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Insider threats pose a significant challenge in cybersecurity, demanding advanced detection methods for effective risk mitigation. This paper presents a comparative evaluation of data imbalance addressing techniques for CNN-based insider threat detection. Specifically, we integrate Convolutional Neural Networks (CNN) with three popular data imbalance addressing techniques: Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE, and Adaptive Synthetic Sampling (ADASYN). The objective is to enhance insider threat detection accuracy and robustness in imbalanced datasets common to cybersecurity domains. Our study addresses the lack of consensus in the literature regarding the superiority of data imbalance addressing techniques in this field. We analyze a human behavior-based dataset (i.e., CERT) that reports users' Information Technology (IT) activities with a substantial number of samples to provide a clear conclusion on the effectiveness of these balancing techniques when coupled with CNN. Experimental results demonstrate that ADASYN, in conjunction with CNN, achieves a ROC curve of 96%, surpassing SMOTE and Borderline-SMOTE in enhancing detection accuracy in imbalanced datasets. We compare the results of these three hybrid models (CNN + imbalance addressing techniques) with state-of-the-art selective studies focusing on ROC, recall, and accuracy measures. Our findings contribute to the advancement of insider threat detection methodologies., (© 2024. The Author(s).)

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required., (© 2024. The Author(s).)

A dominating set of a graph [Formula: see text] is a subset U of its vertices V, such that any vertex of G is either in U, or has a neighbor in U. The dominating-set problem is to find a minimum dominating set in G. Dominating sets are of critical importance for various types of networks/graphs, and find therefore potential applications in many fields. Particularly, in the area of communication, dominating sets are prominently used in the efficient organization of large-scale wireless ad hoc and sensor networks. However, the dominating set problem is also a hard optimization problem and thus currently is not efficiently solvable on classical computers. Here, we propose a biomolecular and a quantum algorithm for this problem, where the quantum algorithm provides a quadratic speedup over any classical algorithm. We show that the dominating set problem can be solved in [Formula: see text] queries by our proposed quantum algorithm, where n is the number of vertices in G. We also demonstrate that our quantum algorithm is the best known procedure to date for this problem. We confirm the correctness of our algorithm by executing it on IBM Quantum's qasm simulator and the Brooklyn superconducting quantum device. And lastly, we show that molecular solutions obtained from solving the dominating set problem are represented in terms of a unit vector in a finite-dimensional Hilbert space., (© 2023. The Author(s).)