Harmony Search for Security Enhancement

doi:10.58496/mjcs/2021/002

Cited by 6 publications

(2 citation statements)

References 13 publications

(13 reference statements)

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“…However, deep learning measures the performance of systems in addressing a problem, whether the examples are one-or multi-dimensional. Scholars are actively trying to find solutions to many issues in the field of cybersecurity by using deep learning methods [85][86][87]. These methods obtain a very high accuracy of more than 97% in identifying attack messages and blocking their display on vehicle screens.…”

Section: Deep Learning In Cybersecuritymentioning

confidence: 99%

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

2023

ijcsm

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People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software.

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Section: Deep Learning In Cybersecuritymentioning

confidence: 99%

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

2023

ijcsm

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“…Since it takes into consideration boundless downloads of pilfered software, open-source codes, and advances and markets pilfered duplicates, it represents a serious risk to internet security. Every year, it develops rapidly and makes the software area experience huge monetary misfortunes [7]. As indicated by the Business Software Partnership (BSA) 2016 exploration, there is an expected 39% public software robbery rate, which causes yearly financial misfortunes of up to $52.2 billion.…”

Section: Introductionmentioning

confidence: 99%

Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units

Bajao

Sarucam

2023

MJCS

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Security for IoT gadgets is an undertaking that has been made more troublesome by the far-reaching utilization of network safety in different applications, including wise modern frameworks, homes, individual devices, and vehicles. The fact that has been introduced makes deep learning for interruption recognition one productive security method. I thought about a few relevant systematic reviews that had already been written. Recent systematic reviews may include older and more recent works on the subject. For better IoT security, late exploration has focused on improving deep learning calculations. The ideal methodology for carrying out interruption recognition in the Internet of Things is determined by looking at the exhibition of different deep learning executions and investigating interruption location techniques that utilise them. Convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs) are the deep learning models used in this review. A standard dataset for IoT interruption identification is considered to evaluate the proposed model. The practical information is then investigated and diverged from current IoT interruption discovery strategies. In contrast with currently utilized approaches, the recommended strategy seems to have the best precision.

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A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

et al. 2023

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In VLSI circuit design, physical design is one of the main steps in placing the circuit into the chip area. Floorplanning is a crucial step in the physical design of IC design, which generates a blueprint for the placement of circuit modules into the chip. A floorplanning step accepts a netlist as its input, given by the circuit-partitioning step of physical design. The floorplanning step generates optimal placements for the circuit modules. The netlist contains the modules' dimensions, size, and interconnect information. During the floorplan generation, the chip area, wire length required for connecting modules and the heat generated by the chips can be estimated. A good floorplan makes placements and routing simple. In order to improve the circuit performance by minimizing chip area, wire length, and peak temperature, it is essential to generate an optimized floorplan by developing metaheuristic optimization algorithms. A novel Hybridized Multicriteria Ant Colony and Firefly Optimization (HMAC-FO) technique is introduced to generate an optimized floorplan. The primary focus of HMAC-FO technique is to design a model for generating efficient floorplanning. The standard MCNC benchmark dataset has a number of modules with their connections. Firefly optimization is the main algorithm in HMAC-FO, which has been used in generating efficient floorplan with the optimized area, wire length and thermal. The firefly algorithm initially requires some number of solutions as the fireflies' population. In the firefly algorithm, usually, the populations are generated randomly. But, to improve the firefly optimization algorithm's performance and obtain better optimum results, the proposed technique uses ACO to generate the initial population, which are all optimal solutions. The firefly algorithm uses the set of optimal solutions as the initial population and generates the globally optimal result. The proposed technique has experimented with the standard MCNC benchmark circuits, and the results prove that the proposed algorithm reduces the area by 3.48%, the wire length by 0.64% and the temperature by 3.33% than the best existing methodology. The proposed methodology generates a good floorplan with all the required optimization, such as area, wire length and heat generation.

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Harmony Search for Security Enhancement

Cited by 6 publications

References 13 publications

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

Threats Detection in the Internet of Things Using Convolutional neural networks, long short-term memory, and gated recurrent units

A Novel Multicriteria Optimization Technique for VLSI Floorplanning Based on Hybridized Firefly and Ant Colony Systems

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