DoS and DDoS Attacks: Defense, Detection and Traceback Mechanisms - A Survey

Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks are typically explicit attempts to exhaust victim2019;s bandwidth or disrupt legitimate users2019; access to services. Traditional architecture of internet is vulnerable to DDoS attacks and it provides an opportunity to an attacker to gain access to a large number of compromised computers by exploiting their vulnerabilities to set up attack networks or Botnets. Once attack network or Botnet has been set up, an attacker invokes a large-scale, coordinated attack against one or more targets. Asa result of the continuous evolution of new attacks and ever-increasing range of vulnerable hosts on the internet, many DDoS attack Detection, Prevention and Traceback mechanisms have been proposed, In this paper, we tend to surveyed different types of attacks and techniques of DDoS attacks and their countermeasures. The significance of this paper is that the coverage of many aspects of countering DDoS attacks including detection, defence and mitigation, traceback approaches, open issues and research challenges

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

A Generalized Renyi Joint Entropy Method for the Detection of DDoS Attacks in IoT

Internet of things connects all the smart devices with internet and gain more information in comparison with other systems. Since different types of objects are connected, privacy and security of the users must be ensured. Because of the decentralised nature, IoT is prone to different types of attacks which are either active or passive. Since internet is the main part of IoT, the security issues present in Internet will be available in the Internet of Things too. Distributed denial of service is a major threat of this type and a critical threat. It reduces the performance of the complete network even it breaks entire communication. For this reason many researches have been made in this area to detect Distributed Denial of Service attack. Entropy-based approaches to identify DDoS attacks in the internet of things are discussed in this research. This new approach is based on the GRJE method, which stands for generalised Renyi joint entropy. Renyi joint entropy is used in the suggested approach to analyse network traffic flow. The suggested method is put into practise and evaluated against other methods based on a few factors.  Results from an analysis of the suggested system's effectiveness in NS2 are reported in this study

Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%

High-performance, Platform-Independent DDoS Detection for IoT Ecosystems

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 − 95% and a bandwidth overhead of less than 1%

Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach

The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.This research was funded by EU, European Regional Development Fund, and the regional government of Extremadura, Spain, grant number IB18003, the Spanish Ministry of Science, Innovation and Universities grant numbers TIN2016-75097-P, RTI2018-102002-A-I00, PEJ2018-003648-A and FEDER and Junta de Andalucía grant number B-TIC-402-UGR18

Entropy-based collaborative detection of DDOS attacks on community networks

A community network often operates with the same Internet service provider domain or the virtual network of different entities who are cooperating with each other. In such a federated network environment, routers can work closely to raise early warning of DDoS attacks to void catastrophic damages. However, the attackers simulate the normal network behaviors, e.g. pumping the attack packages as poisson distribution, to disable detection algorithms. It is an open question: how to discriminate DDoS attacks from surge legitimate accessing. We noticed that the attackers use the same mathematical functions to control the speed of attack package pumping to the victim. Based on this observation, the different attack flows of a DDoS attack share the same regularities, which is different from the real surging accessing in a short time period. We apply information theory parameter, entropy rate, to discriminate the DDoS attack from the surge legitimate accessing. We proved the effectiveness of our method in theory, and the simulations are the work in the near future. We also point out the future directions that worth to explore in the future.<br /