The Role of Traditional Rulers in Addressing Security Challenges: Reflections on Native Authority System in Colonial Sokoto Emirate, 1903-1960

Sokoto Emirate was one of the three(later four)constituent emirates in colonial Sokoto Province,Northern Nigeria.The others being Sokoto,Gwandu,Argungu,and Yauri Emirates.It is located at the northwestern most part of Nigeria.The Sokoto Emirate covers the present day Sokoto and Zamfara states of Nigeria.During the British colonial administration,native authority(NA)system was a model of local government administration created under the“indirect rule system”.It was a system where by the pre-colonial traditional leadership institutions were modified and allowed to function in the local government administration introduced by the British.This paper assessed the role played by traditional leadership institutions in addressing security challenges during the British colonial administration of Sokoto Emirate.In particular,the role played by the NA in arresting Mahdist uprisings,and other related extremist religious movements on the one hand,as well as rural banditry,armed robbery,theft,among other security challenges in the emirate,are highlighted.The paper further argued that some of the roles played by the traditional leadership institutions in successfully addressing those challenges are still relevant in addressing the problems of insurgency on the one hand,and some of the recent emerging security challenges,in northwestern Nigeria,such as cattle rustling,rural banditry,kidnappings,robbery,and other related challenges.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.