Browsing by Author "Odusami, Modupe"
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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.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.