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Register a new userDynamic Black List (DBL) algorithm to defense against DDoS attack in Vehicular Ad-hoc Network
خوارزمية القائمة السوداء الديناميكية للحماية من هجوم حجب الخدمة الموزع DDOS في شبكة العربات المتنقلة
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Added by
Tishreen University ورقة بحثية
Publication date
2017
fields
Communication Engineering
and research's language is
العربية
Created by
Shamra Editor
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شبكات العربات المتنقلة هي مجموعة من العربات التي تحتوي على تجهيزات خاصة تمكنها من الاتصال فيما بينها مشكلة شبكة لاسلكية. تعد الهجومات على الشبكة من أخطر التحديات التي تواجه هذه الشبكات، لا سيما تلك التي تستهدف متطلب التوافرية، الذي يعد من أهم متطلبات الأمن في شبكات VANET . من أهم هذه الهجومات هجوما حجب الخدمة DoS و حجب الخدمة الموزع DDoS لأنهما يجعلان الشبكة غير متاحة للمستخدمين الفعليين فيها. نقدم فـي هذا الـبحث اقتراحاً لخوارزمية كشف هجوم DDoS و التصدي له عند حدوثه. تعتمد هذه الخوارزمية على قائمة سوداء تتضمن معرف العربات الخبيثة التي يتم اختيارها بناءً على قيمة عتبة معينة لعدد الرسائل المستقبلة منها. و نقدم تحليلاً لأداء هذه الخوارزمية بالاعتماد على بارامترات الإنتاجية و معدل وصول الرزم و التأخير نهاية إلى نهاية و مقارنتها مع أداء خوارزمية QLA.لتحقيق هذا الغرض استخدمنا المحاكي NS 2.35 مع استخدام إضافات لدعم اتصالات العربات المتنقلة (WAVE). و قد بينت نتائج المحاكاة أن الخوارزمية المقترحة تخفض تأثير الهجوم بشكل ملحوظ إذ تزيد من الإنتاجية و معدل الرزم المستلمة.
A Vehicular Ad-hoc Network (VANET) is a group of vehicles, which have special equipments enable them to connect with each other as a wireless network .The attacks are considered as the most serious challenge against this network, especially those targeting availability requirement, which is one of the most important security requirements in VANET. The Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are the most important attacks since they make the network not available for actual users. In this research, we present an algorithm to detect and face the DDoS attack. This algorithm depends on a black list contains the IDs for malicious vehicles, which are being chosen depending on a certain threshold value for a number of messages received from them. We analyze the algorithm performance depending on throughput, packet delivery ratio, end to end delay parameters, and compare it with the performance of the Queue Limiting Algorithm (QLA) .To achieve this purpose, we use NS2.35 simulator using details to support Wireless Access in Vehicular Environments (WAVE). The simulation results showed that the proposed algorithm reduces the effect of the attack Significantly since it increases the throughput and packet delivery ratio.
References used
GHANDOUR, A. FELICE, M. BONONI,L and ARTAIL, H. “Modeling and simulation of WAVE 1609.4-based multi-channel vehicular ad hoc networks”. In Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques, Desenzano del Garda, Italy, March 2012, 148-156
EICHLER, S. “Performance Evaluation of the IEEE 802.11p WAVE Communication Standard”. Vehicular Technology Conference,30 Sept.-3 Oct. 2007
QIAN, Y. LU, K and MOAYERI, N, “A SECURE VANET MAC PROTOCOL FOR DSRC APPLICATIONS”, Global Telecommunications Conference,30 Nov.-4 Dec. 2008
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Distributed Denial of Service attack (DDOS) on Vehicular Ad Hoc Networks
(VANETs) is considered to be one of the most serious types of attacks that can be targeted
to those networks. The danger of this attack is in the difficulty of detection becau
se of the
cooperation of several attacking nodes in the network, and its impact on the availability
requirement that is one of the most important security requirements in a network offering
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Simulation results have shown that this attack has a significant impact on both cases, by
comparing the basic parameters of the network, such as throughput in/out and the number
of dropped packets, before and after the attack.
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In this search, we provide a study
about the security in VANET networks which are considered to be a
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moving vehicles. The most important security flaws of this
networks and its applica
tions are studied.
In fact, we study the most important and dangerous attacks that may
target VANET networks and analyze its main risks. Furthermore,
we simulate some of these attacks Specifically Denial-of Service
(DOS) attack using prober tools depending on chosen parameters
that characterize the network performance. moreover, we provide
some Security solutions to help counter measuring these attacks and
minimize this effects, and Finally we apply these solutions and
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Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks. This paper
proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment. First, an entity density maximization method is employed to hide the attacked entities in dense regions in two KGs, such that the derived perturbations are unnoticeable. Second, an attack signal amplification method is developed to reduce the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.
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