Flooding Distributed Denial of Service Attacks-A Review

Flaws either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched. Of the kinds of network attacks, denial-of-service flood attacks have caused the most severe impact. Approach: This study reviews recent researches on flood attacks and their mitigation, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are compared against criteria related to their characteristics, methods and impacts. Results: Denial-of-service flood attacks vary in their rates, traffic, targets, goals and impacts. However, they have general similarities that are the methods used are flooding and the main purpose is to achieve denial of service to the target. Conclusion/Recommendations: Mitigation of the denial-of-service flood attacks must correspond to the attack rates, traffic, targets, goals and impacts in order to achieve effective solution

Flooding Distributed Denial of Service Attacks-A Review

Problem statement: Flaws either in users’ implementation of a network or in the standard specification of protocols has resulted in gaps that allow various kinds of network attack to be launched. Of the kinds of network attacks, denial-of service flood attacks have caused the most severe impact. Approach: This study reviews recent researches on flood attacks and their mitigation, classifying such attacks as either high-rate flood or low-rate flood. Finally, the attacks are compared against criteria related to their characteristics, methods and impacts. Results: Denial-of service flood attacks vary in their rates, traffic, targets, goals and impacts. However, they have general similarities that are the methods used are flooding and the main purpose is to achieve denial of service to the target. Conclusion/Recommendations: Mitigation of the denial-of service flood attacks must correspond to the attack rates, traffic, targets, goals and impacts in order to achieve effective solution

Denial of Service in Voice Over IP Networks

In this paper we investigate denial of service (DoS) vulnerabilities in Voice over IP (VoIP) systems, focusing on the ITU-T H.323 family of protocols. We provide a simple characterisation of DoS attacks that allows us to readily identify DoS issues in H.323 protocols. We also discuss network layer DoS vulnerabilities that affect VoIP systems. A number of improvements and further research directions are proposed

Introducing the SlowDrop Attack

In network security, Denial of Service (DoS) attacks target network systems with the aim of making them unreachable. Last generation threats are particularly dangerous because they can be carried out with very low resource consumption by the attacker. In this paper we propose SlowDrop, an attack characterized by a legitimate-like behavior and able to target different protocols and server systems. The proposed attack is the first slow DoS threat targeting Microsoft IIS, until now unexploited from other similar attacks. We properly describe the attack, analyzing its ability to target arbitrary systems on different scenarios, by including both wired and wireless connections, and comparing the proposed attack to similar threats. The obtained results show that by executing targeted attacks, SlowDrop is successful both against conventional servers and Microsoft IIS, which is closed source and required us the execution of so called \u201cnetwork level reverse engineering\u201d activities. Due to its ability to successfully target different servers on different scenarios, the attack should be considered an important achievement in the slow DoS field

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

Encountering distributed denial of service attack utilizing federated software defined network

This research defines the distributed denial of service (DDoS) problem in software-defined-networks (SDN) environments. The proposes solution uses Software defined networks capabilities to reduce risk, introduces a collaborative, distributed defense mechanism rather than server-side filtration. Our proposed network detection and prevention agent (NDPA) algorithm negotiates the maximum amount of traffic allowed to be passed to server by reconfiguring network switches and routers to reduce the ports' throughput of the network devices by the specified limit ratio. When the passed traffic is back to normal, NDPA starts network recovery to normal throughput levels, increasing ports' throughput by adding back the limit ratio gradually each time cycle. The simulation results showed that the proposed algorithms successfully detected and prevented a DDoS attack from overwhelming the targeted server. The server was able to coordinate its operations with the SDN controllers through a communication mechanism created specifically for this purpose. The system was also able to determine when the attack was over and utilize traffic engineering to improve the quality of service (QoS). The solution was designed with a sophisticated way and high level of separation of duties between components so it would not be affected by the design aspect of the network architecture

On modeling and mitigating new breed of dos attacks

Denial of Service (DoS) attacks pose serious threats to the Internet, exerting in tremendous impact on our daily lives that are heavily dependent on the good health of the Internet. This dissertation aims to achieve two objectives:1) to model new possibilities of the low rate DoS attacks; 2) to develop effective mitigation mechanisms to counter the threat from low rate DoS attacks. A new stealthy DDoS attack model referred to as the quiet attack is proposed in this dissertation. The attack traffic consists of TCP traffic only. Widely used botnets in today\u27s various attacks and newly introduced network feedback control are integral part of the quiet attack model. The quiet attack shows that short-lived TCP flows used as attack flows can be intentionally misused. This dissertation proposes another attack model referred to as the perfect storm which uses a combination of UDP and TCP. Better CAPTCHAs are highlighted as current defense against botnets to mitigate the quiet attack and the perfect storm. A novel time domain technique is proposed that relies on the time difference between subsequent packets of each flow to detect periodicity of the low rate DoS attack flow. An attacker can easily use different IP address spoofing techniques or botnets to launch a low rate DoS attack and fool the detection system. To mitigate such a threat, this dissertation proposes a second detection algorithm that detects the sudden increase in the traffic load of all the expired flows within a short period. In a network rate DoS attacks, it is shown that the traffic load of all the expired flows is less than certain thresholds, which are derived from real Internet traffic analysis. A novel filtering scheme is proposed to drop the low rate DoS attack packets. The simulation results confirm attack mitigation by using proposed technique. Future research directions will be briefly discussed

Resilience to DDoS attacks

Tese de mestrado, Segurança Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasDistributed Denial-of-Service (DDoS) is one of the most common cyberattack used by malicious actors. It has been evolving over the years, using more complex techniques to increase its attack power and surpass the current defense mechanisms. Due to the existent number of different DDoS attacks and their constant evolution, companies need to be constantly aware of developments in DDoS solutions Additionally, the existence of multiple solutions, also makes it hard for companies to decide which solution best suits the company needs and must be implemented. In order to help these companies, our work focuses in analyzing the existing DDoS solutions, for companies to implement solutions that can lead to the prevention, detection, mitigation, and tolerance of DDoS attacks, with the objective of improving the robustness and resilience of the companies against DDoS attacks. In our work, it is presented and described different DDoS solutions, some need to be purchased and other are open-source or freeware, however these last solutions require more technical expertise by cybersecurity agents. To understand how cybersecurity agents protect their companies against DDoS attacks, nowadays, it was built a questionnaire and sent to multiple cybersecurity agents from different countries and industries. As a result of the study performed about the different DDoS solutions and the information gathered from the questionnaire, it was possible to create a DDoS framework to guide companies in the decisionmaking process of which DDoS solutions best suits their resources and needs, in order to ensure that companies can develop their robustness and resilience to fight DDoS attacks. The proposed framework it is divided in three phases, in which the first and second phase is to understand the company context and the asset that need to be protected. The last phase is where we choose the DDoS solution based on the information gathered in the previous phases. We analyzed and presented for each DDoS solutions, which DDoS attack types they can prevent, detect and/or mitigate

Distributed Denial of Service Attack Detection

Distributed Denial of Service (DDoS) attacks on web applications has been a persistent threat. Successful attacks can lead to inaccessible service to legitimate users in time and loss of business reputation. Most research effort on DDoS focused on network layer attacks. Existing approaches on application layer DDoS attack mitigation have limitations such as the lack of detection ability for low rate DDoS and not being able to detect attacks targeting resource files. In this work, we propose DDoS attack detection using concepts from information retrieval and machine learning. We include two popular concepts from information retrieval: Term Frequency (TF)-Inverse Document Frequency (IDF) and Latent Semantic Indexing (LSI). We analyzed web server log data generated in a distributed environment. Our evaluation results indicate that while all the approaches can detect various ranges of attacks, information retrieval approaches can identify attacks ongoing in a given session. All the approaches can detect three well known application level DDoS attacks (trivial, intermediate, advanced). Further, these approaches can enable an administrator identifying new pattern of DDoS attacks