most citedUsing ARIMA to Predict the Expansion of Subscriber Data Consumption

26 citations · 69 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CR20241 cited

Zero-day attack and ransomware detection

Steven Jabulani Nhlapo, Mike Nkongolo Wa Nkongolo

Zero-day and ransomware attacks continue to challenge traditional Network Intrusion Detection Systems (NIDS), revealing their limitations in timely threat classification. Despite e…

cs.LG202426 cited

Using ARIMA to Predict the Expansion of Subscriber Data Consumption

Mike Wa Nkongolo

This study discusses how insights retrieved from subscriber data can impact decision-making in telecommunications, focusing on predictive modeling using machine learning techniques…

cs.CR20243 cited

Ransomware Detection and Classification Using Random Forest: A Case Study with the UGRansome2024 Dataset

Peace Azugo, Hein Venter, Mike Wa Nkongolo

Cybersecurity faces challenges in identifying and mitigating ransomware, which is important for protecting critical infrastructures. The absence of datasets for distinguishing norm…

cs.HC20245 cited

CyberMoraba: A game-based approach enhancing cybersecurity awareness

Mike Nkongolo

Numerous studies confirm Cybersecurity Awareness Games (CAGs) effectively bolster organisational security against cyberattacks. This article introduces a serious CAG, integrating t…

cs.LG202411 cited

Ransomware detection using stacked autoencoder for feature selection

Mike Nkongolo, Mahmut Tokmak

The aim of this study is to propose and evaluate an advanced ransomware detection and classification method that combines a Stacked Autoencoder (SAE) for precise feature selection…

cs.CR20249 cited

Ransomware Detection Dynamics: Insights and Implications

Mike Nkongolo

The rise of ransomware attacks has necessitated the development of effective strategies for identifying and mitigating these threats. This research investigates the utilization of…