A SURVEY ON TOPOLOGY-BASED MESSAGE BROADCAST SCHEMES IN VEHICULAR NETWORKS

Vehicular ad-hoc networks (VANETs) are subclass of mobile ad-hoc networks (MANETs). They have been the most promising research field and development for the last few years. VANETs use vehicles as mobile nodes to provide communication among nearby vehicles and between vehicles and nearby roadside equipment. VANETs come with several challenging characteristics, such as dynamic and potentially large scale network topology, high mobility and intermittent connectivity of vehicular nodes, and broadcasting as the predominant communication to disseminate the safety messages. When a traffic accident happens, the safety message should be broadcasted to all vehicles in the area exposed to potential hazard. Recently, there have been a significant number of broadcasting protocols for VANETs reported in the literature. In this survey paper we provide an overview of topology-based broadcasting protocols and associated requirements, along with challenges and their proposed current and past major solutions. In addition, classification and comparison of topology-based broadcasting protocols are described from their pros and cons. Featured solutions in this domain are categorized and discussed

INVESTIGATING THE IMPACT OF TREE-BASED NETWORK TOPOLOGY ON THE SDN CONTROLLER PERFORMANCE

Software Defined Networking (SDN) is an important technology that enables a new approach to how we develop and manage networks. SDN divides the data plane and control plane and promotes logical centralization of network control so that the controller can schedule the data in the network effectively through the OpenFlow protocol. The performance and capabilities of the controller itself are important. The impact of network topology type on controller performance can be very significant. In order to have better communication in SDN, it is essential to have an analysis of the performance of specific network topologies. In this paper, we simulate ONOS and RYU controllers and compare their different network parameters under the proposed complex custom Tree-based topology. A network topology has been designed using a Mininet emulator, and the code for topology is executed in Python. From the throughput, packet transmission rate, and latency analysis, the ONOS controller displayed better results than RYU, showing that it can respond to requests more efficiently under complex SDN topologies and traffic loads. On the contrary, the RYU controller provides better results for the less complex SDN networks

Analysis of DDoS Attack Detection Techniques for Securing Software- Defined Networks

Software-Defined Networks (SDN) is an important technology that enables a new approach to how we develop and manage networks. SDN divides the data plane and control plane and supports the logical centralization of network control. However, the centralized architecture of SDN is also a potential vulnerability for various types of malicious attacks. The paper elaborates on the security aspects of virtualization as a basic concept of SDN architecture. Among the many types of attack, one of the most frequent and destructive are Distributed Denial of Service (DDoS) attacks. This paper presents an analysis of techniques to detect DDoS attacks in SDN networks. It first describes the SDN architecture and then elaborates on different detection techniques for DDoS attacks. Additionally, this paper emphasizes the types, components, and categories of detection solutions according to the techniques or methods used. The important approaches and those that can answer the complexity of detecting DDoS attacks in SDN are the detection schemes based on entropy and machine learning principles. This paper in general focuses on these two detection techniques and summarizes their benefits and drawbacks and finally provides a guideline for future research directions related to DDoS detection techniques in SDN networks.12 International scientific conference “Archibald Reiss Days” - Investigating and Proving Contemporary Forms of Crime: Scientific Approaches, Belgrade, 8-9 November 202

Nova metoda detekcije DDoS napada primenom softverski definisanih mreža

This dissertation is the result of a detailed research of detection and identification of DDoS attacks by denying network services. The scientific justification of the research is based on the fact that this important type of attack is increasingly carried out within software-defined networks, which represent a completely new and increasingly important paradigm of network management. A new method for the detection of anomalies and DDoS attacks is proposed and analyzed, which applies a combined approach that includes the entropy calculation of network attributes and the application of supervised machine learning algorithms. Entropy calculation as a high-level metric was applied on the edge OpenFlow network switch to realize fast attack detection, while supervised machine learning algorithms were executed on the controller, which achieved more accurate detection, reduced the number of false alarms and performed effective classification of network traffic. The detailed experimental analysis performed for the simulation topology of the software-defined network, obtained results that show that the proposed DDoS attack detection method achieves a high degree of efficiency and classification accuracy. Also, the proposed solution has the characteristic of generality, so it has the ability to detect different flooding attacks