Preventing DDoS using Bloom Filter: A Survey

Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.Comment: 9 pages, 1 figure. This article is accepted for publication in EAI Endorsed Transactions on Scalable Information System

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

TDDA- Traceback-based Defence against DDoS Attack

Look In today's fast growing of internet use, security of the data and information, resources and other useful files are more important viewpoints. Distributed Denial-of-Service (DDoS) attacks are responsible for making a machine or network resource unavailable to its appropriate users. Also a DDoS attack reduces the efficiency or capability of the server to doing its job. That’s why they are very challenging issues for us. The problem is rises when spoofed IP addresses are present in the attack packets. In order to solve this critical situation of problem, that’s why we proposed a new mechanism to efficiently reduce the impact or outcome caused by DDoS attacks. In some cases, even if the attacking traffic can be filtered by the victim side, here also the attacker may blocks the access of the victim by consuming the computing resources or by consuming a large amount portion of the bandwidth of the victim. This paper is proposes a Traceback-based Defense against DDoS Attacks (TDDA) approach to resolve this problem very goodly. In this paper, we present and design one technique that can be impressively filter out the majority of DDoS attack traffic. Our primary objective or intention for this work is to improving the overall throughput and performance of the appropriate traffic and also reduce the attack traffic to maintain the quality of service for that user

A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner

An approach in identifying and tracing back spoofed IP packets to their sources

With internet expanding in every aspect of businesses infrastructure, it becomes more and more important to make these businesses infrastructures safe and secure to the numerous attacks perpetrated on them conspicuously when it comes to denial of service (DoS) attacks. A Dos attack can be summarized as an effort carried out by either a person or a group of individual to suppress a particular outline service. This can hence be achieved by using and manipulating packets which are sent out using the IP protocol included into the IP address of the sending party. However, one of the major drawbacks is that the IP protocol is not able to verify the accuracy of the address and has got no method to validate the authenticity of the sender’s packet. Knowing how this works, an attacker can hence fabricate any source address to gain unauthorized access to critical information. In the event that attackers can manipulate this lacking for numerous targeted attacks, it would be wise and safe to determine whether the network traffic has got spoofed packets and how to traceback. IP traceback has been quite active specially with the DOS attacks therefore this paper will be focusing on the different types of attacks involving spoofed packets and also numerous methods that can help in identifying whether packet have spoofed source addresses based on both active and passive host based methods and on the router-based methods

Network domain entrypoint/path determination for DDoS attacks

Accepted versio

Scalable schemes against Distributed Denial of Service attacks

Defense against Distributed Denial of Service (DDoS) attacks is one of the primary concerns on the Internet today. DDoS attacks are difficult to prevent because of the open, interconnected nature of the Internet and its underlying protocols, which can be used in several ways to deny service. Attackers hide their identity by using third parties such as private chat channels on IRC (Internet Relay Chat). They also insert false return IP address, spoofing, in a packet which makes it difficult for the victim to determine the packet\u27s origin. We propose three novel and realistic traceback mechanisms which offer many advantages over the existing schemes. All the three schemes take advantage of the Autonomous System topology and consider the fact that the attacker\u27s packets may traverse through a number of domains under different administrative control. Most of the traceback mechanisms make wrong assumptions that the network details of a company under an administrative control are disclosed to the public. For security reasons, this is not the case most of the times. The proposed schemes overcome this drawback by considering reconstruction at the inter and intra AS levels. Hierarchical Internet Traceback (HIT) and Simple Traceback Mechanism (STM) trace back to an attacker in two phases. In the first phase the attack originating Autonomous System is identified while in the second phase the attacker within an AS is identified. Both the schemes, HIT and STM, allow the victim to trace back to the attackers in a few seconds. Their computational overhead is very low and they scale to large distributed attacks with thousands of attackers. Fast Autonomous System Traceback allows complete attack path reconstruction with few packets. We use traceroute maps of real Internet topologies CAIDA\u27s skitter to simulate DDoS attacks and validate our design

FAIR: Forwarding Accountability for Internet Reputability

This paper presents FAIR, a forwarding accountability mechanism that incentivizes ISPs to apply stricter security policies to their customers. The Autonomous System (AS) of the receiver specifies a traffic profile that the sender AS must adhere to. Transit ASes on the path mark packets. In case of traffic profile violations, the marked packets are used as a proof of misbehavior. FAIR introduces low bandwidth overhead and requires no per-packet and no per-flow state for forwarding. We describe integration with IP and demonstrate a software switch running on commodity hardware that can switch packets at a line rate of 120 Gbps, and can forward 140M minimum-sized packets per second, limited by the hardware I/O subsystem. Moreover, this paper proposes a "suspicious bit" for packet headers - an application that builds on top of FAIR's proofs of misbehavior and flags packets to warn other entities in the network.Comment: 16 pages, 12 figure