Text steganography, the science of hiding secret messages in innocent-looking text documents ensures the secrecy of the embedded secret. Cryptography, on the other hand, encrypts and converts the secret message into a...
详细信息
In this paper, an evaluation strategy is proposed for evaluation of optimization algorithms, called the Complex Preference Analysis, that assesses the efficiency of different evolutionary algorithms by considering mul...
详细信息
Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computin...
详细信息
This paper addresses the underexplored landscape of chaotic functions in steganography, existing literature when examined under PRISMA-ScR framework it was realized that most of the studies predominantly focuses on ut...
详细信息
Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and...
详细信息
Wireless sensor networks (WSN) have seen immense use in everyday life, like health, battle-field administration, and disaster administration. Nodes inside WSN are more vulnerable to safety attacks like data replay and eavesdropping attacks. Node capture attacks function as destructive attacks that let attackers physically seize sensor nodes, reconfigure the structures, and deploy new nodes. An efficient architecture consists of a number of protocols for safe key creation and node capture attack revocation. A pairwise key establishment addresses arbitrary inputs from the pair of nodes implicated for the secure key establishment. Thus, the detailed exploration of various attack models to enhance key management security is a critical research direction in WSN security. Our model approaches the node capture attack problem from an attacker's viewpoint. The proposed model discovers the optimal collection of nodes likely to be attacked for node capturing. Based on the optimization algorithm i.e., fruit fly, the proposed model identifies multiple objectives like the set of dominating nodes, the vulnerability in paths, traveling cost, node contribution, and dominant rank and computes the optimal set of nodes with higher destructiveness. This indicates that the suggested node capture model has significant performance in the aspect of the least cost and lower attacking rounds. In this proposed model, we present an improved fruit fly optimization based attacking model consisting of several objectives as node strength, node and key participation rank, dominant rank and cost for capturing nodes in the system. Our approach outperforms existing attack models like RA, MLA, MTA, MKA, FGA, FFOA, and MA in terms of largest traffic compromised, lowest total attacking rounds, key captured, and least energy cost. The results demonstrated that the proposed method attained a path compromise probability up to 91% and reduced the cost by 60% in a network size of 100 nodes. The deduction in th
Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variabilit...
详细信息
Telerehabilitation is a cost-effective alternative to in-clinic rehabilitation. Although convenient, it lacks immersive and free-viewpoint patient visualization. Current research explores two solutions to this issue. ...
详细信息
Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and ...
详细信息
暂无评论