The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.
相似文献With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.
相似文献Congestion control issues have received consistent attention from several telecommunication researchers and practitioners for many years because network congestion often causes significant packet loss and delay problems. Although many suggested methods for these issues and problems can be available in telecommunication literature, there is room for improvement. This paper's primary objective is to propose a dynamic weighting method in routing to reduce congestion during peak hours by predicting incoming traffics and optimizing weights in the adjacency matrix of the routing table online. While changing weight values in the routing table, this method implicitly changes the routing path of many nodes through the congested node. As a result, the packet losses and delays can be decreased in used nodes. The main steps of this proposed framework are fourfold. First, the candidate weighing list is calculated based on the routing table of the congested node. Second, a proposed auto-adaptive framework (AAF) is used to predict congestion levels and apply the weighing list in the network node. Third, the evaluation of network congestion is then performed by the probability of packet losses due to overflowing buffers. Finally, the case study results demonstrate that the proposed AAF framework can significantly reduce network congestion.
相似文献Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.
相似文献This paper presents two levels of enhancing the basic Moth flame optimization (MFO) algorithm. The first step is hybridizing MFO and the local-based algorithm, hill climbing (HC), called MFOHC. The proposed algorithm takes the advantages of HC to speed up the searching, as well as enhancing the learning technique for finding the generation of candidate solutions of basic MFO. The second step is the addition of six popular selection schemes to improve the quality of the selected solution by giving a chance to solve with high fitness value to be chosen and increase the diversity. In both steps of enhancing, thirty benchmark functions and five IEEE CEC 2011 real-world problems are used to evaluate the performance of the proposed versions. In addition, well-known and recent meta-heuristic algorithms are applied to compare with the proposed versions. The experiment results illustrate that the proportional selection scheme with MFOHC, namely (PMFOHC) is outperforming the other proposed versions and algorithms in the literature.
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