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Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain unauthorised network access without the users consent. Due to the increasing applications of computers and dependency on electronically saved private data, malware attacks on sensitive information have become a dangerous issue for individuals and organizations across the world. Hence, malware defense is critical for keeping our computer systems and data protected. Many recent survey articles have focused on either malware detection systems or single attacking strategies variously. To the best of our knowledge, no survey paper demonstrates malware attack patterns and defense strategies combinedly. Through this survey, this paper aims to address this issue by merging diverse malicious attack patterns and machine learning (ML) based detection models for modern and sophisticated malware. In doing so, we focus on the taxonomy of malware attack patterns based on four fundamental dimensions the primary goal of the attack, method of attack, targeted exposure and execution process, and types of malware that perform each attack. Detailed information on malware analysis approaches is also investigated. In addition, existing malware detection techniques employing feature extraction and ML algorithms are discussed extensively. Finally, it discusses research difficulties and unsolved problems, including future research directions., Comment: 30 pages, 9 figures, 7 tables, no where submitted yet

Blockchain (BC) and Software Defined Networking (SDN) are some of the most prominent emerging technologies in recent research. These technologies provide security, integrity, as well as confidentiality in their respective applications. Cloud computing has also been a popular comprehensive technology for several years. Confidential information is often shared with the cloud infrastructure to give customers access to remote resources, such as computation and storage operations. However, cloud computing also presents substantial security threats, issues, and challenges. Therefore, to overcome these difficulties, we propose integrating Blockchain and SDN in the cloud computing platform. In this research, we introduce the architecture to better secure clouds. Moreover, we leverage a distributed Blockchain approach to convey security, confidentiality, privacy, integrity, adaptability, and scalability in the proposed architecture. BC provides a distributed or decentralized and efficient environment for users. Also, we present an SDN approach to improving the reliability, stability, and load balancing capabilities of the cloud infrastructure. Finally, we provide an experimental evaluation of the performance of our SDN and BC-based implementation using different parameters, also monitoring some attacks in the system and proving its efficacy., Comment: 12 Pages 16 Figures 3 Tables

Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) are achieved., Comment: The paper has been accepted for publication in Computer Science journal: http://journals.agh.edu.pl/csci

Blockchain has recently been able to draw wider attention throughout the research community. Since its emergence, the world has seen the mind-blowing expansion of this new technology, which was initially developed as a pawn of digital currency more than a decade back. A self-administering ledger that ensures extensive data immutability over the peer-to-peer network has made it attractive for cybersecurity applications such as a sensor-enabled system called the Internet of things (IoT). Brand new challenges and questions now demand solutions as huge IoT devices are now online in a distributed fashion to ease our everyday lives. After being motivated by those challenges, the work here has figured out the issues and perspectives an IoT infrastructure can suffer because of the wrong choice of blockchain technology. Though it may look like a typical review, however, unlike that, this paper targets sorting out the specific security challenges of the blockchain-IoT eco-system through critical findings and applicable use-cases. Therefore, the contribution includes directing Blockchain architects, designers, and researchers in the broad domain to select the unblemished combinations of Blockchain-powered IoT applications. In addition, the paper promises to bring a deep insight into the state-of-the-art Blockchain platforms, namely Ethereum, Hyperledger, and IOTA, to exhibit the respective challenges, constraints, and prospects in terms of performance and scalability., Comment: 22 pages, 11 Figures, 7 Tables

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability., Comment: 12 pages, 13 Figures, 4 tables IEEE Transaction

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design. In the interim, Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. On the other hand, Blockchain technology is showing prominent possibilities with secured, dispersed, and auditable data recording and sharing schemes. In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs. Besides, probable issues, challenges, solutions, and future research directions for BC-Enabled FL frameworks for IoVs are also presented. This survey can further be used as the basis for developing modern BC-Enabled FL solutions to resolve different data privacy issues and scenarios of IoVs.