Exploiting Delay for Stealth: A Delay-Aware False Data Injection Framework for Wireless Sensor Networks

Öz

False data injection attacks can undermine WSN security, while the existing models assume static attackers working in isolation from network processes. This paper presents a new type of FDI attack referred to as the Delay-Aware FDI (DA-FDI). Specifically, the attacker utilizes the stochastic delays to craft injections that mimic real measurements. Next, a framework for the stealthiness analysis that uses the metrics of Stealth Success Rate (SSR) and Attack Impact Score (AIS) to quantify the Stealth-Impact Tradeoff (SIT) relationship is presented. Thereafter, four propositions that provide reasons for ineffectiveness of the following approaches in detecting DA-FDI, Cumulative Sum (CUSUM), the 3-sigma rule, LSTM networks, and autoencoders are provided. It is shown that the reason for detection failures is the interaction between the delay and the injection, and not only delay uncertainty alone. In order to overcome the deficiency, a new detector called Delay-Normalized Residual Detector (DNRD) is proposed. Detection results in simulations in NS-3 achieve accuracy of 89.4% with a false alarm rate of 3.8%.

Anahtar Kelimeler

• False Data Injection
• WSN Security
• LSTM Anomaly Detection
• Delay-Normalized Residual
• Stealth-Impact Tradeoff
• Simulation

Gizlilik için Gecikmeden Yararlanma: Kablosuz Algılayıcı Ağları için Gecikmeye Duyarlı Bir Yanlış Veri Enjeksiyonu Çerçevesi

Öz

Yanlış Veri Enjeksiyonu saldırıları, Kablosuz Sensör Ağlarının bütünlüğüne ciddi bir tehdit oluşturmaktadır; ancak mevcut modellerin çoğu, ağ dinamiklerinden bağımsız olarak çalışan statik düşmanları varsaymaktadır. Bu makalede, akıllı bir düşmanın, meşru verileri yakından taklit eden enjeksiyonlar üretmek için rastgele iletim gecikmelerinden yararlandığı bir Gecikme Farkındalıklı FDI (DA-FDI) saldırısı sunuyoruz. Bu çalışmada, Gizlilik Başarı Oranı ve Saldırı Etki Puanı temelinde, Gizlilik-Etki Değişimini yakalayan bir Gizlilik Analizi Çerçevesi geliştiriyoruz. Dört önerme, CUSUM, 3-sigma, LSTM ve otoenkoder tabanlı dedektörlerin DA-FDI'ye saldırılarına karşı neden zorlandığını açıklamaktadır. Analizimiz, tespit başarısızlığının öncelikle gecikme değişkenliğinden ziyade gecikme ve enjeksiyon arasındaki bağlantıdan kaynaklandığını göstermektedir. Bu sınırlamayı ele almak için, Gecikme Normalleştirilmiş Artık Dedektörü (DNRD) öneriyoruz. NS-3 simülasyonları, Azuma-Hoeffding eşitsizliklerine dayalı teorik garantilerle desteklenen, %3,8 yanlış pozitif oranında %89,4'lük tespit doğruluğu göstermektedir.

Anahtar Kelimeler

• Yanlış Veri Enjeksiyonu
• Kablosuz Algılayıcı Ağlar Güvenliği
• LSTM Anomali Tespiti
• Gecikmeye Göre Normalize Edilmiş Artık
• Gizlilik-Etki Dengesi
• Simülasyon

Kaynakça

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