Articles
International Journal of Business Intelligence and Data Mining (17438195)23(2)pp. 150-165
Due to the importance of identifying influential nodes in different applications, many methods have been proposed for it. Some of them are not accurate enough or have high temporal complexity. In this paper, a method named new GLS (NGLS) is developed based on the global and local search (GLS) algorithm. GLS, despite its high accuracy compared to other methods is not fast and efficient enough. NGLS is developed to improve the efficiency and scalability of GLS. To reach this goal, the number of common neighbours of each node is counted only up to a radius of two. The execution time of NGLS on average has been reduced by 85% in real-world networks and 97% on simulated networks, while the accuracy of NGLS is the same as GLS accuracy. Therefore, NGLS is applicable for larger real-world networks. © 2023 Inderscience Enterprises Ltd.
International Journal of Computer Network and Information Security (20749104)11(9)pp. 9-17
Malware poses one of the most serious threats to computer information systems. The current detection technology of malware has several inherent constraints. Because signature-based traditional techniques embedded in commercial antiviruses are not capable of detecting new and obfuscated malware, machine learning algorithms are applied in identifing patterns of malware behavior through features extracted from programs. There, a method is presented for detecting malware based on the features extracted from the PE header and section table PE files. The packed files are detected and then unpacke them. The PE file features are extracted and their static features are selected from PE header and section tables through forward selection method. The files are classified into malware files and clean files throughs different classification methods. The best results are obtained through DT classifier with an accuracy of 98.26%. The results of the experiments consist of 971 executable files containing 761 malware and 210 clean files with an accuracy of 98.26%. © 2019 MECS.
International Journal of Innovative Technology and Exploring Engineering (discontinued) (22783075)8(11)pp. 4258-4265
In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works. © BEIESP.
Bateni, M.,
Baraani, A.,
Ghorbani, A.A.,
Rezaei, A. International Journal of Innovative Computing, Information and Control (13494198)9(1)pp. 231-255
There are many different approaches to alert correlation such as using correlation rules and prerequisite-consequences, using machine learning and statistical methods and using similarity measures. In this paper, iCorrelator, a new AIS-inspired architecture, is presented. It uses a three-layer architecture that is inspired by three types of responses in the human immune system: the innate immune system's response, the adaptive immune system's primary response, and the adaptive immune system's secondary response. In comparison with other correlators, iCorrelator does not need information about different attacks and their possible relations in order to discover an attack scenario. It uses a very limited number of general rules that are not related to any specific attack scenario. A process of incremental learning is used to encounter new attacks. Therefore, iCorrelator is easy to set up and work dynamically without reconfiguration. As a result of using memory cells and improved alert selection policy, the computational cost of iCorrelator is also acceptable even for online correlation. iCorrelator is evaluated by using the DARPA 2000 dataset and a netForensics honeynet data. The completeness, soundness, false correlation rate and execution time are reported. Results show that iCorrelator is able to extract the attack graphs with acceptable accuracy that is comparable to the best known solutions. © 2013 ICIC International.
