Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Öz

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.

Anahtar Kelimeler

• Keystroke analysis
• anomaly detection
• user behavior analytics
• machine learning

Kimlik Doğrulama için Tuş Vuruşu Biyometrik Verileri: Makine Öğrenmesi Algoritmalarının Performans Analizi

Öz

Geleneksel güvenlik önlemlerinin etkisiz kaldığı günümüzde siber saldırılar giderek artmaktadır. Sonuç olarak, yapay zeka teknolojisi gibi son teknoloji araçların benimsenmesi, siber tehditlere karşı mücadelede kritik önem taşımaktadır. Klavye dinamikleri gibi kullanıcı davranışları, siber saldırılara karşı koruma için kullanılabilecek potansiyel veriler sağlamaktadır. Tuş vuruş dinamikleri, standart kullanıcı klavyeleri kullanılarak yakalanabildiğinden, kullanıcı davranışlarını tespit etmek için kullanılabilecek en hızlı ve en uygun maliyetli yöntemlerden biridir. Bu verinin analizi ve siber saldırılara karşı kullanılması makine öğrenimi algoritmaları ile mümkün olmaktadır. Bu çalışmada, klavye dinamikleri temel alınarak, kullanıcı davranışı analizi ve anomali tespiti için k-En Yakın Komşu (k-NN), Destek Vektör Makineleri (SVM), Rastgele Orman (RF) ve Sinir Ağı (NN) yöntemlerinin performansı analiz edilmektedir. Bulgular, çeşitli makine öğrenimi algoritmalarının başarılarını inceleyerek yapay zekanın siber güvenlikteki önemine ışık tutmaktadır. Çalışmanın bulguları, siber güvenlik alanında gelecekteki araştırmalar ve yeni çözümler için bir temel oluşturabilir.

Anahtar Kelimeler

• Tuş vuruşu analizi
• kullanıcı davranışı analizi
• anomali tespiti
• makine öğrenimi

Kaynakça

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