Revolutionizing Cybersecurity with Machine Learning: Insights from Benjamin Borketey

Investigating Malicious URLs: A Data-Driven Approach

Borketey’s study examined a dataset of 11,000 URL samples, each defined by 32 unique features linked to malicious behavior. The dataset included 6,157 non-malicious URLs and 4,898 classified as malicious, providing a robust foundation for analysis. Project has been published on Github Predicting-Cyber-Security-Using-Machine-Learning/Detection_Cybersecurity_using_Machine_Learning.ipynb at main · bbortey9/Predicting-Cyber-Security-Using-Machine-Learning

Through meticulous exploratory analysis, Borketey ensured data integrity, confirming no missing values. To improve model precision, highly correlated features were excluded to reduce redundancy, and the challenge of class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE). This technique balanced the dataset, enabling machine learning models to accurately detect both malicious and non-malicious URLs. “Balancing the dataset was essential to ensure that the models could learn effectively and deliver reliable predictions.”

Borketey’s expertise spans fraud detection, data science, data management, prediction, forecasting, Machine Learning, and Artificial Intelligence. He is a Data Scientist focusing on developing advanced machine learning models for fraud detection. He holds a master’s degree in Quantitative Economics and Econometrics from the University of Akron in Ohio and a postgraduate certificate in Machine Learning and Artificial Intelligence from Purdue University.

Choosing the Best Machine Learning Model

Borketey tested multiple machine learning algorithms, including Logistic Regression, Support Vector Machines, Random Forest, and XGBoost, to determine the most effective method for detecting malicious URLs. Using rigorous evaluation metrics such as AUC, F1 Score, Precision, Recall, and PRAUC, the study prioritized comprehensive model performance over basic accuracy.

The Random Forest model emerged as the top performer, achieving an accuracy of 97.03% on training data, an F1 score of 99.15%, and an AUC of 99.03%. Its performance remained consistent during testing, with only minimal reductions in metrics, demonstrating strong generalization capabilities and resistance to overfitting. “Random Forest’s ability to balance precision and recall makes it a reliable tool for real-world cybersecurity applications,” notes Borketey.

Key Findings and Real-World Applications

The study identified pivotal features, such as SSL Final State and URL Anchor, as key indicators of malicious activity. These insights have significant implications for strengthening cybersecurity defenses, enabling organizations to focus on the most critical factors in preventing cyberattacks.

The proposed methodology has broad applications across industries, including:

Corporate Security: Protecting businesses from phishing attacks and data breaches.

Government Agencies: Enhancing national cybersecurity infrastructure.

End-User Protection: Safeguarding individuals against malicious links in emails and advertisements.

Impact on the U.S. Economy and Cybersecurity Landscape

Cybercrime imposes substantial economic costs, with the FBI reporting $10.3 billion in internet-related losses in 2022[1]. Borketey’s work holds significant economic implications by reducing these losses and fostering market confidence. Improved cybersecurity measures not only protect resources but also encourage growth in e-commerce, attract investments, and create job opportunities in the technology sector. “By addressing cyber threats, we can fortify the U.S. economy while ensuring a safer digital landscape for everyone,” Borketey says.

Identity Theft as the root of cyberfraud

As an expert in identity fraud detection, Mr. Borketey underscores the critical importance of prioritizing the detection of identity fraud at all institutional levels to effectively prevent cyberfraud. He emphasizes that identity theft serves as the root cause of most fraudulent activities, acting as the gateway for account takeovers, credit card fraud, and synthetic identity schemes. Institutions must adopt a proactive approach, integrating robust detection mechanisms and leveraging advanced technologies like machine learning to mitigate identity-related vulnerabilities. “By addressing identity fraud at its inception, organizations can dismantle the foundation of cyberfraud, ensuring stronger security for individuals and businesses alike.”