CURRICULUM VITAE

DR. NICHOLAS KIPKURUI KIGET

1. Personal Details

E – Mail: nkiget@mu.ac.ke / kigetn@gmail.com Facebook: www.facebook.com/N.Langat Twitter: www.twitter/Kipkurui

2. Professional Experience

Lecturer- Moi University, School of Information Sciences Department of Information Technology

Lecturer - Masinde Muliro University of Science and Technology, Computer Science Department

Network Administrator - Masinde Muliro University of Science and Technology

Graduate Teacher - Teachers Service Commission (TSC) - Ol’lessos T.T.I.

3. Current Responsibilities

Coordinator – E-learning School of Information Sciences, Moi University

Chairman – Board of Management Kapsabet Boys Primary School

Secretary – Board of Management Nandi Agricultural Value Chain Incubator

Member – Innovation Platform for the Value Chain Research Incubation Centre for Nandi County (ASDSP Programme)

4. Education

PHD in Information Technology – Kibabii University

Master of Science in Information Technology - MMUST

Post Graduate Diploma in Education (PGDE) - Kenyatta University

Bachelor of Science in Computer and Mathematics BSC (Science) - Jomo Kenyatta University

of Science and Technology (JKUAT) Computer and Statistics - 2^nd Class Honours Upper Division

Longisa High School KCSE - Grade B, Kongoi Primary School KCPE - Grade B+

5. Professional Qualifications

ICT Policy and Strategic Planning - Diplo Foundation

Campus Wireless and Bandwidth Optimization

IT Essentials and CCNA Instructor Course

Wireless Campus Network Design and Operations

Grid computing in the framework of the UNESCO-HP Brain Gain initiative

Microsoft Certified Program (MCP) - Strathmore University, Nairobi

CCNA –Computer Pride, Nairobi

6. Professional Affiliations

Internet Society - Global Member, Registration Number – 432253

Association for Computing Machinery (ACM) – Member, Registration Number – 9960094

Kenya Institute of Management (KIM) – Associate Member ,Registration Number –31220

7. Publications on Journals

Kiget N., Karume S., Mugeni G. (2017). Grid Computing Adoptability Model for Collaborative Research in Universities. International Journal of Emerging Technologies in Engineering Research Volume 5, Issue 7, July (2017) ISSN: 2454-6410

Kiget N., Karume S., Mugeni G. (2017). Grid Computing: Distributed Computing Paradigm for Collaborative Research. International Journal of Innovative Research and Knowledge Volume 2 Issue 7, July 2017 ISSN: 2213-1356

Kiget N. K, Aselemo I.P. & Prof. Wanyembi G. (2014). Evaluating Usability of E-learning Systems in

Universities.International Journal of Advanced Computer Science and Applications . Vol. 5, No. 8, 2014

Kiget N. K, & Mugeni G.B (2014). Is it time for the Philosophy of ICT? International Journal of Information and Communication Technology Research.ISSN 2223-4985. Pp. 299-303. Volume 4 No. 8, August 2014

8. Research Areas

Usability of E-learning Systems, Grid Computing, Cloud Computing, Agribusiness incubation

9. Personal Profile

To integrate ICT in governance, teaching, learning and in agribusiness

To undertake intensive research in computing and related fields

(Last updated: February 21, 2019)

Recent Publications

Yego, J.K., Kiget, N.K. & Samoei, D., 2022. A Deep Reinforcement Learning Approach to Modelling an Intrusion Detection Sustem Using Asynchronous Advantage Actor-Critic (A3C) Algorithm. Journal of Research Innovation and Implications in Education, 6(1), p.441-452. Abstract

An increase in growth and use of the internet has also resulted in attacks evolving and more novel attacks having a devastating effect are witnessed. The Intrusion Detection System (IDS) is yet to achieve maximum success due to false positives and low detection. The purpose of the study was to determine the modelling of an intrusion detection system using the Asynchronous Advantage Actor-Critic (A3C) Algorithm. In this paper we look at the following: (i) To evaluate the current machine learning techniques being used in IDS, (ii) To determine the effectiveness of using the Asynchronous Advantage Actor-Critic algorithm in anomaly detection, (iii) To select the appropriate training data set and prepare for use on A3C. A conceptual study was done in looking at these objectives. The UNSW_TRAIN and UNSW_TEST were samples selected by purposive sampling from the whole population of UNSW-NB15 dataset. Analysis of the dataset was done using Python. Key findings were that anomaly detection approach is the best approach due to its ability to detect novel attacks. Also, there is need to continue research on intrusion detection and improve solutions to the problem of false positives and fully optimize on accuracy. The UNSW-NB15 dataset is comprehensive and so all the attack types should be used so as to accurately depict the intrusions and should selected attack types be used, feature selection should be done accurately so as to reflect modern attack types.

Kiget, N.K., Karume, S. & Mugeni, G., 2017. Grid Computing: Distributed Computing Paradigm for Collaborative Research. International Journal of Innovative Research and Knowledge, 2(7), p.2213-1356.

Kiget, N.K., Karume, S. & Mugeni, G., 2017. Grid Computing Adoptability Model for Collaborative Research in Universities. International Journal of Emerging Technologies in Engineering Research, 5(7), p.2454-6410.

Nicholas K. Kiget

Lecturer of Information Technology

CURRICULUM VITAE

Recent Publications

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