ARTIFICIAL INTELLIGENCE-BASED IOT SECURITY MONITORING SYSTEM

Abstract

In this study, a security system was developed to detect Distributed Denial of Service (DDoS) attacks targeting networks connected to Internet of Things (IoT) devices. Due to their low processing power, limited memory capacity, and inadequate security measures, IoT devices are frequently targeted by cyber attackers. Accordingly, a detection model based on supervised learning techniques was designed to mitigate the impact of DDoS attacks, which are commonly encountered in IoT-based networks. For model training, a labeled dataset containing various types of attacks and DDoS-related traffic was utilized. During the training process, the dataset was divided into training and testing subsets, and the model’s performance was evaluated using accuracy, precision, recall, and F1-score metrics. In addition, k-fold cross-validation was applied to enhance the model’s generalization ability. The system was structured not only to work with static data but also to analyze live network traffic. DDoS attacks were successfully detected through real-time traffic analysis, and instant alerts were delivered to the user. As a result, an effective, dynamic, and AI-based detection mechanism was established against attacks targeting IoT networks.

Keywords

• DDoS Detection
• IoT Security
• AI-Based Detection
• Machine Learning
• Real-Time Monitoring

YAPAY ZEKA DESTEKLİ IOT GÜVENLİK İZLEME SİSTEMİ

Abstract

Bu çalışmada, IoT (Nesnelerin İnterneti) cihazlarının bağlı bulunduğu ağlarda gerçekleşen DDoS (Dağıtılmış Hizmet Engelleme) saldırılarının tespitine yönelik bir güvenlik sistemi geliştirilmiştir. IoT cihazlarının düşük işlem gücü, sınırlı bellek kapasitesi ve zayıf güvenlik önlemleri nedeniyle bu cihazlar, siber saldırganlar tarafından sıklıkla hedef alınmaktadır. Bu doğrultuda, IoT tabanlı ağlarda sıkça rastlanan DDoS saldırılarının etkilerini azaltmak amacıyla denetimli öğrenme yöntemleriyle çalışan bir tespit modeli tasarlanmıştır. Modelin eğitimi için farklı DDoS saldırı türlerini içeren ve DDoS trafiğiyle etiketli bir veri seti kullanılmıştır. Eğitim sürecinde veri seti eğitim ve test kümelerine ayrılmış, modelin başarımı doğruluk, kesinlik, hatırlama ve F1 skoru metrikleriyle değerlendirilmiştir. Ayrıca, k-fold çapraz doğrulama uygulanarak modelin genelleme yeteneği artırılmıştır. Sistem yalnızca statik veri ile sınırlı kalmamış, canlı ağ trafiğini de analiz edebilecek biçimde yapılandırılmıştır. Gerçek zamanlı olarak analiz edilen ağ trafiği üzerinden DDoS saldırıları başarıyla tespit edilmiş ve kullanıcıya anlık bildirimler sağlanmıştır. Böylece, IoT ağlarına yönelik saldırılara karşı etkin, dinamik ve yapay zeka tabanlı bir tespit mekanizması ortaya konulmuştur.

Keywords

• DDoS Tespiti
• IoT Güvenliği
• Yapay Zeka Tabanlı Tespit
• Makine Öğrenmesi
• Gerçek Zamanlı İzleme

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