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 malwar...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.展开更多
The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptio...The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.展开更多
基金This researchwork is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R411),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘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.
文摘The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.
