Search Results (9)

Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%. Full article

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively. Full article

The Internet of Things or “IoT” determines the highly interconnected network of heterogeneous devices where each type of communication seems to be possible, even unauthorized. Consequently, the security requirement for these networks became crucial, while conventional Internet security protocol was identified as unusable in these types of networks, especially because of some classes of IoT devices with constrained resources. Secure group communication (SGC) in the IoT environment is vital to ensure the confidentiality, integrity, and availability (CIA) of data swapped within a collection of IoT devices. Typically, IoT devices were resource-constrained with limited memory, processing, energy, and power, which makes SGC a difficult task. This article designs a Fully Homomorphic Encryption with Optimal Key Generation Secure Group Communication (FHEOKG-SGC) technique in the IoT environment. The presented FHEOKG-SGC technique mainly focuses on the encryption and routing of data securely in the IoT environment via group communication. To accomplish this, the presented FHEOKG-SGC technique initially designs an FHE-based encryption technique to secure the data in the IoT environment. Next, the keys in the FHE technique are chosen optimally using the sine cosine algorithm (SCA). At the same time, the plum tree algorithm (PTA) is applied for the identification of the routes in the IoT network. Finally, the FHEOKG-SGC technique employs a trust model to improve the secure communication process, and the key management center is used for optimal handling of the keys. The simulation analysis of the FHEOKG-SGC technique is tested using a series of experiments, and the outcomes are studied under various measures. An extensive comparative study highlighted the improvement of the FHEOKG-SGC algorithm over other recent approaches. Full article

With the advent of several new means of communication, safeguarding the confidentiality of messages has become more crucial. Financial institutions, virtual currencies, and government organizations are all examples of high-risk contexts where information exchanges need particular care. The importance of data security in preventing unauthorized access to data is emphasized. Several cryptographic methods for protecting the secrecy and integrity of data were compared. In this research, the proposed work includes a new Turbo Code-based encryption algorithm. The Turbo encoder’s puncturing process is controlled by a secret key, and a typical random sequence is generated to encrypt the data and fix any mistakes. Key generation utilizing pre-existing data eliminates the requirement for sending keys over a secure channel. Using recurrence relations and the Lower–Upper (LU) decomposition method, the presented study suggests a novel approach to message encryption and decryption. The resulting encrypted grayscale image has a very high level of security, with an entropy of 7.999, a variation from perfection of 0.0245, and a correlation of 0.0092 along the diagonal, 0.0009 along the horizontal, and −0.0015 along the vertical. Directly decrypted pictures have a Peak Signal-to-Noise Ratio (PSNR) of 56.22 dB, but the suggested approach only manages an embedding capacity of 0.5 bpp (bits per pixel). This may be achieved by decreasing the size of the location map by only 0.02 bpp. Full article

Healthcare comprises the largest revenue and data boom markets. Sharing knowledge about healthcare is crucial for research that can help healthcare providers and patients. Several cloud-based applications have been suggested for data sharing in healthcare. However, the trustworthiness of third-party cloud providers remains unclear. The third-party dependency problem was resolved using blockchain technology. The primary objective of this growth was to replace the distributed system with a centralized one. Therefore, security is a critical requirement for protecting health records. Efforts have been made to implement blockchain technology to improve the security of this sensitive material. However, existing methods depend primarily on information obtained from medical examinations. Furthermore, they are ineffective for sharing continuously produced data streams from sensors and other monitoring devices. We propose a trustworthy access control system that uses smart contracts to achieve greater security while sharing electronic health records among various patients and healthcare providers. Our concept offers an active resolution for secure data sharing in mobility computing while protecting personal health information from potential risks. In assessing existing data sharing models, the framework valuation and protection approach recognizes increases in the practicality of lightweight access control architecture, low network expectancy, and significant levels of security and data concealment. Full article

License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user’s license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys. Full article

Since the development of information systems during the last decade, cybersecurity has become a critical concern for many groups, organizations, and institutions. Malware applications are among the commonly used tools and tactics for perpetrating a cyberattack on Android devices, and it is becoming a challenging task to develop novel ways of identifying them. There are various malware detection models available to strengthen the Android operating system against such attacks. These malware detectors categorize the target applications based on the patterns that exist in the features present in the Android applications. As the analytics data continue to grow, they negatively affect the Android defense mechanisms. Since large numbers of unwanted features create a performance bottleneck for the detection mechanism, feature selection techniques are found to be beneficial. This work presents a Rock Hyrax Swarm Optimization with deep learning-based Android malware detection (RHSODL-AMD) model. The technique presented includes finding the Application Programming Interfaces (API) calls and the most significant permissions, which results in effective discrimination between the good ware and malware applications. Therefore, an RHSO based feature subset selection (RHSO-FS) technique is derived to improve the classification results. In addition, the Adamax optimizer with attention recurrent autoencoder (ARAE) model is employed for Android malware detection. The experimental validation of the RHSODL-AMD technique on the Andro-AutoPsy dataset exhibits its promising performance, with a maximum accuracy of 99.05%. Full article

Soon after the coronavirus disease 2019 pandemic was proclaimed, digital health services were widely adopted to respond to this public health emergency, including comprehensive monitoring technologies, telehealth, creative diagnostic, and therapeutic decision-making methods. The World Health Organization suggested that artificial intelligence might be a valuable way of dealing with the crisis. Artificial intelligence is an essential technology of the fourth industrial revolution that is a critical nonmedical intervention for overcoming the present global health crisis, developing next-generation pandemic preparation, and regaining resilience. While artificial intelligence has much potential, it raises fundamental privacy, transparency, and safety concerns. This study seeks to address these issues and looks forward to an intelligent healthcare future based on best practices and lessons learned by employing telehealth and artificial intelligence during the COVID-19 pandemic. Full article

The demand for energy due to the population boom, together with the harmful consequences of fossil fuels, makes it essential to explore renewable thermal energy. Solar Thermal Systems (STS’s) are important alternatives to conventional fossil fuels, owing to their ability to convert solar thermal energy into heat and electricity. However, improving the efficiency of solar thermal systems is the biggest challenge for researchers. Nanomaterial is an effective technique for improving the efficiency of STS’s by using nanomaterials as working fluids. Therefore, the present theoretical study aims to explore the thermal energy characteristics of the flow of nanomaterials generated by the surface gradient (Marangoni convection) on a disk surface subjected to two different thermal energy modulations. Instead of the conventional Fourier heat flux law to examine heat transfer characteristics, the Cattaneo–Christov heat flux (Fourier’s heat flux model) law is accounted for. The inhomogeneous nanomaterial model is used in mathematical modeling. The exponential form of thermal energy modulations is incorporated. The finite-difference technique along with Richardson extrapolation is used to treat the governing problem. The effects of the key parameters on flow distributions were analyzed in detail. Numerical calculations were performed to obtain correlations giving the reduced Nusselt number and the reduced Sherwood number in terms of relevant key parameters. The heat transfer rate of solar collectors increases due to the Marangoni convection. The thermophoresis phenomenon and chaotic movement of nanoparticles in a working fluid of solar collectors enhance the temperature distribution of the system. Furthermore, the thermal field is enhanced due to the thermal energy modulations. The results find applications in solar thermal exchanger manufacturing processes. Full article