تشخيص ناهنجاري در شبکه‌هاي اقتضايي مبتني بر خوشه با روش رأي‌گيري فازي
محورهای موضوعی : مهندسی برق و کامپیوتر
محمد رحماني‌منش
1
(دانشگاه تربيت مدرس)
سعيد جليلي
2
(دانشگاه تربيت مدرس)
کلید واژه: شبکه اقتضايي تشخيص ناهنجاري تحليل پروتکل AODV تحليل حمله ترکيب دسته‌بندهاي تک‌کلاسي رأي‌گيري فازي,
چکیده مقاله :
در اين مقاله روشي براي تشخيص و تحليل حمله در شبکه اقتضايي با ساختار مبتني بر خوشه و با پروتکل مسيريابي AODV پيشنهاد مي‌شود. براي توصيف رفتار پروتکل AODV، ابتدا تعدادي خصيصه با رويکردي مبتني بر تحليل مرحله به مرحله ويژگي‌ها و رفتار پروتکل AODV تعريف مي‌شود. آنگاه براي تشخيص حمله، از رويکرد تشخيص ناهنجاري استفاده مي‌شود و رفتار عادي پروتکل AODV با استفاده از خصيصه‌هاي تعريف‌شده و بر اساس مدل به‌دست آمده از ترکيب دسته‌بندهاي تک‌کلاسي SVDD، MoG و SOM يادگيري مي‌شود و نظر هر گره بر مبناي مدل ترکيبي به‌دست آمده شکل مي‌گيرد. نظرات گره‌ها در فرايند تشخيص ناهنجاري مرتباً به گره‌هاي سرخوشه فرستاده مي‌شود تا تشخيص نهايي در آن گره‌ها انجام شود. در روش پيشنهادي، براي ترکيب نتايج دسته‌بندهاي تک‌کلاسي در هر گره و براي تجميع نظرات ارسالي از گره‌هاي هر خوشه در گره سرخوشه، روشي فازي ارائه مي‌شود که موجب مي‌شود کارايي روش پيشنهادي در تشخيص حمله‌هاي سياه‌چاله، سوراخ کرم، تکرار بسته‌ها، عجول و جعل بسته‌هاي RouteError به مقدار قابل توجهي ارتقا يابد. در اين مقاله همچنين يک روش تحليل حمله بر مبناي رتبه‌بندي اثرپذيري خصيصه‌ها پيشنهاد مي‌شود که مشخص مي‌کند هر کدام از اين حمله‌ها چه بخش‌هايي از ويژگي‌ها و رفتار پروتکل AODV را بيشتر تحت تأثير قرار مي‌دهند. اين تحليل در هنگام کارکرد شبکه مي‌تواند منجر به تشخيص نوع حمله‌اي که در شبکه در حال اعمال است، شود.
In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal behavior of AODV protocol are defined via step by step analysis of AODV protocol and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy voting method is used for combining support vector data description (SVDD), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classifiers and the combined model is utilized to partially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and final decision on attack detection is carried out in the cluster head. In the proposed method, a fuzzy voting method is used for aggregating the votes of cluster members in the cluster head by which the performance of the method improves significantly in detecting blackhole, rushing, route error fabrication, packet replication, and wormhole attacks. In this paper, an attack analysis method based on feature sensitivity ranking is also proposed that determines which features are influenced more by the mentioned attacks. This sensitivity ranking leads to the detection of the types of attacks launched on the network.
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