The employment of blockchain technology in electronic voting (e-voting) systems is attracting significant attention due to its ability to enhance transparency, security, and integrity in digital voting. This study presents an extensive review of the existing research on e-voting systems that rely on blockchain technology. The study investigates a range of key research concerns, including the benefits, challenges, and impacts of such systems, together with technologies and implementations, and an identification of future directions of research in this domain. We use a hybrid review approach, applying systematic literature review principles to select and categorize scientific papers and reviewing the technology used in these in terms of the above key concerns. In the 252 selected papers, aspects such as security, transparency, and decentralization are frequently emphasized as the main benefits. In contrast, although aspects like privacy, verifiability, efficiency, trustworthiness, and auditability receive significant attention, they are not the primary focus. We observed a relative lack of emphasis on aspects such as accessibility, compatibility, availability, and usability in the reviewed literature. These aspects, although acknowledged, are not as thoroughly discussed as the aforementioned key benefits in the proposed solutions for blockchain-based e-voting systems, whereas the considered studies have proposed well-structured solutions for blockchain-based e-voting systems focusing on how blockchain can strengthen security, transparency, and privacy, in particular, the crucial aspect of scalability needs attention.

Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (L-BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three well-known datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the I-FGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture.

We suggest a modified version of the nonnegative matrix factorization problem, adding penalty terms to the model with the aim of taking control of the condition number of the decomposition elements. This measure is taken to reinforce computational stability in the solution path of the relevant optimization model. Then, using the ellipsoid vector norm in the Barzilai–Borwein least‐squares model, we gain scaling parameters for the memoryless BFGS updating formula. Therefore, two augmented versions of the Oren–Spedicato self‐scaling parameter are proposed when the BFGS and DFP formulas for the Hessian are employed as the matrix index of the ellipsoid norm. A similar analysis is conducted to obtain two augmented versions of the Oren–Luenberger self‐scaling parameter using the mentioned quasi‐Newton formulas for the inverse Hessian. We address the global convergence of the method under a modified version of a nonmonotone backtracking line search. Then, we discuss accelerating the method using a local memoryless quadratic model considering the given scaling parameters as scalar approximations of the Hessian. Finally, the computational merits of the given augmented nonnegative matrix factorization model as well as the effectiveness of the proposed scaling parameters are assessed on some standard and randomly generated test problems, and also, a classic database of facial images. The outputs generally support our analytical spectrum.