Adaptive Response System for Distributed Denial-of-Service Attacks

The continued prevalence and severe damaging effects of the Distributed Denial of Service (DDoS) attacks in today’s Internet raise growing security concerns and call for an immediate response to come up with better solutions to tackle DDoS attacks. The current DDoS prevention mechanisms are usually inflexible and determined attackers with knowledge of these mechanisms, could work around them. Most existing detection and response mechanisms are standalone systems which do not rely on adaptive updates to mitigate attacks. As different responses vary in their “leniency” in treating detected attack traffic, there is a need for an Adaptive Response System. We designed and implemented our DDoS Adaptive ResponsE (DARE) System, which is a distributed DDoS mitigation system capable of executing appropriate detection and mitigation responses automatically and adaptively according to the attacks. It supports easy integrations for both signature-based and anomaly-based detection modules. Additionally, the design of DARE’s individual components takes into consideration the strengths and weaknesses of existing defence mechanisms, and the characteristics and possible future mutations of DDoS attacks. These components consist of an Enhanced TCP SYN Attack Detector and Bloom-based Filter, a DDoS Flooding Attack Detector and Flow Identifier, and a Non Intrusive IP Traceback mechanism. The components work together interactively to adapt the detections and responses in accordance to the attack types. Experiments conducted on DARE show that the attack detection and mitigation are successfully completed within seconds, with about 60% to 86% of the attack traffic being dropped, while availability for legitimate and new legitimate requests is maintained. DARE is able to detect and trigger appropriate responses in accordance to the attacks being launched with high accuracy, effectiveness and efficiency. We also designed and implemented a Traffic Redirection Attack Protection System (TRAPS), a stand-alone DDoS attack detection and mitigation system for IPv6 networks. In TRAPS, the victim under attack verifies the authenticity of the source by performing virtual relocations to differentiate the legitimate traffic from the attack traffic. TRAPS requires minimal deployment effort and does not require modifications to the Internet infrastructure due to its incorporation of the Mobile IPv6 protocol. Experiments to test the feasibility of TRAPS were carried out in a testbed environment to verify that it would work with the existing Mobile IPv6 implementation. It was observed that the operations of each module were functioning correctly and TRAPS was able to successfully mitigate an attack launched with spoofed source IP addresses

Implementing Pushback: Router-Based Defense Against DDoS Attacks

Pushback is a mechanism for defending against distributed denial-of-service (DDoS) attacks. DDoS attacks are treated as a congestion-control problem, but because most such congestion is caused by malicious hosts not obeying traditional end-to-end congestion control, the problem must be handled by the routers. Functionality is added to each router to detect and preferentially drop packets that probably belong to an attack. Upstream routers are also notified to drop such packets (hence the term Pushback) in order that the router's resources be used to route legitimate traffic. In this paper we present an architecture for Pushback, its implementation under FreeBSD, and suggestions for how such a system can be implemented in core routers

IP TRACEBACK Scenarios

Internet Protocol (IP) trace back is the enabling technology to control Internet crime. In this paper, we present novel and practical IP traceback systems which provide a defense system with the ability to find out the real sources of attacking packets that traverse through the network. IP traceback is to find the origin of an IP packet on the Internet without relying on the source IP address field. Due to the trusting nature of the IP protocol, the source IP address of a packet is not authenticated. As a result, the source address in an IP packet can be falsified (IP address spoofing). Spoof IP packets can be used for different attacks. The problem of finding the source of a packet is called the IP traceback problem. IP Traceback is a critical ability for identifying sources of attacks and instituting protection measures for the Internet. Most existing approaches to this problem have been tailored toward DDoS attack detection

IPv6: a new security challenge

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2011O Protocolo de Internet versão 6 (IPv6) foi desenvolvido com o intuito de resolver alguns dos problemas não endereçados pelo seu antecessor, o Protocolo de Internet versão 4 (IPv4), nomeadamente questões relacionadas com segurança e com o espaço de endereçamento disponível. São muitos os que na última década têm desenvolvido estudos sobre os investimentos necessários à sua adoção e sobre qual o momento certo para que o mesmo seja adotado por todos os players no mercado. Recentemente, o problema da extinção de endereçamentos públicos a ser disponibilizado pelas diversas Region Internet registry – RIRs - despertou o conjunto de entidades envolvidas para que se agilizasse o processo de migração do IPv4 para o IPv6. Ao contrário do IPv4, esta nova versão considera a segurança como um objetivo fundamental na sua implementação, nesse sentido é recomendado o uso do protocolo IPsec ao nível da camada de rede. No entanto, e devido à imaturidade do protocolo e à complexidade que este período de transição comporta, existem inúmeras implicações de segurança que devem ser consideradas neste período de migração. O objetivo principal deste trabalho é definir um conjunto de boas práticas no âmbito da segurança na implementação do IPv6 que possa ser utilizado pelos administradores de redes de dados e pelas equipas de segurança dos diversos players no mercado. Nesta fase de transição, é de todo útil e conveniente contribuir de forma eficiente na interpretação dos pontos fortes deste novo protocolo assim como nas vulnerabilidades a ele associadas.IPv6 was developed to address the exhaustion of IPv4 addresses, but has not yet seen global deployment. Recent trends are now finally changing this picture and IPv6 is expected to take off soon. Contrary to the original, this new version of the Internet Protocol has security as a design goal, for example with its mandatory support for network layer security. However, due to the immaturity of the protocol and the complexity of the transition period, there are several security implications that have to be considered when deploying IPv6. In this project, our goal is to define a set of best practices for IPv6 Security that could be used by IT staff and network administrators within an Internet Service Provider. To this end, an assessment of some of the available security techniques for IPv6 will be made by means of a set of laboratory experiments using real equipment from an Internet Service Provider in Portugal. As the transition for IPv6 seems inevitable this work can help ISPs in understanding the threats that exist in IPv6 networks and some of the prophylactic measures available, by offering recommendations to protect internal as well as customers’ networks

DDoS: DeepDefence and Machine Learning for identifying attacks

Distributed Denial of Service (DDoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through the network is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this work and developments have been made on proactive detection of attacks. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset published in year 2017,2018 and CICDDoS2019 and program has been developed in Matlab R17b, utilizing Wireshark for features extraction from the datasets. Network Intrusion attacks on critical oil and gas industrial installation become common nowadays, which in turn bring down the giant industrial sites to standstill and suffer financial impacts. This has made the production companies to started investing millions of dollars revenue to protect their critical infrastructure with such attacks with the active and passive solutions available. Our thesis constitutes a contribution to such domain, focusing mainly on security of industrial network, impersonation and attacking with DDoS

Towards IP traceback based defense against DDoS attacks.

Lau Nga Sin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 101-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Research Motivation --- p.2Chapter 1.2 --- Problem Statement --- p.3Chapter 1.3 --- Research Objectives --- p.4Chapter 1.4 --- Structure of the Thesis --- p.6Chapter 2 --- Background Study on DDoS Attacks --- p.8Chapter 2.1 --- Distributed Denial of Service Attacks --- p.8Chapter 2.1.1 --- DDoS Attack Architecture --- p.9Chapter 2.1.2 --- DDoS Attack Taxonomy --- p.11Chapter 2.1.3 --- DDoS Tools --- p.19Chapter 2.1.4 --- DDoS Detection --- p.21Chapter 2.2 --- DDoS Countermeasure: Attack Source Traceback --- p.23Chapter 2.2.1 --- Link Testing --- p.23Chapter 2.2.2 --- Logging --- p.24Chapter 2.2.3 --- ICMP-based traceback --- p.26Chapter 2.2.4 --- Packet marking --- p.28Chapter 2.2.5 --- Comparison of various IP Traceback Schemes --- p.31Chapter 2.3 --- DDoS Countermeasure: Packet Filtering --- p.33Chapter 2.3.1 --- Ingress Filtering --- p.33Chapter 2.3.2 --- Egress Filtering --- p.34Chapter 2.3.3 --- Route-based Packet Filtering --- p.35Chapter 2.3.4 --- IP Traceback-based Packet Filtering --- p.36Chapter 2.3.5 --- Router-based Pushback --- p.37Chapter 3 --- Domain-based IP Traceback Scheme --- p.40Chapter 3.1 --- Overview of our IP Traceback Scheme --- p.41Chapter 3.2 --- Assumptions --- p.44Chapter 3.3 --- Proposed Packet Marking Scheme --- p.45Chapter 3.3.1 --- IP Markings with Edge Sampling --- p.46Chapter 3.3.2 --- Domain-based Design Motivation --- p.48Chapter 3.3.3 --- Mathematical Principle --- p.49Chapter 3.3.4 --- Marking Mechanism --- p.51Chapter 3.3.5 --- Storage Space of the Marking Fields --- p.56Chapter 3.3.6 --- Packet Marking Integrity --- p.57Chapter 3.3.7 --- Path Reconstruction --- p.58Chapter 4 --- Route-based Packet Filtering Scheme --- p.62Chapter 4.1 --- Placement of Filters --- p.63Chapter 4.1.1 --- At Sources' Networks --- p.64Chapter 4.1.2 --- At Victim's Network --- p.64Chapter 4.2 --- Proposed Packet Filtering Scheme --- p.65Chapter 4.2.1 --- Classification of Packets --- p.66Chapter 4.2.2 --- Filtering Mechanism --- p.67Chapter 5 --- Performance Evaluation --- p.70Chapter 5.1 --- Simulation Setup --- p.70Chapter 5.2 --- Experiments on IP Traceback Scheme --- p.72Chapter 5.2.1 --- Performance Metrics --- p.72Chapter 5.2.2 --- Choice of Marking Probabilities --- p.73Chapter 5.2.3 --- Experimental Results --- p.75Chapter 5.3 --- Experiments on Packet Filtering Scheme --- p.82Chapter 5.3.1 --- Performance Metrics --- p.82Chapter 5.3.2 --- Choices of Filtering Probabilities --- p.84Chapter 5.3.3 --- Experimental Results --- p.85Chapter 5.4 --- Deployment Issues --- p.91Chapter 5.4.1 --- Backward Compatibility --- p.91Chapter 5.4.2 --- Processing Overheads to the Routers and Network --- p.93Chapter 5.5 --- Evaluations --- p.95Chapter 6 --- Conclusion --- p.96Chapter 6.1 --- Contributions --- p.96Chapter 6.2 --- Discussions and future work --- p.99Bibliography --- p.11

Implementing Flash Event Discrimination in IP Traceback using Shark Smell Optimisation Algorithm

 Denial of service attack and its variants are the largest ravaging network problems. They are used to cause damage to network by disrupting its services in order to harm a business or organization. Flash event is a network phenomenon that causes surge in normal network flow due to sudden increase in number of network users, To curtail the menace of the Denial of service attack it is pertinent to expose the perpetrator and take appropriate action against it. Internet protocol traceback is a network forensic tool that is used to identify source of an Internet protocol packet. Most of presently available Internet protocol traceback tools that are based on bio-inspired algorithm employ flow-based search method for tracing source of a Denial of service attack without facility to differentiate flash event from the attack. Surge in network due to flash event can mislead such a traceback tool that uses flow-based search. This work present a solution that uses hop-by-hop search with an incorporated discrimination policy implemented by shark smell optimization algorithm to differentiate the attack traffic from other traffics. It was tested on performance and convergence against an existing bio-inspired traceback tool that uses flow-base method and yielded outstanding results in all the test

Developing an Advanced IPv6 Evasion Attack Detection Framework

Internet Protocol Version 6 (IPv6) is the most recent generation of Internet protocol. The transition from the current Internet Version 4 (IPv4) to IPv6 raised new issues and the most crucial issue is security vulnerabilities. Most vulnerabilities are common between IPv4 and IPv6, e.g. Evasion attack, Distributed Denial of Service (DDOS) and Fragmentation attack. According to the IPv6 RFC (Request for Comment) recommendations, there are potential attacks against various Operating Systems. Discrepancies between the behaviour of several Operating Systems can lead to Intrusion Detection System (IDS) evasion, Firewall evasion, Operating System fingerprint, Network Mapping, DoS/DDoS attack and Remote code execution attack. We investigated some of the security issues on IPv6 by reviewing existing solutions and methods and performed tests on two open source Network Intrusion Detection Systems (NIDSs) which are Snort and Suricata against some of IPv6 evasions and attack methods. The results show that both NIDSs are unable to detect most of the methods that are used to evade detection. This thesis presents a detection framework specifically developed for IPv6 network to detect evasion, insertion and DoS attacks when using IPv6 Extension Headers and Fragmentation. We implemented the proposed theoretical solution into a proposed framework for evaluation tests. To develop the framework, “dpkt” module is employed to capture and decode the packet. During the development phase, a bug on the module used to parse/decode packets has been found and a patch provided for the module to decode the IPv6 packet correctly. The standard unpack function included in the “ip6” section of the “dpkt” package follows extension headers which means following its parsing, one has no access to all the extension headers in their original order. By defining, a new field called all_extension_headers and adding each header to it before it is moved along allows us to have access to all the extension headers while keeping the original parse speed of the framework virtually untouched. The extra memory footprint from this is also negligible as it will be a linear fraction of the size of the whole set of packet. By decoding the packet, extracting data from packet and evaluating the data with user-defined value, the proposed framework is able to detect IPv6 Evasion, Insertion and DoS attacks. The proposed framework consists of four layers. The first layer captures the network traffic and passes it to second layer for packet decoding which is the most important part of the detection process. It is because, if NIDS could not decode and extract the packet content, it would not be able to pass correct information into the Detection Engine process for detection. Once the packet has been decoded by the decoding process, the decoded packet will be sent to the third layer which is the brain of the proposed solution to make a decision by evaluating the information with the defined value to see whether the packet is threatened or not. This layer is called the Detection Engine. Once the packet(s) has been examined by detection processes, the result will be sent to output layer. If the packet matches with a type or signature that system admin chose, it raises an alarm and automatically logs all details of the packet and saves it for system admin for further investigation. We evaluated the proposed framework and its subsequent process via numerous experiments. The results of these conclude that the proposed framework, called NOPO framework, is able to offer better detection in terms of accuracy, with a more accurate packet decoding process, and reduced resources usage compared to both exciting NIDs