The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices’ power usage. Also, a rand order code (ROC) technique is used with SNN to detect cyber-attacks. The proposed method is evaluated by comparing its performance with two other methods: IDS-DNN and IDS-SNNTLF by using three performance metrics: detection accuracy, latency, and energy usage. The simulation results have shown that IDS-SNNDT attained low power usage and less latency in comparison with IDS-DNN and IDS-SNNTLF methods. Also, IDS-SNNDT has achieved high detection accuracy for cyber-attacks in contrast with IDS-SNNTLF.
A legal discourse in the Qur’an and Sunnah is almost devoid of the use of one of the general formulas, and due to its frequent rotation in the tongue of the legislator, the formulas may overlap their members in apparently contradictory provisions, which makes the individual from the general members appear to the beholder to be covered by two contradictory provisions, and this research came to present what might happen to him The legal text interpreter of weighting between the two opposing texts is the strength of the generality that is established by the generality formula, so the two strongest formulas in the inclusion of its members outweigh the weaker of them and precede them, and the research decided that the formulas vary
... Show MoreAnomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the
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Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a
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Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local
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A newly developed analytical method characterized by its speed and sensitivity for the determination of mefenamic acid (MFA) in pure and pharmaceutical preparation is established via turbidimetric measurement (0-180o) by Ayah 6SX1-ST-2D Solar cell CFI Analyser . The method was based on the reaction of
phosphomolybdic acid with mefenamic acid in aqueous medium to form blue color precipitate as an ion-pair complex . Turbidity was measured via the reflection of incident light that collides on the surface precipitated particles at 0-180o . The chemical and physical parameters were studied and optimized. The calibration graph was linear in the range of 0.3-7 or 0.3-10 mMol.L-1, with correlation coefficient r = 0.9907 or 0.9556 respectively
The researchers seek to shed light on the importance of accounting disclosure on social responsibility, and the research aims to provide a theoretical approach to social accounting and its disclosure, identify the concept of sustainable development, highlight the theoretical foundations of sustainable development and employ disclosure of social responsibility towards achieving the goals of sustainable development, and identify the impact of The accounting disclosure on social responsibility in achieving sustainable development goals, and the research problem can be reviewed by asking the following question (Does the accounting disclosure of the social responsibility of economic units contribute to achieving sustainable developmen
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