Computer Software and Media Applications


2578-1669 (Online)

Journal Abbreviation:

Comput. Soft. Media. Appl.

Computer Software and Media Applications (CSMA) is a peer-reviewed, open-access publication that delivers high-quality journal papers that are significant in all disciplines of computer software and media applications. With a focus on publishing high quality academic papers, detailing the latest information and research in the field of computer software and media applications. CSMA aims to provide a communication and information exchange platform for a broad audience of professionals. Articles from related fields that are interesting to a broad readership are particularly welcome.


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Computer Software and Media Applications is an Open Access Journal under EnPress Publisher. All articles published in Computer Software and Media Applications are accessible electronically from the journal website without commencing any kind of payment. In order to ensure contents are freely available and maintain publishing quality, Article Process Charges (APCs) are applicable to all authors who wish to submit their articles to the journal to cover the cost incurred in processing the manuscripts. Such cost will cover the peer-review, copyediting, typesetting, publishing, content depositing and archiving processes. Those charges are applicable only to authors who have their manuscript successfully accepted after peer-review.

Journal TitleAPCs
Computer Software and Media Applications$800

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Vol 7, No 1 (2024)

Table of Contents

Open Access
Original Research Article
Article ID: 5224
by S. Sengamala Barani, R. Durga
Comput. Soft. Media. Appl. 2024 , 7(1);    68 Views
Abstract Block chain technology is regarded for enhancing the characteristics of security because of decentralized design, safe distributed storage, and privacy. However, in recent times the present situation of block chain technology has experienced some crisis that may delay the quick acceptance and utilization in real-time applications. To conquer this subdues, a blockchain based system for attack detection and mitigation with Deep Learning (DL) named Fractional Tasmanian Devil Harris Optimization_Zeiler and Fergus network (FTDHO_ZFNet) is introduced. In this investigation, the entities utilized are owner, block chain, server, trusted authority and user. Here, authentication phase is done by means of Ethereum block chain by Key Exchange module and privacy preserved data sharing and communication is also done. Then, recorded log file creation is executed by the below mentioned stages. At first, a log file is generated with the basis of communication to record the events. After wards, the features are extracted by BoT-IoT database. Then, feature fusion is done by overlap coefficient utilizing Deep Q-Network (DQN). Moreover, data augmentation (DA) is doneusing bootstrapping method. At last, attack detection is observed by ZFNet tuned by FTDHO. Here, FTDHO is unified by Fractional Tasmanian Devil Optimization (FTDO) and Harris Hawks Optimization (HHO). Additionally, FTDO is integrated by Fractional Calculus (FC) concept and Tasmanian devil optimization (TDO). Furthermore, attack mitigation is performed. The performance measures applied for FTDHO_ZFNet are accuracy, and True Negative rate (TNR), observed supreme values with 92.9%, 93.8% and 92.9%.
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