Detection of attacks in the computers and networks keeps being the pertinent and challenging area of researchers. Intrusion-Detection System is an essential technology of Network Security. Currently, Intrusion Detection still faces some challenges like huge amounts of data to process, high averages of false alarms and low detection rates especially in cloud environment which more vulnerable to attacks. This paper includes an overview of the intrusion-detection system and introduces to the reader some fundamental concepts of IDS work in cloud computing, also propose a new algorithm Fast Learning Network to work based on an intrusion detection.
<span>This article aims to provide an overview of cyber attack awareness and prevention in network security. This article discussed the different types of cyber attacks, current trends of cyber attacks, how to prevent cyber attacks and uum students' awareness of cyber attacks. First, we will go over the different types of cyber attack, current trend, impact of cyber attack and the prevention. The approach entailed comparing and observing the outcomes of 13 different papers. The survey's findings would demonstrate the results obtained after analyzing the data collection which are the questionnaire filled out by respondents after watching the cyber attack awareness video to improve awareness of students through the cyber attack. Depending on the outcome of this survey, we will have a better understanding of current students' knowledge and awareness of cyber attacks, allowing us to improve students' understanding of cyber threats and the necessity of cyber security.</span>
The unforeseeable demand for secure paradigm cannot be fulfilled by the arbitrary sequence generated by the linear feedback shift register, which means the generated sequence can't meet satisfy the unpredictable demand for secure paradigm. Tent chaotic equation combined with the linear property of Linear Feedback Shift Register (LFSR) has resulted in a novel arbitrary sequence generator having a lengthier and composite structure. An analysis of the LFSR output sequence's architecture when combined with Tent map has revealed similar conformity compared to the homologous set of the individual linear constituents. Furthermore, to ensure the reliability of using the proposed Pseudo Random Number Generator (PRNG) in secure algorithms, the generated output bits sequence has been subjected to statistical analysis by NIST test suite and the result of the generated sequence confirm the efficiency of the proposed generator. The speed of the proposed generator and the security in terms of key space has been evaluated which give a robustness against different attacks.
Mobile malware is a serious threat in mobile security. Malware detector is the primary tool and first defense to protect mobile device against mobile malware. The impact of malware is negative. It causes files to be corrupted and secret information being compromised. Nowadays malware writers try to avoid detection from malware detectors using several techniques such as polymorphism and metamorphism. In order to overcome malware, concern in mobile, we propose a new framework for malware detection which is signature based technique using pattern matching. This framework uses signature number and Secure Hash Algorithm (SHA) as signature in the detection process. Both features will be examined by sequence in order to detect leakage of information. As a result, a file that considered as suspicious or malware will be sent to cloud for analyzing and classification.
Graphics Processing Units (GPUs) have become increasingly popular nowadays, giving exciting computational resources for the low-cost gaming gadgets. GPUs are also well suited for scientific applications, allowing researchers to accelerate computations and to improve the precision of mathematical methods. Paper describes a new version of Visual Environment for Metamaterials Modelling (VEM2), which implements algorithms of computation of physical properties of metamaterials using GPUs of a desktop computer. This allows VEM2' users to make intensive calculations with cheap hardware equipment instead of expensive supercomputers and significantly increase the effectiveness of metamaterials design.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.