Software Engineering, Intelligent Systems And Information Security
Electronics and Computer Engineering
At the Electronics And Computer Engineering department office
Appointment on Visitation important
Topic: Software Systems Engineering, Intelligent And Secure Software Systems
Software engineering is an applied discipline that aims to employ scientific and engineering principles, theories and methodologies to design and develop software systems that addresses human challenges. Generally, my interest in software systems engineering research relates to requirement analysis, design and methodologies for developing traditional, embedded and secure software systems.. However, my doctoral research introduced a new thematic window, Software Engineering for Intelligent Systems. The latter thus create a research window in smart, intelligent and adapting software systems (including learning technologies / environments). Within the latter sub-theme, I research into the requirement analysis and design of intelligent / adapting software systems. The foregoing involves the exploration of the interplay between technologies and theories for smart or intelligent virtual platforms and learning management systems. The aforementioned sub-theme is a multidisciplinary one that brings together ideas from diverse disciplines and/or sub-disciplines such as artificial intelligence, human computer interaction, software engineering methodologies and learning theories. Experiences gathered from the research are enormous as it opens the window of opportunities and application in diverse contexts even outside the education domain. Ideas from the stated research themes/sub-themes have been published, including publications in journals and presentation in e-Assessment in Practice, a symposium organised by the Defence Academy of United Kingdom, Cranfield University, UK. Also, some of my research outputs include a new pedagogic-driven software design metamodel; cognitive process visibility, intelligent authoring tool design characteristics, among others. The metamodel is underpinned by the synthesized and augmented ontological and epistemological meanings of two conventional theories that have never been utilized or integrated within a smart learning environment prior to its conception in the earlier mentioned research works.
|1.||Ph.D (Software Engineering and eLearning)||De Montfort University, Leicester, UK.||2012|
Application of Machine Learning to Security and Privacy Issues in Internet of Things (IoT): A Systematic Review
IoT devices are points of system vulnerability through which the system can be attacked. Machine Learning (ML) has been applied to detect threats in real-time. Different ML techniques and their various algorithms have been applied as solutions in various scenarios in order to meet security and privacy (S&P) requirements for the IoT system. One of the areas in which ML has been successfully applied is in intrusion detection (anomaly, signature or hybrid) and has been shown to perform better than traditional means in flagging new trends of attacks. This paper presents a systematic review of the literature on the application of machine learning techniques for intrusion detection in IoT.
Ten (10) years (2011-2021) data comprising academic articles are sourced from two databases (IEEE and Proquest) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Results showed that intrusion of privacy in IoT can be significantly detected with ML techniques. Furthermore, some of the common metrics used in confirming the validity of the proposed tecniques were identified.
ADENOWO ADETOKUNBO is a Senior Lecturer at the Department of Electronics and Computer Engineering
ADENOWO has a Ph.D in Software Engineering and eLearning from De Montfort University, Leicester, UK.