Araştırma Çıktıları / Research Outputs
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Browsing Araştırma Çıktıları / Research Outputs by Author "Abayomi-Alli, Olusola"
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Article Citation Count: 5An improved random bit-stuffing technique with a modified RSA algorithm for resisting attacks in information security (RBMRSA)(Cairo Univ, Fac Computers & information, 2022) Şengül, Gökhan; Misra, Sanjay; Mısra, Sanjay; Abayomi-Alli, Olusola; Sengul, Gokhan; Computer EngineeringThe recent innovations in network application and the internet have made data and network security the major role in data communication system development. Cryptography is one of the outstanding and powerful tools for ensuring data and network security. In cryptography, randomization of encrypted data increases the security level as well as the Computational Complexity of cryptographic algorithms involved. This research study provides encryption algorithms that bring confidentiality and integrity based on two algorithms. The encryption algorithms include a well-known RSA algorithm (1024 key length) with an enhanced bit insertion algorithm to enhance the security of RSA against different attacks. The security classical RSA has depreciated irrespective of the size of the key length due to the development in computing technology and hacking system. Due to these lapses, we have tried to improve on the contribution of the paper by enhancing the security of RSA against different attacks and also increasing diffusion degree without increasing the key length. The security analysis of the study was compared with classical RSA of 1024 key length using mathematical evaluation proofs, the experimental results generated were compared with classical RSA of 1024 key length using avalanche effect in (%) and computational complexity as performance evaluation metrics. The results show that RBMRSA is better than classical RSA in terms of security but at the cost of execution time. (C) 2022 THE AUTHORS. Published by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.Article Citation Count: 26A review of soft techniques for SMS spam classification: Methods, approaches and applications(Pergamon-elsevier Science Ltd, 2019) Mısra, Sanjay; Misra, Sanjay; Abayomi-Alli, Adebayo; Odusami, Modupe; Computer EngineeringBackground: The easy accessibility and simplicity of Short Message Services (SMS) have made it attractive to malicious users thereby incurring unnecessary costing on the mobile users and the Network providers' resources. Aim: The aim of this paper is to identify and review existing state of the art methodology for SMS spam based on some certain metrics: AI methods and techniques, approaches and deployed environment and the overall acceptability of existing SMS applications. Methodology: This study explored eleven databases which include IEEE, Science Direct, Springer, Wiley, ACM, DBLP, Emerald, SU, Sage, Google Scholar, and Taylor and Francis, a total number of 1198 publications were found. Several screening criteria were conducted for relevant papers such as duplicate removal, removal based on irrelevancy, abstract eligibility based on the removal of papers with ambiguity (undefined methodology). Finally, 83 papers were identified for depth analysis and relevance. A quantitative evaluation was conducted on the selected studies using seven search strategies (SS): source, methods/ techniques, AI approach, architecture, status, datasets and SMS spam mobile applications. Result: A Quantitative Analysis (QA) was conducted on the selected studies and the result based on existing methodology for classification shows that machine learning gave the highest result with 49% with algorithms such as Bayesian and support vector machines showing highest usage. Unlike statistical analysis with 39% and evolutionary algorithms gave 12%. However, the QA for feature selection methods shows that more studies utilized document frequency, term frequency and n-grams techniques for effective features selection process. Result based on existing approaches for content-based, non-content and hybrid approaches is 83%, 5%, and 12% respectively. The QA based on architecture shows that 25% of existing solutions are deployed on the client side, 19% on server-side, 6% collaborative and 50% unspecified. This survey was able to identify the status of existing SMS spam research as 35% of existing study was based on proposed new methods using existing algorithms and 29% based on only evaluation of existing algorithms, 20% was based on proposed methods only. Conclusion: This study concludes with very interesting findings which shows that the majority of existing SMS spam filtering solutions are still between the "Proposed" status or "Proposed and Evaluated" status. In addition, the taxonomy of existing state of the art methodologies is developed and it is concluded that 8.23% of Android users actually utilize this existing SMS anti-spam applications. Our study also concludes that there is a need for researchers to exploit all security methods and algorithm to secure SMS thus enhancing further classification in other short message platforms. A new English SMS spam dataset is also generated for future research efforts in Text mining, Tele-marketing for reducing global spam activities.Review Citation Count: 0A study of existing use case extensions and experience: a systematic review(Taylor & Francis Ltd, 2020) Mısra, Sanjay; Olotu, Samuel Ibukun; Abayomi-Alli, Olusola; Misra, Sanjay; Ikotun, Abiodun Motunrayo; Computer EngineeringUse case has embedded potentials that enable the diversity in its adoption to addressing other issues in software engineering. This objective of this paper is to gathered and classified use case researches carried out over the years and defines the state-of-art. We identified and analysed 62 relevant articles with deep insights into six different initiatives. The publications included 29 journal articles, 18 conference articles, 3 reports, 5 book chapters, 5 workshops and 2 symposiums. This study deduced that as many as 15 different extensions are proposed from use case ranging from formal descriptions to subjective methodology. Use case has evolved from initial functional requirement elicitation to a more formal and extensive structure. There are fewer publications in cloud computing and use case-based systems. This study contributed to the use case body of knowledge by assessing the extended initiatives and giving an overview of the challenges that are not yet resolved.Article Citation Count: 8A survey and meta-analysis of application-layer distributed denial-of-service attack(Wiley, 2020) Mısra, Sanjay; Misra, Sanjay; Abayomi-Alli, Olusola; Abayomi-Alli, Adebayo; Fernandez-Sanz, Luis; Computer EngineeringBackground One of the significant attacks targeting the application layer is the distributed denial-of-service (DDoS) attack. It degrades the performance of the server by usurping its resources completely, thereby denying access to legitimate users and causing losses to businesses and organizations. Aim This study aims to investigate existing methodologies for application-layer DDoS (APDDoS) attack defense by using specific measures: detection methods/techniques, attack strategy, and feature exploration of existing APDDoS mechanisms. Methodology The review is carried out on a database search of relevant literature in IEEE Xplore, ACM, Science Direct, Springer, Wiley, and Google Search. The search dates to capture journals and conferences are from 2000 to 2019. Review papers that are not in English and not addressing the APDDoS attack are excluded. Three thousand seven hundred eighty-nine studies are identified and streamlined to a total of 75 studies. A quantifiable assessment is performed on the selected articles using six search procedures, namely: source, methods/technique, attack strategy, datasets/corpus, status, detection metric, and feature exploration. Results Based on existing methods/techniques for detection, the results show that machine learning gave the highest proportion with 36%. However, assessment based on attack strategy shows that several studies do not consider an attack form for deploying their solution. Result based on existing features for the APDDoS detection technique shows request stream during a user session and packet pattern gave the highest result with 47%. Unlike packet header information with 33%, request stream during absolute time interval with 12% and web user features 8%. Conclusion Research findings show that a large proportion of the solutions for APDDoS attack detection utilized features based on request stream during user session and packet pattern. The optimization of features will improve detection accuracy. Our study concludes that researchers need to exploit all attack strategies using deep learning algorithms, thus enhancing effective detection of APDDoS attack launch from different botnets.