Empirical estimation of ETSI ITS-G5 performance over an IPv6-based platform. - In: 2022 IEEE International Performance, Computing, and Communications Conference (IPCCC), (2022), S. 185-193
Intelligent transport systems (ITS) promise to leverage wireless communications among vehicles. Performance of wireless communication is of crucial importance and can severely impact safety, efficiency and infotainment of transport applications. In this paper, in order to promote the usage of IPv6 in vehicular networks, we deploy a wireless communication framework in a Linux environment based on the Internet protocol (IP). Further, we evaluate the behaviour of the ITS-G5 in various stationary and mobile scenarios to investigate the impact of environmental and radio factors on the performance of ITS-G5. Several measurements such as inter-reception time (IRT), packet loss, latency, end-to-end (E2E) latency, reception position error (RPE), and throughput are carried out to discover the limitations and strength of the technology in a real world test-bed.
https://doi.org/10.1109/IPCCC55026.2022.9894344
Recent review of distributed denial of service attacks in the Internet of Things. - In: 5th Conference on Cloud and Internet of Things, (2022), S. 32-39
The use of the Internet of Things (IoT) in almost all domains nowadays makes it the network of the future. Due to the high attention since its creation, this network is the target of numerous attacks of different purpose and nature, of which one of the most perpetrated and virulent is the distributed denial of services (DDoS) attack. This article reviews the different security requirements and some of the attacks within IoT networks. It then focuses on DDoS attacks on the IoT and summarizes some methods of countermeasures for this attack, from the oldest to the most recent ones. Based on this study, it seems that the benefits of machine learning (ML) and deep learning (DL) combined with other technologies such as software defined networking (SDN) are very promising approaches against DDoS attacks.
https://doi.org/10.1109/CIoT53061.2022.9766655
Distributed vehicular communication protocols for autonomous intersection management. - In: The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40), (2022), S. 150-157
Intersections are considered to be a vital part of urban transportation and drivers are prone to make more mistakes, when driving through the intersections. A high percentage of the total fatal car accidents leading to injuries are reported within intersections annually. On the other side, there usually is traffic congestion at intersections during busy times of day. Stopping the vehicles in one direction to let the vehicle pass in the other directions leads to this phenomenon and it has a huge effect on traffic delay, which causes great squander in natural and human resources as well as leading to weather pollution in metropolises. The goal of this paper is to design and simulate different spatio-temporal-based algorithms for autonomous connected vehicles to be able to cross the intersection safely and efficiently. Vehicles employ vehicle-to-vehicle (V2V) communication via dedicated short range communications (DSRC) [4, 1] to exchange their kinematic information with each other. The proposed algorithms are compared to each other as well as with traditional methods like traffic lights in terms of various performance metrics such as traffic congestion, speed and especially delay to find the optimal control approach for autonomous intersection management.
https://doi.org/10.1016/j.procs.2022.03.022
Intrusion Detection System against Denial of Service attack in Software-Defined Networking. - Ilmenau : Universitätsbibliothek, 2022. - 1 Online-Ressource (vii, 153 Seiten)
Technische Universität Ilmenau, Dissertation 2022
Das exponentielle Wachstum der Online-Dienste und des über die Kommunikationsnetze übertragenen Datenvolumens macht es erforderlich, die Struktur traditioneller Netzwerke durch ein neues Paradigma zu ersetzen, das sich den aktuellen Anforderungen anpasst. Software-Defined Networking (SDN) ist hierfür eine fortschrittliche Netzwerkarchitektur, die darauf abzielt, das traditionelle Netzwerk in ein flexibleres Netzwerk umzuwandeln, das sich an die wachsenden Anforderungen anpasst. Im Gegensatz zum traditionellen Netzwerk ermöglicht SDN die Entkopplung von Steuer- und Datenebene, um Netzwerkressourcen effizient zu überwachen, zu konfigurieren und zu optimieren. Es verfügt über einen zentralisierten Controller mit einer globalen Netzwerksicht, der seine Ressourcen über programmierbare Schnittstellen verwaltet. Die zentrale Steuerung bringt jedoch neue Sicherheitsschwachstellen mit sich und fungiert als Single Point of Failure, den ein böswilliger Benutzer ausnutzen kann, um die normale Netzwerkfunktionalität zu stören. So startet der Angreifer einen massiven Datenverkehr, der als Distributed-Denial-of-Service Angriff (DDoSAngriff) von der SDN-Infrastrukturebene in Richtung des Controllers bekannt ist. Dieser DDoS-Angriff führt zu einer Sättigung der Steuerkanal-Bandbreite und belegt die Ressourcen des Controllers. Darüber hinaus erbt die SDN-Architektur einige Angriffsarten aus den traditionellen Netzwerken. Der Angreifer fälscht beispielweise die Pakete, um gutartig zu erscheinen, und zielt dann auf die traditionellen DDoS-Ziele wie Hosts, Server, Anwendungen und Router ab. In dieser Arbeit wird das Verhalten von böswilligen Benutzern untersucht. Anschließend wird ein Intrusion Detection System (IDS) zum Schutz der SDN-Umgebung vor DDoS-Angriffen vorgestellt. Das IDS berücksichtigt dabei drei Ansätze, um ausreichendes Feedback über den laufenden Verkehr durch die SDN-Architektur zu erhalten: die Informationen von einem externen Gerät, den OpenFlow-Kanal und die Flow-Tabelle. Daher besteht das vorgeschlagene IDS aus drei Komponenten. Das Inspector Device verhindert, dass böswillige Benutzer einen Sättigungsangriff auf den SDN-Controller starten. Die Komponente Convolutional Neural Network (CNN) verwendet eindimensionale neuronale Faltungsnetzwerke (1D-CNN), um den Verkehr des Controllers über den OpenFlow-Kanal zu analysieren. Die Komponente Deep Learning Algorithm(DLA) verwendet Recurrent Neural Networks (RNN), um die vererbten DDoS-Angriffe zu erkennen. Sie unterstützt auch die Unterscheidung zwischen bösartigen und gutartigen Benutzern als neue Gegenmaßnahme. Am Ende dieser Arbeit werden alle vorgeschlagenen Komponenten mit dem Netzwerkemulator Mininet und der Programmiersprache Python modelliert, um ihre Machbarkeit zu testen. Die Simulationsergebnisse zeigen hierbei, dass das vorgeschlagene IDS im Vergleich zu mehreren Benchmarking- und State-of-the-Art-Vorschlägen überdurchschnittliche Leistungen erbringt.
https://doi.org/10.22032/dbt.51449
EEC-GA: energy-efficient clustering approach using genetic algorithm for heterogeneous wireless sensor networks. - In: The 36th International Conference on Information Networking (ICOIN 2022), (2022), S. 280-286
In a wireless sensor network (WSN), sensor nodes are distributed in a predefined environment, collecting environmental data and transferring them to the base station or sink for processing and analysis. In the meantime, much research has been done on heterogeneous wireless sensor networks. Due to the fact that in a heterogeneous wireless sensor network (HWSN) factors such as primary energy, data processing ability, etc. greatly affect the life of the network, the selection of cluster heads (CHs) for coordinating a cluster of sensor nodes has created a wide range for further development of network efficiency capabilities. In this work, an event-driven energy-efficient protocol based on a genetic algorithm is proposed that uses various parameters such as the remaining energy of the node, the minimum distance of a node to the base station, and also the degree of the neighborhood of a node as fitness function values, and evaluates solutions in a heterogeneous wireless sensor network. For evaluating the proposal, network stability, energy consumption, death of the first node, and number of living nodes per round are considered as evaluation criteria.
https://doi.org/10.1109/ICOIN53446.2022.9687275
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles. - In: IEEE access, ISSN 2169-3536, Bd. 10 (2022), S. 7937-7972
https://doi.org/10.1109/ACCESS.2022.3142450
Towards applying IPSec between edge switches and end users to counter DDoS attacks in SDNs. - In: 2021 IEEE 23rd International Conference on High Performance Computing Communications/7th International Conference on Data Science Systems/19th International Conference on Smart City/7th International Conference on Dependability in Sensor, Cloud Big Data Systems Applications, (2021), S. 1545-1551
Software-Defined Networking (SDN) is a new networking paradigm with many advantages compared to traditional networks, such as reliability, scalability, and flexibility. However, SDN inherits some vulnerabilities from traditional networks and even shows new properties that malicious users might exploit as vulnerable aspects. In this paper, a novel solution is introduced based on the notion of the IP Security protocol (IPSec) and an adaptive threshold algorithm to counter Distributed Denial of Service (DDoS) attacks and freeloading attacks. The simulation results show the ability of the proposed countermeasure to prevent these attacks by distinguishing between benign and malicious users, which shows a notable enhancement compared to previous approaches.
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys53884.2021.00229
Safety-centric vehicle classification using vehicular networks. - In: The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), the 16th International Conference on Future Networks and Communications (FNC), the 11th International Conference on Sustainable Energy Information Technology, (2021), S. 238-245
This paper investigates the vehicle classification (VC) based on vehicular ad-hoc networks (VANETs). Using VANETs, one can extract the physical and mobility characteristics of the vehicles globally and in a real-time manner. In this paper, we propose an in-depth novel safety-driven VC method for heterogeneous connected vehicles. In this innovative approach, road vehicles are classified into a broad range of classes according to their distinctive behaviors and safety measures. The proposed method can play a vital role in reducing collisions and can be used as a safety standard reference in VANETs-based VC systems. Furthermore, advance driver assistance systems (ADAS) can integrate this method and extend road safety by notifying vehicles of dangerous situations on the road using V2X communication.
https://doi.org/10.1016/j.procs.2021.07.030
Deep learning algorithms for detecting denial of service attacks in Software-Defined Networks. - In: The 18th International Conference on Mobile Systems and Pervasive Computing (MobiSPC), the 16th International Conference on Future Networks and Communications (FNC), the 11th International Conference on Sustainable Energy Information Technology, (2021), S. 254-263
In Software-Defined Networking (SDN) the controller is the only entity that has the complete view on the network, and it acts as the brain, which is responsible for traffic management based on its global knowledge of the network. Therefore, an attacker attempts to direct malicious traffic towards the controller, which could lead to paralyze the entire network. In this work, Deep Learning algorithms are used to protect the controller by applying high-security measures, which is essential for the continuous availability and connectivity in the network. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are proposed to recognize and prevent the intrusion attacks. We evaluate our models using a recently released dataset (InSDN dataset). Finally, our experiments manifest that our models achieve very high accuracy for the detection of Denial of Service (DoS) attacks. Thus, a significant improvement in attack detection can be shown compared to one of the benchmarking state of the art approaches.
https://doi.org/10.1016/j.procs.2021.07.032
16. Ilmenauer TK-Manager Workshop : Technische Universität Ilmenau, 16. September 2021 : Tagungsband. - Ilmenau : ilmedia, 2021. - 1 Online-Ressource (25 Seiten)
https://doi.org/10.22032/dbt.50118