for the prediction of skin cancer. Moreover, we also explored the pre-trained models such as Visual Geometry Group 19 (VGG19), Residual Network 152 version 2 (ResNet152v2), Inception-Residual Network version 2 (InceptionResNetV2), Dense Convolutional Network 201 (DenseNet201), Residual Network 50 (ResNet50), Inception version 3 (InceptionV3), For training, skin lesions dataset is used with malignant and benign cases. The models extract features and divide skin lesions into two categories: malignant and benign. The features are then fed into machine learning methods, including Linear Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM), our results demonstrate that combining E-VGG19 model with traditional classifiers significantly improves the overall classification accuracy for skin cancer detection and classification. Moreover, we have also compared the performance of baseline classifiers and pre-trained models with metrics (recall, F1 score, precision, sensitivity, and accuracy). The experiment results provide valuable insights into the effectiveness of various models and classifiers for accurate and efficient skin cancer detection. This research contributes to the ongoing efforts to create automated technologies for detecting skin cancer that can help healthcare professionals and individuals identify potential skin cancer cases at an early stage, ultimately leading to more timely and effective treatments.

Abstract The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00–91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics.

Software Defined Network (SDN) has alleviated traditional network limitations but faces a significant challenge due to the risk of Distributed Denial of Service (DDoS) attacks against an SDN controller, with current detection methods lacking evaluation on unrealistic SDN datasets and standard DDoS attacks (i.e., high-rate DDoS attack). Therefore, a realistic dataset called HLD-DDoSDN is introduced, encompassing prevalent DDoS attacks specifically aimed at an SDN controller, such as User Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP), and User Datagram Protocol (UDP). This SDN dataset also incorporates diverse levels of traffic fluctuations, representing different traffic variation rates (i.e., high and low rates) in DDoS attacks. It is qualitatively compared to existing SDN datasets and quantitatively evaluated across all eight scenarios to ensure its superiority. Furthermore, it fulfils the requirements of a benchmark dataset in terms of size, variety of attacks and scenarios, with significant features that highly contribute to detecting realistic SDN attacks. The features of HLD-DDoSDN are evaluated using a Deep Multilayer Perception (D-MLP) based detection approach. Experimental findings indicate that the employed features exhibit high performance in the detection accuracy, recall, and precision of detecting high and low-rate DDoS flooding attacks.

The Internet of Things (IoT) connects daily use devices to the Internet, such as home appliances, health care equipment, sensors, and industrial devices. Concurrently, numerous cyber-attacks target those objects and their backbone IoT networks consecutively. Therefore, several researchers have adopted Machine Learning (ML) and Deep Learning (DL) algorithms to develop efficient Intrusion Detection Systems (IDSs). However, the restricted resources of IoT devices hinder integrating those systems with those tiny devices. Hence, designing lightweight IDSs gets more interest from researchers to build efficient detection models to discard attacks in IoT networks. To give a holistic insight into this research domain, this paper presents a Systematic Literature Review (SLR) to review and analyse the recent ML and DL techniques to lighten the IDS models for detecting attacks in IoT devices. In addition, the literature studies were retrieved from six scientific databases Google Scholar, Science Direct, IEEE Xplore®, Scopus, Web of Science, and Springer. From 4,703 identified records, 57 studies were adopted based on predesigned research questions and inclusion/exclusion criteria. The study's findings illustrate the most recently used ML and DL mechanisms and feature engineering techniques to lighten the proposed IDS models. It also shows the most attacks detected, datasets used, tools and network simulators employed, and evaluation metrics and parameters. Furthermore, it suggests the research challenges and future direction after discussing the limitations of the currently proposed techniques. This study shows that most selected studies are journal articles published in IEEE Xplore®. Furthermore, the most used feature engineering techniques are filter-based, as they deliver better performance and lightness than the developed models. Most studies use correlation algorithms as a feature selection technique. Finally, the most discussed attack in the selected studies is the DoS attack.

The metaverse, an emergent interconnected network that harmoniously merges digital and physical realities, represents a revolutionary paradigm in the computing realm, engendering a nexus of immersive, interactive experiences through user avatars. This new digital landscape, forged by the advancement of immersive technologies like virtual and augmented reality, coupled with the sophistication of artificial intelligence, blockchain, and edge computing, presents diverse prospects from innovative experiential creations to the resolution of complex issues like remote work and virtual social engagement, to remote surgeries, immersive learning and so on. Nevertheless, it confronts obstacles, including privacy, security, equitable access, and ethical concerns, necessitating the construction of robust legal and ethical frameworks for the common good. This research, a comprehensive examination of this burgeoning phenomenon, systematically scrutinizes its underpinning constructs and trailblazing applications via databases such as ScienceDirect, ResearchGate, and IEEE Xplore. It uncovers the metaverse’s incarnations in gaming, social platforms, education, and healthcare, signifying its transformative capacity across these sectors. The exploration underscores the imminent requirement of addressing legal and ethical dimensions as we move towards this novel digital existence, thereby paving the way for future research to architect a secure, efficient, and inclusive metaverse.

Phishing represents a cybersecurity attack strategy commonly employed by cybercriminals to unlawfully acquire sensitive user information, including passwords, account details, credit card data, and other personally identifiable information. Phishing websites bear a striking resemblance to their legitimate counterparts, thus rendering them inconspicuous and challenging for an unsuspecting user to identify. Criminals and phishing experts frequently leverage cloaking mechanisms to evade detection software and web crawlers. This paper provides a comprehensive systematic review of primary studies conducted between 2012 and 2022 on using cloaking techniques to evade detection by anti-phishing entities based on data extracted from Scopus, Web of Science, and Google Scholar. Different server-side and client-side detection strategies, phishing techniques and cloaking mechanisms, toolkits, blacklists, phishing or anti-phishing ecosystems, and other such concepts have been taken as thematic outputs of the study and have been discussed in detail. This systematic literature review (SLR) is one of the first reviews to be conducted for analyzing the current cloaking or evasion techniques used by phishers, and the limitations of the study have been outlined as well.

In recent times, the emergence of the Metaverse has garnered worldwide attention as an innovative digital space that holds immense potential to provide a wide range of health services to medical professionals and patients. With increasing stress on healthcare systems, it has become crucial to explore the latest and cost-effective solutions that can provide fast and reliable medical services. The focus of this study, therefore, is to explore applications of metaverse in various health care systems and elaborate on how it can efficiently improve the clinical management of patients. Consequently, an in-depth assessment of the metaverse has been carried out, while covering its core fundamentals, key technologies, and diverse applications in healthcare and medicine, including but not limited to, emergency response learning, hands-on experience in anatomy learning, orthopaedics, paediatrics and so on. To carry out the study, we have used an exploratory approach to analyze qualitative data on healthcare metaverse services in our systematic review. Relevant articles from scientific databases such as Web of Science, Springer, Scopus, and IEEE have been identified, and the analysis has been conducted using the PRISMA reporting guideline to ensure transparent and comprehensive reporting. The results of the study suggest that the metaverse has the potential to transform healthcare systems by introducing novel methods for delivering healthcare services. Metaverse’s AR/VR technologies can enable remote medical consultations and training, benefiting patients and healthcare professionals. Additionally, patients can access health-related information and resources, empowering them to manage their health better and make more informed decisions.

Innovative framework on Vehicles in Network Simulation (VEINS) for Vehicular Ad-hoc Network (VANET) that use security aspect is mainly limited and dispersed. In order to offer valuable visions for technical settings and researchers, the study looked into the trends and gaps that were currently present. As a result, this systematic literature review was carried out to develop a comprehensive taxonomy of the research landscape. A thorough study was done for papers about (a) VANET, (b) VEINS, and (C) security aspects. This research used three databases, namely IEEE Xplore, ScienceDirect, and Scopus. These databases included in-depth research focused on VANET based on the VEINS framework. Then, on the basis of the security aspect, filtering was accomplished. The first class includes threats and vulnerabilities that evaluate the effects of threats and vulnerabilities on VANETs by using the VEINS framework and suggest ways to mitigate or lessen their effects. The second category includes articles on the solution technology that uses blockchain, machine learning, and Software-Defined Networking (SDN) techniques in VEINS-based VANET applications. The third class comprises the requirements that satisfy privacy, authentication, trust management, reliability, and revocation of the VANET security-based VEINS framework. Finally, this paper reviews the architecture and bidirectional coupling of the VEINS framework.

Message Queuing Telemetry Transport (MQTT) is expected to be the de facto messaging IoT standard. Therefore, MQTT must achieve efficient security. Nevertheless, the most significant drawback of the MQTT is its lack of protection mechanisms. Meanwhile, the existing approaches have added processing overhead to the devices and are still vulnerable to various attacks. Therefore, this research work presented an integrated scheme known as the Robust Security Scheme (RSS) to protect the MQTT against any exploitations that might result in sophisticated cyberattacks. The proposed RSS employs two cryptosystems: 1) a dynamic variant of the Advanced Encryption Standard (D-AES); and 2) Key-Policy Attribute-Based Encryption (KP-ABE). RSS introduces a new design architecture of the symmetric AES algorithm to encrypt the MQTT payload called D-AES. Additionally, the second part of the proposed hybrid cryptosystem is KP-ABE, which is utilized to cipher the private key of the proposed D-AES to avoid the computation overhead of bilinear maps. The performance of the proposed RSS is measured in terms of processing time and traffic overhead. Additionally, the security aspects are evaluated in terms of balance, avalanche effect, and hamming distance and compared to the existing works in a testbed environment. Results revealed that the proposed D-AES is more promising with improvements than the standard AES algorithm. The proposed scheme achieves polymorphism while maintaining interoperability. RSS exhibited improvements over the standard AES algorithm by 8.75%, 10.45%, and 6.81% in terms of balance, avalanche effect, and hamming distance, respectively.

Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.

The fifth-generation (5G) technology-enabled vehicular network has been widely used in intelligent transportation in recent years. Since messages shared among vehicles are always broadcasted by openness environment’ nature, which is vulnerable to several privacy and security problems. To cope with this issue, several researchers have proposed pseudonym authentication schemes for the 5G-enabled vehicular network. Nevertheless, these schemes applied complected and time-consumed operations. Therefore, this paper proposes a fog computing-based pseudonym authentication (FC-PA) scheme to decrease the overhead of performance in 5G-enabled vehicular networks. The FC-PA scheme applies only one scalar multiplication operation of elliptic curve cryptography to prove information. A security analysis of our work explains that our scheme satisfies privacy-preserving and pseudonym authentication, which are resilient against common security attacks. With performance efficiency, our work can obtain better trade-offs between efficiency and security than the well-known recent works.

Abstract In the era of the Internet of things (IoT), billions of electrical devices are connected to the Internet and communicate remotely with one another without any human intervention. IoT devices often do not enforce proper security measures; this raises serious privacy and security concerns. One of the big issues in securing IoT communications is that traditional cryptographical protocols incur high computational costs and are not suitable for resource‐constrained devices. In this paper, the authors focus on issues related to secret keys distribution and authentication schemes. Although there exist some lightweight authentication schemes for IoT devices in the literature, they come with some limitations (e.g. preconfiguration of shared secret keys). A new lightweight authentication scheme suitable for IoT devices is proposed. This scheme incurs low resource utilisation and provides a trusted mode of Secret key distribution between IoT devices and a control server. A real‐time test‐bed environment is deployed to examine whether the authors’ proposed scheme is applicable for resource‐constrained devices. The authors’ performance analysis shows that the proposed scheme is efficient and is resistant to possible security attacks such as replay attacks, man‐in‐the middle attacks, impersonation attacks, modification attacks, and remote access trojan attacks.

Abstract The rapid growth of the number of devices connected to the Internet and the increasing demand for electronic services have led to a huge growth in the number of cyberattacks targeting cyberspace and the development of their methodology. Therefore, there must be mechanisms, laws, and rules regulating the work of these applications and protecting them from electronic attacks. The Domain Name System (DNS) has several vulnerabilities that can be exploited by cyber attackers to launch their attacks, and the most important one of these vulnerabilities is that the response size is always greater than the size of the request. According to reports published by numerous security companies, distributed reflection denial of service (DRDoS) attacks against DNS are regarded as one of the most hazardous and rapidly spreading threats in recent years. An enhanced mechanism that is able to detect DNS‐based DRDoS attacks that exploit the DNS responses to launch their attacks is proposed. The proposed mechanism was designed based on modified metaheuristic optimization algorithms and an adaptive threshold. This mechanism consists of two models and four stages. The first model is called ‘Proactive Feature Selection,’ and the second model is called ‘Evolving Dynamic Fuzzy Clustering.’ The four stages of the proposed mechanism are: the preprocessing stage, feature selection stage, detection stage, and enhancement stage. The new mechanism has been implemented on the CICDDoS2019 standard dataset and achieves a detection accuracy of 95.44% with a false‐positive rate of 0.22%. The results show that the new mechanism is better than others depending on the detection accuracy and false positives.

People are increasingly sharing their personal information online as internet usage grows. As a result, fraudsters have access to a massive amount of personal information and financial activities. In recent years, phishing assaults have become one of the most common threats faced by internet users, governments, and service providers. The attacker(s) uses falsified emails or bogus websites to obtain the client's sensitive data (i.e., user account login details, credit/debit card numbers, etc.) in a phishing assault. Studies have classed phishing attacks based on fundamental phishing mechanisms and defenses, ignoring the importance of the phishing lifecycle from beginning to conclusion. This study also provides a new thorough taxonomy of phishing assaults, including attack phases, attacker types, vulnerabilities, threats, targets, attack media, and attacking strategies. Furthermore, the proposed anatomy will help readers comprehend the full lifecycle of a phishing attack, which will raise awareness of these phishing attacks and the strategies utilized; it will also aid in the development of a comprehensive anti-phishing system. In addition, various preventative precautions are being investigated

Traffic safety and efficiency are extremely significant in both private and public transportation. The fifth-generation (5G)-enabled vehicular networks works wirelessly to share information among vehicles for helping drivers and passengers. Since the vehicle broadcasts the traffic status messages, privacy and security are considered as a challenging issue in 5G-enabled vehicular networks. In order to satisfy these privacy and security requirements, many privacy-preserving and security attacks schemes have been proposed. Nevertheless, since these schemes use a complex elliptic curve and bilinear pair cryptography operations, the performance efficiency of in terms of communication and computational costs is not satisfactory, which denial of service (DoS) attacks occurs. To address this, this paper proposes modular square root-based to resist denial of service (DoS) attacks (MSR-DoS) scheme in 5G-enabled vehicular networks. Our MSR-DoS scheme satisfies authenticity of source, integrity of message, pseudonym privacy-preserving, unlinkable, traceable and revocable in vehicular networks. The security of our work is proved under burrows abadi needham (BAN) logic. The performance analysis and comparison shows that MSR-DoS scheme has less communication and computational costs as compared to the most recent existing works. Meanwhile, the proposed MSR-DoS scheme reduces the computation overhead of signing the message and verifying the message by 99.80% and 98.55%, respectively.

Recently, the rapid flourish of blockchain technology in the financial field has attracted many cybercriminals’ attention to launching blockchain-based attacks such as ponzi schemes, scam wallets, and phishing scams. Currently, Ethereum is the most prominent blockchain-based platform and the first that supports smart contracts. However, the number of phishing scam accounts are reportedly more than 50% of all cybercrimes in Ethereum. In contrast, this paper proposes a detection mechanism called Ethereum Phishing Scam Detection (Eth-PSD) that attempts to detect phishing scam-related transactions using a novel machine learning-based approach. Eth-PSD tackles some of the limitations in the existing works, such as the use of imbalanced datasets, complex feature engineering, and lower detection accuracy. We also investigated the aspects of constructing a new updated, balanced dataset that can be used to evaluate Eth-PSD effectively. Our experimental results indicate that Eth-PSD could efficiently detect the phishing scam on Ethereum with a detection accuracy of 98.11%, with a very low False Positive Rate of 0.01. Taken together, Eth-PSD showed a superior advantage compared to the existing works in reducing the dimensionality of the dataset by feature engineering and achieved an overall detection accuracy with an improvement of at least 6% compared to other existing solutions from the related work.

Internet Protocol version 6 (IPv6) is envisioned as the cornerstone for future internet connectivity and information technology (IT) expansion. Due to its enormous address pool, extendable headers, high level of security, and mobility, IPv6 is positioned as the next-generation Internet Protocol. NDP is an integral component of IPv6 since it resolves addresses, locates routers, and finds duplicated addresses in a local-link network. Because NDP is based on the premise that all nodes in the network are trustworthy, it is subject to a variety of attacks, including Denial of Service (DoS) on Duplicate Address Detection (DAD) attacks (aka. DoS-on-DAD), Address Resolution-based attacks, Router Advertisement (RA) based attacks, and Redirect attacks. This paper proposes an NDP security (NDPsec) mechanism based on the Ed25519 digital signature to authenticate IPv6 hosts to prevent unauthorized devices from joining the network. The proposed NDPsec mechanism is evaluated and compared to Secure NDP (SeND), Match-Prevention, and Trust-ND mechanisms. The performance is measured in terms of processing time, traffic overhead, and resilience against network-based attacks. The results obtained from the experiments showed that NDPsec successfully prevented cyberattacks, with approximately 144% less processing time and over 50% less traffic overhead compared to SeND (the default security mechanism for NDP protocol). The proposed NDPsec mechanism has remarkable superiority in terms of resilience against attacks compared to Match-Prevention and Trust-ND mechanisms.

For many reasons, Ethereum attracts more investors, researchers, and even scammers; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network. However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years. Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks. Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks. In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack. Furthermore, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015. Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.

The role that vehicular fog computing based on the Fifth Generation (5G) can play in improving traffic management and motorist safety is growing quickly. The use of wireless technology within a vehicle raises issues of confidentiality and safety. Such concerns are optimal targets for conditional privacy-preserving authentication (CPPA) methods. However, current CPPA-based systems face a challenge when subjected to attacks from quantum computers. Because of the need for security and anti-piracy features in fog computing when using a 5G-enabled vehicle system, the L-CPPA scheme is proposed in this article. Using a fog server, secret keys are generated and transmitted to each registered car via a 5G-Base Station (5G-BS) in the proposed L-CPPA system. In the proposed L-CPPA method, the trusted authority, rather than the vehicle's Onboard Unit (OBU), stores the vehicle's master secret data to each fog server. Finally, the computation cost of the suggested L-CPPA system regards message signing, single verification and batch verification is 694.161 ms, 60.118 ms, and 1348.218 ms, respectively. Meanwhile, the communication cost is 7757 bytes.

Fifth-generation (5G)-enabled vehicular fog computing technologies have always been at the forefront of innovation because they support smart transport like the sharing of traffic data and cooperative processing in the urban fabric. Nevertheless, the most important factors limiting progress are concerns over message protection and safety. To cope with these challenges, several scholars have proposed certificateless authentication schemes with pseudonyms and traceability. These schemes avoid complicated management of certificate and escrow of key in the public key infrastructure-based approaches in the identity-based approaches, respectively. Nevertheless, problems such as high communication costs, security holes, and computational complexity still exist. Therefore, this paper proposes an efficient certificateless authentication called the ECA-VFog scheme for fog computing with 5G-assisted vehicular systems. The proposed ECA-VFog scheme applied efficient operations based on elliptic curve cryptography that is supported by a fog server through a 5G-base station. This work conducts a safety analysis of the security designs to analysis the viability and value of the proposed ECA-VFog scheme. In the performance ovulation section, the computation costs for signing and verification process are 2.3539 ms and 1.5752 ms, respectively. While, the communication costs and energy consumption overhead of the ECA-VFog are 124 bytes and 25.610432 mJ, respectively. Moreover, comparing the ECA-VFog scheme to other existing schemes, the performance estimation reveals that it is more cost-effective with regard to computation cost, communication cost, and energy consumption.

Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR.

Internet Exchange Points (IXPs) interconnect heterogeneous networks and transfer substantial traffic volumes. In the past decade, the number of IXPs has seen tremendous growth, with more operators connecting to these IXPs even though these IXPs faced various inter-domain routing limitations. Routers based on Border Gateway Protocol (BGP) forwards packets only based on destination IP prefix and selects only routes learned from their neighbors. IXPs designed using Software-Defined Network (SDN), called SDX, offer solutions for existing inter-domain routing problems. This paper presents the existing scalability limitations of inter-domain routing at IXP and how traditional IXP structural design can be transformed into a highly scalable SDX design by exploiting the SDN platform’s functionalities in different use cases of SDX. The paper then reviewed how the SDX improved various IXP operators’ scalability by reviewing and analyzing the latest SDX models and approaches, which provide enhanced policies to enhance providers’ management operations and offer good quality of services (QoS) to the various participating members. Finally, we discussed the open issues and challenges in this area that need further study and a solution to tackle them.

Vehicular ad hoc networks (VANETs) have become increasingly common in the past decades and provides essential and efficient communication for vehicles within intelligent transportation systems. Securing the VANETs wireless communication channel is one of the principal challenges in VANETs since existing security schemes are still vulnerable to security and privacy issues and have substantial computational and communicational overheads. To overcome these issues, this paper focuses on enhancing an authentication scheme based on conditional privacy-preserving and improving its performance efficiency. This paper reviews the security vulnerabilities of the existing schemes. It also proposes enhancements to the identity-based conditional privacy-preserving authentication scheme to secure and improve the efficiency of VANETs communications. The proposed scheme not only satisfies the security and privacy requirements but also has been proven secure under the random oracle model. Finally, the performance evaluation shows that the proposed scheme is more efficient computationally and communicational than the existing schemes in signing and verifying VANETs messages.

Vehicular Ad hoc Networks (VANET) broadcast messages regarding road and environmental conditions. Due to its design, VANET inadvertently introduced security and privacy issues. Many researchers have suggested various approaches to address these shortcomings as the deployment of VANET becomes more widespread. Nevertheless, these solutions could not address all the security and privacy shortcomings in VANET. Furthermore, the proposed approaches incur high costs in terms of computation due to the complexity involved in doing so sequentially. One of the significant approaches used in VANET security and privacy mitigation is identity-based schemes. This paper provides a comprehensive survey on VANETs and the entities involved, attack models, and an analysis of the security and privacy requirements for identity-based security and privacy schemes for VANETs.

Industry 4.0 as a driving force is making huge strides, particularly in the manufacturing sector, where all integral components involved in the production processes are getting digitally interconnected. Fused with improved automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity makes industrial systems increasingly vulnerable to cyber-attacks. While the impacts and intentions of cyber-attacks vary, they always have a detrimental effect on manufacturers, including financial losses, supply chain disruption, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. It is mainly designed to be utilized in a controlled and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight as it lacks security safeguards to protect against cyber-attacks. This paper proposes a digital signature-based security mechanism that offers authentication, integrity, and protection against cyber-attacks. The proposed mechanism is compared with the industry-standard SECS/GEM implementation in terms of processing time, payload overhead, and resilience against cyber-attacks. The results indicate that SECS/GEMsec effectively prevented untrusted entities from establishing communication links with legit industrial equipment while maintaining message integrity by discarding forged messages. Additionally, it protected SECS/GEM communications against Denial-of-Service (DoS) attacks, Replay attacks, and False-Data-Injection-Attack (FDIA) attacks.

Vehicle Ad hoc Networks (VANETs) are an emergent wireless communication technology that has the potential to reduce the risk of accidents caused by drivers and provide a wide range of entertainment facilities. Because of the nature of VANETs' open-access environment, security attacks can affect the messages broadcast by a vehicle. VANET is therefore vulnerable to security and privacy issues. Recently, many schemes for addressing these problems of VANET have been proposed. However, most of them are affected by massive computation overhead and security issues. In this paper, we propose a scheme named efficient conditional privacy preservation with mutual authentication to address the problems mentioned above in VANET. This scheme depends on the division of geographical areas into a number domains and their distribution, where each domain stores the Certificate Revocation List (CRL) in all Road-side Units (RSUs) located inside the domain. During the mutual authentication phase, the vehicle should authenticate with the TA. After the vehicle obtains a pool of pseudo-identities and the corresponding secret keys from RSU, it is allowed to transmit a message to the other components in the VANET. Because our scheme does not use the bilinear pairing, the performance evaluation shows that our scheme has a lower system cost in terms of computation and communication than other existing methods. Meanwhile, the proposed scheme reduces the computation costs of signing the message and verifying the message by 99.85% and 99.93%, respectively. While the proposed scheme reduces the communication costs of the message size by 13.3%.

Over the past years, vehicular ad hoc networks (VANETs) have been commonly used in intelligent traffic systems. VANET's design encompasses critical features that include autonomy, distributed networking, and rapidly changing topology. The characteristics of VANET and its implementations for road safety have attracted considerable industry and academia interest, particularly in research involving transport systems enhancement that could potentially save lives. Message broadcasting in an open access system, such as VANET, is the main and utmost challenging problem with regard to security and privacy in VANETs. Various studies on VANET security and privacy have been proposed. Nevertheless, none has considered overall privacy requirements such as unobservability. In order to address these shortcomings, we propose a VANET based privacy-preserving communication scheme (VPPCS), which meets the requirements for content and contextual privacy. It leverages elliptic curve cryptography (ECC) and an identity-based encryption scheme. We have carried out a detailed security analysis (burrows-abadi-needham (BAN) logic, random oracle model, security of proof, and security attributes) to validate and verify the proposed scheme. The analysis has shown that our scheme is secure and also shown to be effective in a performance evaluation. The proposed scheme does not only meet the previously mentioned security and privacy requirements, but also impervious to various types of attacks such as replay, impersonation, modification, and man-in-the-middle attacks.

Recently, Vehicle Ad Hoc Networks (VANETs) have been increasingly developed in Intelligent Transportation Systems (ITSs). However, VANETs are vulnerable to security issues because of the open-medium nature of Vehicle-To-Vehicle (V2V) and Vehicle-To-Infrastructure (V2I) communication. Recently, many studies have proposed security schemes to address these issues. However, many of these schemes have massive computational overheads, especially in the process of batch verification method, which verifies multiple messages simultaneously. In this article, a Lightweight Security Without Using Batch Verification Method (LSWBVM) scheme is proposed based on Elliptic Curve Cryptography (ECC). The proposed LSWBVM scheme uses the XOR operation and general hash function during mutual authentication. Meanwhile, to verify a large number of messages, the proposed scheme uses an efficient single verification instead of batch verification in a high traffic density-area. BAN logic proves that the LSWBVM achieves the security goals to mutually authenticate between the nodes. The security analysis indicates that the LSWBVM satisfies the security requirements, such as: identity privacy preserving, tractability and revocability; and secure non-forgery under the random oracle model in an adaptively chosen message attack. The preference evaluation shows that the single verification of proposed LSWBVM is more efficient when compared with batch verification of the related works.

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized the results of the SLR into five areas: (i) The different types of DDoS attack detection in ML/DL approaches; (ii) the methodologies, strengths, and weaknesses of existing ML/DL approaches for DDoS attacks detection; (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature; (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature; and (v) current research gaps and promising future directions.

Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are 0.0031, 2.0185 to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.

Vehicular fog computing enabled by the Fifth Generation (5G) has been on the rise recently, providing real-time services among automobiles in the field of smart transportation by improving road traffic safety and enhancing driver comfort. Due to the public nature of wireless communication channels, in which communications are conveyed in plain text, protecting the privacy and security of 5G-enabled vehicular fog computing is of the utmost importance. Several existing works have proposed an anonymous authentication technique to address this issue. However, these techniques have massive performance efficiency issues with authenticating and validating the exchanged messages. To face this problem, we propose a novel anonymous authentication scheme named ANAA-Fog for 5G-enabled vehicular fog computing. Each participating vehicle’s temporary secret key for verifying digital signatures is generated by a fog server under the proposed ANAA-Fog scheme. The signing step of the ANAA-Fog scheme is analyzed and proven secure with the use of the ProfVerif simulator. This research also satisfies privacy and security criteria, such as conditional privacy preservation, unlinkability, traceability, revocability, and resistance to security threats, as well as others (e.g., modify attacks, forgery attacks, replay attacks, and man-in-the-middle attacks). Finally, the result of the proposed ANAA-Fog scheme in terms of communication cost and single signature verification is 108 bytes and 2.0185 ms, respectively. Hence, the assessment metrics section demonstrates that our work incurs a little more cost in terms of communication and computing performance when compared to similar studies.

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an SVM-based DDoS detection model shows superior performance. This comparative analysis offers a valuable insight into the development of efficient and accurate techniques for detecting DDoS attacks in SDN environments with less complexity and time.

Both security and privacy are central issues and need to be properly handled because communications are shared among vehicles in open channel environments of 5G-enabled vehicular networks. Several researchers have proposed authentication schemes to address these issues. Nevertheless, these schemes are not only vulnerable to quantum attacks but also use heavy operations to generate and verify signatures of messages. Additionally, these schemes need an expensive component RoadSide Unit (RSU)-aided scheme during the joining phase. To address these issues, we propose a lightweight quantum-resistant scheme according to the lattice method in 5G-enabled vehicular networks. Our proposal uses matrix multiplication instead of operations-based bilinear pair cryptography or operations-based elliptic curve cryptography to generate and verify signatures of messages shared among vehicles. Our proposal satisfies a significant reduction in performance, which makes it lightweight enough to handle quantum attacks. Our proposal is based on 5G technology without using any RSU-aided scheme. Security analysis showed that our proposal satisfies privacy and security properties as well as resists quantum attacks. Finally, our proposal also shows favorable performance compared to other related work.

Due to emerging internet technologies that mostly depend on the decentralization concept, such as cryptocurrencies, cyber attackers also use the decentralization concept to develop P2P botnets. P2P botnets are considered one of the most serious and challenging threats to internet infrastructure security. Consequently, several open issues still need to be addressed, such as improving botnet intrusion detection systems, because botnet detection is essentially a confrontational problem. This paper presents PeerAmbush, a novel approach for detecting P2P botnets using, for the first time, one of the most effective deep learning techniques, which is the Multi-Layer Perceptron, with certain parameter settings to detect this type of botnet, unlike most current research, which is entirely based on machine learning techniques. The reason for employing machine learning/deep learning techniques, besides data analysis, is because the bots under the same botnet have a symmetrical behavior, and that makes them recognizable compared to benign network traffic. The PeerAmbush also takes the challenge of detecting P2P botnets with fewer selected features compared to the existing related works by proposing a novel feature engineering method based on Best First Union (BFU). The proposed approach showed considerable results, with a very high detection accuracy of 99.9%, with no FPR. The experimental results showed that PeerAmbush is a promising approach, and we look forward to building on it to develop better security defenses.

Mobile ad hoc networks (MANETs) are being used more and more in a variety of fields, including the environment, energy efficiency, smart transportation, intelligent agriculture, and in Internet of Things (IoT) ecosystems. They are also anticipated to play an increasingly significant role in the future of the Internet due to the strong evolution of wireless technology in recent years. Nevertheless, this inter-node communication is vulnerable to various security attacks such as Man–In-The-Middle (MITM) attacks, which are considered to be the main challenge in MANETs. This happens when a harmful node intercepts data shared by legal nodes. Therefore, the main goal of this work is to investigate the impact of attackers’ strategies to execute MITM assaults in MANETs, such as message-delayed and message-dropped assaults. The output of this work shows that these assaults have a severe impact on legal entities in MANETs as the network experiences a high number of compromised messages as well as high E2ED and PLD. Finally, by using symmetry or asymmetry cryptographies, our proposal will avoid MITM attacks that intercept the communication between legal nodes.