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In mobile edge computing, unmanned aerial vehicles (UAVs) equipped with computing servers have emerged as a promising solution due to their exceptional attributes of high mobility, flexibility, rapid deployment, and terrain agnosticism. These attributes enable UAVs to reach designated areas, thereby addressing temporary computing swiftly in scenarios where ground-based servers are overloaded or unavailable. However, the inherent broadcast nature of line-of-sight transmission methods employed by UAVs renders them vulnerable to eavesdropping attacks. Meanwhile, there are often obstacles that affect flight safety in real UAV operation areas, and collisions between UAVs may also occur. To solve these problems, we propose an innovative A*SAC deep reinforcement learning algorithm, which seamlessly integrates the benefits of Soft Actor-Critic (SAC) and A* (A-Star) algorithms. This algorithm jointly optimizes the hovering position and task offloading proportion of the UAV through a task offloading function. Furthermore, our algorithm incorporates a path-planning function that identifies the most energy-efficient route for the UAV to reach its optimal hovering point. This approach not only reduces the flight energy consumption of the UAV but also lowers overall energy consumption, thereby optimizing system-level energy efficiency. Extensive simulation results demonstrate that, compared to other algorithms, our approach achieves superior system benefits. Specifically, it exhibits an average improvement of 13.18% in terms of different computing task sizes, 25.61% higher on average in terms of the power of electromagnetic wave interference intrusion into UAVs emitted by different auxiliary UAVs, and 35.78% higher on average in terms of the maximum computing frequency of different auxiliary UAVs. As for path planning, the simulation results indicate that our algorithm is capable of determining the optimal collision-avoidance path for each auxiliary UAV, enabling them to safely reach their designated endpoints in diverse obstacle-ridden environments. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods. [ABSTRACT FROM AUTHOR]

Abstract With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

Abstract VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) devices into commercial or industrial buildings to create smart environments, such as Smart Buildings (SBs), has enabled real‐time data collection and processing to effectively manage building operations. Due to poor security design and implementation in IoT devices, SB networks face an array of security challenges and threats (e.g., botnet malware) that leverage IoT devices to conduct Distributed Denial of Service (DDoS) attacks on the Internet infrastructure. Machine Learning (ML)‐based traffic classification systems aim to automatically detect such attacks by effectively differentiating attacks from benign traffic patterns in IoT networks. However, there is an inherent accuracy‐efficiency tradeoff in network traffic classification tasks. To balance this tradeoff, we develop an accurate yet lightweight device‐specific traffic classification model. This model classifies SB traffic flows into four types of coarse‐grained flows, based on the locations of traffic sources and the directions of traffic transmissions. Through these four types of coarse‐grained flows, the model can extract simple yet effective flow rate features to conduct learning and predictions. Our experiments find the model to achieve an overall accuracy of 96%, with only 32 features to be learned by the ML model. [ABSTRACT FROM AUTHOR]

The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely, KDD Cup-99, NSL-KDD, WSN-DS, CICIDS 2017, and Drone, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries.

There is a significant surge in unmanned aerial vehicles' (UAVs) popularity with an ongoing increase in demand due to their multi-functional uses. The capacity of the UAVs to respond to society and industry needs account for their pervasiveness. UAVs can be strategically deployed to enhance network efficiency and support critical missions, including vital tasks like infrastructure monitoring operations. To be effective, a UAV must interact securely with its network's entities, such as ground control stations, other UAVs, air traffic control systems, and navigation satellite systems. UAVs are exposed to a dangerous and costly world of cyber dangers as a result of cyber technology and connections. The UAV and the Ground Control Station (GCS) exchange information using communication lines, which are vulnerable to cyber-attacks. As a result, detective, defensive, and preventative countermeasures are critical. The Micro Air Vehicle Link (MAVLink) protocol is a widely used lightweight communication protocol for enabling communication between UAVs and GCSs. It outlines communication exchanges between a GCS and a UAV. It carries information about the UAV's condition as well as commands for control from the GCS. Although widely used, the MAVLink protocol lacks sufficient security measures and is susceptible to various types of attacks, which can pose significant risks to public safety. UAV data should be encrypted because they are very sensitive. To give a high level of security, complex operation encryption techniques are developed. These mathematical operations bring forth issues with efficiency and power usage, though. One of these methods, the ChaCha cipher, recently gained popularity after Google used it in several applications. In the current study, a new stream cipher method with low-duty cycles is proposed for protecting data in UAVs and is compared with existing security algorithms on the basis of various factors. The research findings show that by including the suggested method in MAVLink, it is possible to maintain both message secrecy and battery life for a resource-constrained UAV. This can be done while using comparable amounts of memory and CPU and without affecting MAVLink's performance. [ABSTRACT FROM AUTHOR]

Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery. [ABSTRACT FROM AUTHOR]

This study explores the complex field of phone spoofing in the context of India's digital revolution, examining how people react to and perceive dishonest communication techniques. The study examines the interconnected dynamics of Knowledge of Call Spoofing, Perceived Threat, and Trust in Phone Communication, with a focus on the Indian context, where traditional values collide with rapid technological advancements. This study employs a mixed-methods approach, integrating both qualitative and quantitative data. Quantitative data is gathered through a structured survey questionnaire distributed to a demographically diverse sample, and qualitative insights are obtained through in-depth interviews and focus groups. The results show a strong and positive correlation between call spoofing knowledge and phone communication trust, indicating that knowledgeable users are more likely to have confidence in their phone interactions. These views are further shaped by demographic subtleties, which include differences in gender, age, education, and occupation. The results offer a basis for developing proactive and culturally aware approaches to boost user confidence, guaranteeing a safe and robust digital communication environment catered to the various demands of the enormous Indian populace. In addition to that, this research has real-world consequences for educators, technology companies, and governments who are involved in determining India's digital future. [ABSTRACT FROM AUTHOR]

A new digital key distribution and encryption system for broad use is presented. It offers affordable perfect security for in-transit digital communications and is quantum-resistant. It uses true random bits generated from vacuum quantum fluctuations in an optical electromagnetic field to protect the bit (key) distribution. This distribution process uses recorded quantum noise and works in any channel or combination of channels. The quantum noise, independently, creates truly random bits and cloaks the transmitted signals. The default encryption, with distilled keys from these transmitted bits, is an automated one-time-pad, with no need for couriers to replenish keys. The level of security is calculated. The coexistence of encryption and quantum computers raises multi-disciplinary problems, theoretical and practical. Challenges exist now, because “harvest now, decrypt later” is increasingly practiced. An answer for a quantum resistant key distribution is Quantum Key Distribution (QKD). It is purely quantum and offers unconditional security. However, many practical and technological aspects plague QKD: it is slow, distance limited, needs quantum channels and is very costly - not for broad commercial use. NIST is starting to offer encryption solutions believed to be quantum-resistant. They are based on deterministic computational complexities. Differently, the security of our solution rests primarily on non-deterministic features.

In the field of network security attack and defense, attackers frequently utilize network monitoring to analyze traffic features and obtain user privacy. Most defense methods employ feature-based traffic morphing techniques. However, the existing traffic camouflage method based on the Wasserstein Generative Adversarial Network (WGAN) exhibits limited defensive effectiveness, as the transformed traffic can still be detected due to the inherent limitations of the model and algorithm. In this paper, we propose a Wasserstein Generative Adversarial Network model with a Self-Attention mechanism (SA-WGAN) and adjust the parameter of the discriminator. Simultaneously, in the traffic generation algorithm, a constraint on padding packets was added: the Jaccard Index of the set of statistical features of the generated traffic must reach a threshold of 0.25, while ensuring the inclusion of three-way handshake packets to optimize the camouflage effect. To verify the camouflage effectiveness of the optimized defense method, we conduct a series of adversarial attacks. Experimental results show that the feature defense method based on SA-WGAN can significantly reduce the detection accuracy of monitored traffic. Compared to the feature defense method based on WGAN, it decreases the classification accuracy by 9.15% under the Panchenko attack, effectively enhancing the defensive capability and successfully increasing the difficulty for attackers to penetrate network traffic.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research.

The Internet of Things (IoT) has emerged as a revolutionary communication technology, enabling the connection of resource-limited devices to the Internet. These devices are deployed in various industrial control systems to remotely monitor and control industrial applications. However, the public Internet’s inherent vulnerability to malicious attacks poses a significant challenge to the secure operation of these systems. To address this challenge, a lightweight and efficient authentication framework, LEAF-IIoT, is proposed. LEAF-IIoT leverages authenticated encryption (AE) techniques to provide a multifaceted security solution encompassing confidentiality, authentication, and data integrity. It establishes a secure channel by exchanging messages between the user, gateway, and smart embedded device, culminating in the creation of a session key for secure data exchange. Rigorous security assessment confirms the robustness of LEAF-IIoT, while performance evaluation demonstrates its significantly lower computational cost and reduced communication overhead compared to existing frameworks. Despite these efficiencies, LEAF-IIoT continues to provide strong security features, ensuring the integrity and confidentiality of data exchanged in the IIoT context.

We consider a scenario where an unmanned aerial vehicle (UAV), a typical unmanned aerial system (UAS), transmits confidential data to a moving ground target in the presence of multiple eavesdroppers. Multiple friendly reconfigurable intelligent surfaces (RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV. We formulate an optimization problem to minimize the energy consumption of the UAV, subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold. We present an online planning method following the framework of model predictive control (MPC) to jointly optimize the motion of the UAV and the configurations of the RISs. The effectiveness of the proposed method is validated via computer simulations.

Confronting the challenges of securing communication in-vehicle wireless sensor networks demands innovative solutions, particularly as vehicles become more interconnected. This paper proposes a tailored communication security framework for in-vehicle wireless sensor networks, addressing both scientific and technical challenges through effective encryption methods. It segments the local vehicle network into independent subsystems communicating via encrypted and authenticated tunnels, enhancing automotive system safety and integrity. The authors introduce a process for periodic cryptographic key exchanges, ensuring secure communication and confidentiality in key generation without disclosing parameters. Additionally, an authentication technique utilizing the sender's message authentication code secures communication tunnels, significantly advancing automotive cybersecurity and interconnectivity protection. Through a series of steps, including key generation, sending, and cryptographic key exchange, energy costs were investigated and compared with DTLS and TLS methods. For cryptographic security, testing against brute-force attacks and analysis of potential vulnerabilities in the AES-CBC 128 encryption algorithm, HMAC authentication, and HKDF key derivation function were carried out. Additionally, an evaluation of the memory resource consumption of the DTLS and TLS protocols was compared with the proposed solution. This work is crucial for mitigating risks associated with in-vehicle communication compromises within smart cities. [ABSTRACT FROM AUTHOR]

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution. [ABSTRACT FROM AUTHOR]

As wireless communication scenarios grow more complicated, security issues are becoming increasingly prominent and severe. In the Internet of Things and vehicle-to-everything scenarios, conventional cryptographic technology faces numerous challenges. These include difficulties in secret key distribution and management, low update rates of secret keys, and vulnerability to quantum attacks. Physical layer secret key generation is considered a promising solution to security issues. The perfect secrecy proposed by Shannon can be achieved by combining secret key generation and the one-time pad when the length of secret keys is equal to that of plaintext. Hence, it is important to increase secret key generation rates. Intelligent reflecting surfaces demonstrate great advantages in improving the secret key generation performance. This paper provides a comprehensive review of current research efforts related to secret key generation assisted by intelligent reflecting surfaces, which is divided into three main categories: introducing the randomness of intelligent reflecting surfaces, optimizing the reflecting coefficients, and designing probing protocols. Comparative results of existing optimization approaches are provided and discussed. Furthermore, we emphasize the significance of selecting a random source of secret key generation from the perspective of information theory. Finally, two significant application scenarios, the Industrial Internet of Things and vehicle-to-everything, are discussed, and some challenges and opportunities are presented. [ABSTRACT FROM AUTHOR]

The ever expanding digital space and government initiatives like Digital India have increased connectivity, digitization, remote employment which empowered us with technology and made our lifestyle easy and fast but as digitization is enhancing, cyberattacks are proportionally growing. In the initial stage of this study, it was observed from the data analysis and it was evident that women are particularly a soft target of many cybercriminals and cyber fraudsters. Women feel insecure in cyberspace, hence there is a need to dive deeper into our understanding and statistics of cybercrimes against women in India. It becomes necessary to determine the factors that have led to the clear surge of such crimes in recent years. In recent studies no such analysis is done focusing on geographical factors and top most cybercrime types committed against women. Therefore, in this article, prediction for cybercrime trends against women was performed using statistical tools and techniques to provide a better insight into the current scenario revolving around cybercrimes and women. A regressive statistical analysis of cybercrime data of all states of India to understand the current trend of cybercrimes, identification of the most vulnerable states of India and specific cybercrime with their percentage in whole, also where cybercrime afflicted women stand in those numbers. Further, for preventive measures a robust guidelines is proposed to combat cybercrimes for a better future. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive analysis of contemporary scholarship pertaining to instant messaging (IM) user behavior and security protocols. Through meticulous selection, the authors highlight critical studies that illuminate optimized message consumption strategies and delve into the evolving landscape of IM security models. Focusing on the past four years, the review meticulously dissects cutting-edge advancements in this domain. A significant insight emerges: achieving optimal communication security necessitates the synergistic convergence of three fundamental techniques: end-to-end encryption for data confidentiality, decentralized authentication for independent user verification, and zero-knowledge proof for identity obscurity. The review postulates that the simultaneous integration of these elements within the application architecture is paramount for robust privacy and heightened security in the realm of IM. [ABSTRACT FROM AUTHOR]

Due to recent advancements in mobile and wireless technologies, many mobile-based applications have received greater attention. Users can use their mobile devices to access various web services via the Internet from any location at any time. Hence, security becomes a critical issue in wireless communications because of the open nature of the network. Over the last two decades, many researchers have proposed various authentication protocols for mobile devices to ensure safe communication. These protocols follow either two party architecture or three party architecture. Most of these protocols are based on discrete logarithms or integer factorization problems, which are solvable in polynomial time algorithms for quantum computers. As a result, authenticated key agreement (AKA) schemes based on factorization and discrete logarithms are not secure in post-quantum environments. Thus, analyzing and designing AKA schemes for the quantum environment is required. We propose two party authenticated key agreement scheme for mobile devices based on ring learning with error problems. The proposed AKA scheme security is based on hard lattice problems. The security of the proposed design is analyzed and proved in the random oracle model. Moreover, performance evaluation and comparative study are also done to understand the proposed design's usefulness. [ABSTRACT FROM AUTHOR]

In the 5G era, the development of communication and computing in the Internet of vehicles has been limited by the rapidly increasing amount of information.New breakthroughs in communication and computing in Internet of vehicles can be achieved by applying AI algorithms to the Internet of vehicles.Firstly, the application of AI algorithms in communication security, communication resource allocation, computation resource allocation, task offloading decision, server deployment, communication-computation integration were investigated.Secondly, the achievements and shortcomings of the present AI algorithms in different scenarios were analyzed.Finally, combined with the Internet of vehicle development trend, some future research directions for AI algorithms applied in the Internet of vehicles were discussed.

Voting allows the people to elect their representative and express their preferences. In modern day democracy, voting is one fundamental and most important tool for election. To strengthen this process, efforts must be made to achieve a confirmable and transparent voting system. Naturally, the veracity of the election process is of ultimate importance for the honor of the democracy itself. The online voting or e‐voting system is a cost‐effective procedure, which saves a lot of money and time spent for organizing the election. However, security, integrity of data and privacy of the voter are the increasing concerns in this context. In this paper, we have done a thorough literature survey of the various latest schemes in this context and found that the schemes prone to several security and privacy threats. Moreover, a major issue with all these schemes is that they are inefficient in terms of computation and communication overheads. With this motivation, we present a novel and efficient e‐voting scheme to tackle the security and privacy concerns. The proposed scheme is designed using the concept of blind signature, anonymous channel and trust worthy entities where elliptic curve cryptography (ECC) works as a backbone. The proposed scheme is formally evaluated using well known AVISPA simulation tool, which simulates the attack model using CL‐AtSe as well as OFMC backend according to Dolev‐Yao threat model. In both the cases, all the possible security threats are being nullified by the proposed scheme and results in safe communication. Also, the scheme is analyzed against all the possible attacks that jeopardize the integrity of the existing voting systems. Moreover, the proposed scheme is also compared with other prevailing schemes in terms of computation and communication overheads and found more efficient. [ABSTRACT FROM AUTHOR]

Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

In order to achieve the requirement for security and lightweight in unmanned aerial vehicle(UAV)communication,two authentication and key agreement protocols targeted for UAVs with different computational performance are proposed,including an ECC based protocol,DroneSec,and a symmetric cipher based protocol,DroneSec-lite.The two protocols achieve secure mutual authentication and key configuration between ground stations and UAVs.DroneSec protocol achieves relatively low computational overhead while ensuring forward secrecy through combining ECDH and MAC,which is suitable for relatively high-performance platforms.DroneSec-lite protocol achieves extremely low computational overhead through using only symmetric ciphers,which is suitable for low-performance platforms.The security of the proposed protocols under the enhanced Dolve-Yao model is verified using ProVerif,a formal protocol verification tool,and the performance of the protocols is analyzed in the simulation environment.The results show that it is superior to existing protocols in terms of computation overhead,communication overhead and security.

The emerging technology that is vehicular platooning is an exciting technology. It promises to save space on congested roadways, improve safety and utilise less fuel for transporting goods, reducing greenhouse gas emissions. The technology has already been shown to be vulnerable to attack and exploitation by attackers. Attackers have several attack surfaces available for exploitation to achieve their goals (either personal or financial). The goal of this paper and its contribution to the area of research is to present the attacks and defence mechanisms for vehicular platoons and put risks of existing identified attacks forwards. Here the variety of attacks that have been identified in the literature are presented and how they compromise the wireless communications of vehicle platoons. As part of this, a risk assessment is presented to assess the risk factor of the attacks. Finally, this paper presents the range of defence and countermeasures to vehicle platooning attacks and how they protect the safe operations of vehicular platoons. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In resource-constrained devices such as Subscriber Identity Module (SIM), the possibility of using authentication and key exchange methods, which include the mutual verification of two parties to ensure the security of communication, provides a significant advantage. As the cost efficient computations may not be efficiently performed on resource-constrained devices, security is the main problem for SIMs. With the advancing technology and the use of quantum computers, it is predicted that this may lead to threats. It is a well-known fact that security on traditional public key cryptosystems will become vulnerable by using quantum computers due to Shor’s algorithm. In this paper, two different resistant to quantum attacks structures are proposed to ensure secure communication between SIM and service providers. In the proposed methods, Advanced Encryption Standard (AES-256) is used for communication with resource-constrained devices, and N-th degree Truncated polynomial Ring Units (NTRU) encryption system is used for communication with servers. Two methods are proposed, one with the private key and one without the private key in the production phase. This protocol provides authentication, data privacy and integrity for post-quantum SIM cards. The proposed method is inspired by the FLAT (Federated Lightweight Authentication) protocol. However, the main difference from the FLAT protocol is that it has more nodes and is resistant to quantum attacks.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores. [ABSTRACT FROM AUTHOR]