Past Examination Papers for Computing and Mathematics https://repository.cuk.ac.ke/handle/123456789/311 2026-04-14T18:01:48Z 2026-04-14T18:01:48Z https://repository.cuk.ac.ke/handle/123456789/1909 2026-04-13T12:21:03Z 2025-01-01T00:00:00Z

COURSE CODE: BSTA 2235 END OF SEMESTER EXAMINATIONS EXAMINATION FOR THE DEGREE OF BACHELOR SCIENCE IN APPLIED STATISTICS, BACHELOR OF SCIENCE IN DATA SCIENCE, BACHELOR SCIENCE IN APPLIED STATISTICS AND DATA SCIENCE, BACHELOR SCIENCE IN APPLIED STATISTICS AND ECONOMICS

2025-01-01T00:00:00Z https://repository.cuk.ac.ke/handle/123456789/1908 2026-04-13T10:10:55Z 2023-07-01T00:00:00Z

COURSE CODE: BSTA 2235 SUPPLEMENTARYEXAMINATIONS EXAMINATION FOR THE DEGREE OF BACHELOR OF SCIENCE IN APPLIED STATISTICS

2023-07-01T00:00:00Z Kariuki Kanja, George Mbandu Angolo, Shem Shikali, Casper https://repository.cuk.ac.ke/handle/123456789/1886 2026-01-15T12:21:20Z 2025-10-01T00:00:00Z

Uncovering Sentiment-Based Predictors of Cyber Defacement Attacks: A Case of Online Discourse on X-Platform Kariuki Kanja, George; Mbandu Angolo, Shem; Shikali, Casper This paper discussed the possibility of utilizing a sentiment analysis of online discussions on X platform (which was previously X) as a predictor of cyber defacement attacks. It bridged a serious gap in the literature on cybersecurity, where the focus has been on technical signatures and little consideration has been made on socio-technical antecedents. The hypothesis that spikes of negative public sentiment might be predictive indicators of ideologically motivated cases of defacement was tested in the study. A hybrid sentiment analysis model was used, which incorporates lexicon-based VADER model with machine learning classifiers, such as Naive Bayes and Long Short-Term Memory networks. The data consisted of 503456 posts related to cybersecurity and the data were compared to the verified cases of defacement in repositories like Zone-H using time-series analysis, Pearson correlation, and cross-correlation functions. Findings indicated that negative sentiment only comprised of 8.6% of the posts with the majority being neutral (50.9) and positive (40.5). The temporal analysis showed that there is not a substantial change in negative sentiment, but short bursts of negative sentiment are associated with cybersecurity disclosure. The cross-correlation analysis showed only weak contemporaneous correlation (r ≈ 0.12, lag = 0 days) but no predictive correlation in negative lags. The stacked ensemble model (Naïve Bayes, BiLSTM, ARIMA) was very strong in classification (Accuracy = 0.8568, F1 = 0.8055, ROC-AUC = 0.9116) but mainly it was very sensitive to concurrent or retrospective signals. The research established that aggregate sentiment does not provide predictive information, socio-technical prediction would combat inactive fine-grained and entity-specific signals combined with technical threat knowledge. A research article published in the journal of information security.

2025-10-01T00:00:00Z https://repository.cuk.ac.ke/handle/123456789/1884 2026-01-15T09:35:33Z 2022-12-14T00:00:00Z

UNIT TITLE: BUSINESS SYSTEM MODELLING A PAST PAPER ON END OF SEMESTER EXAMINATION DECEMBER -2022 EXAMINATION FOR THE DEGREE OF BACHELOR OF BUSINESS AND INFORMATION TECHNOLOGY, INFORMATION TECHNOLOGY (YR III SEM II)

2022-12-14T00:00:00Z