Distributed Denial of Service (DDoS) flooding attacks are one of the biggest concerns for security professionals. DDoS flooding attacks are typically explicit attempts to disrupt legitimate users' access to services. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies (i.e., Botnets). Once an attack army has been set up, an attacker can invoke a coordinated, large-scale attack against one or more targets. Developing a comprehensive defense mechanism against identified and anticipated DDoS flooding attacks is a desired goal of the intrusion detection and prevention research community. However, the development of such a mechanism requires a comprehensive understanding of the problem and the techniques that have been used thus far in preventing, detecting, and responding to various DDoS flooding attacks. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. We categorize the DDoS flooding attacks and classify existing countermeasures based on where and when they prevent, detect, and respond to the DDoS flooding attacks. Moreover, we highlight the need for a comprehensive distributed and collaborative defense approach. Our primary intention for this work is to stimulate the research community into developing creative, effective, efficient, and comprehensive prevention, detection, and response mechanisms that address the DDoS flooding problem before, during and after an actual attack.

/pdf/a-survey-of-defense-mechanisms-against-distributed-denial-of-36i5dbdzy6.pdf

A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks

HTTP flood attack detection in application layer using machine learning metrics and bio inspired bat algorithm

Application layer HTTP-GET flood DDoS attacks

An Application Layer Distributed Denial of Service Attack (DDoS) is one of the biggest concerns for web security. Many detection methods are designed to mitigate DDoS attack based on IP and TCP layer instead of the Application layer. These methods are not suitable for detection of Application layer DDoS attack since most of the IP and TCP layer DDoS attacks are based on request flooding attack. But Application layer DDoS attacks consist of request flooding, session flooding, and asymmetric attack. The solutions available to detect Application layer DDoS attack, detect only limited number of Application layer DDoS attacks. The solutions that detect all types of Application layer DDoS attacks have huge algorithm complexity. One of the major challenges in the detection of an Application layer DDoS attack is the non-availability of features to detect such attacks. Hence it is difficult to model normal user behavior from attack behavior. In this paper, Deep learning architecture is introduced to learn deep features of Application layer DDoS attack. Deep learning architecture consist of very deep neural network, typically more than three layers. In the proposed work the concept of AutoEncoder is applied, which is one of the deep learning based models that learns deep useful features in the Application layer DDoS attack dataset. The Stacked AutoEncoder deep learning architecture, is aimed to receive high level features. The performance of the proposed method was evaluated in terms of the metrics such as false positive rate and detection rate. Comparison of the proposed method with the existing methods reveals that the proposed method performs better than the existing methods.

Detection of Application Layer DDoS attack by feature learning using Stacked AutoEncoder

Taxonomy of Distributed Denial of Service mitigation approaches for cloud computing

Nowadays, web servers are suffering from application layer distributed denial of service (DDoS) attacks, to which network layer solutions is not applicable as attackers are indistinguishable based on packets or protocols. In this study, the authors propose trust management helmet (TMH) as a partial solution to this problem, which is a lightweight mitigation mechanism that uses trust to differentiate legitimate users from attackers. Its key insight is that a server should give priority to protecting the connectivity of good users during application layer DDoS attacks, instead of identifying all the attack requests. The trust to clients is evaluated based on their visiting history and used to schedule the service to their requests. The authors introduce license, for user identification (even beyond NATs) and storing the trust information at clients. The license is cryptographically secured against forgery or replay attacks. The authors realise this mitigation mechanism and implement it as a Java package and use it for evaluation. The simulation results show that TMH is effective in mitigating session flooding attack: even with 20 times number of attackers, more than 99% of the sessions from legitimate users are accepted with TMH; whereas less than 18% are accepted without it. Moreover, we found that the additional computation cost on the deployed server is neglectable and the bandwidth overhead is acceptable.

Mitigating application layer distributed denial of service attacks via effective trust management

Application layer DDoS attacks, to which network layer solutions is not applicable as attackers are indistinguishable based on packets or protocols, prevent legitimate users from accessing services. In this paper, we propose Trust Management Helmet (TMH) as a partial solution to this problem, which is a lightweight mitigation mechanism that uses trust to differentiate legitimate users and attackers. Its key insight is that a server should give priority to protecting the connectivity of good users during application layer DDoS attacks, instead of identifying all the attack requests. The trust to clients is evaluated based on their visiting history, and used to schedule the service to their requests. We introduce license, for user identification (even beyond NATs) and storing the trust information at clients. The license is cryptographically secured against forgery or replay attacks. We realize this mitigation mechanism and implement it as a Java package and use it for simulation. Through simulation, we show that TMH is effective in mitigating session flooding attack: even with 20 times number of attackers, more than 99% of the sessions from legitimate users are accepted with TMH; whereas less than 18% are accepted without it.

/pdf/a-lightweight-mechanism-to-mitigate-application-layer-ddos-1at8sc4q9k.pdf

A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks

Monitoring, analyzing and characterizing lookup traffic in a large-scale DHT

Nowadays, web servers are suffering from flash crowds and application layer DDoS attacks that can severely degrade the availability of services. It is difficult to prevent them because they comply with the communication protocol. Peer-to-peer (P2P) networks have been exploited to amplify DDoS attacks, but we believe their available resource, such as distributed storage and network bandwidth, can be used to mitigate both flash crowds and DDoS attacks. In this paper, we propose a server initiated approach to employ deployed P2P networks as distributed web caches, so that the workload directed to web servers can be reduced. In experiments, we use Kad as the particular P2P network for the realization of a large-scale distributed web cache. We performed comprehensive evaluation on the feasibility, efficiency and robustness of our scheme, through experiments and simulations on the prototype we implemented. The evaluation results show that our scheme can increase the capacity of the protected web servers at least 10 times at the same cost of connection and bandwidth consumption. The web contents cached in Kad remain reachable even under churn of peers and targeted DoS attack, and the access latency is comparable to normal direct access to web servers. It also achieves good load balancing under the heavy-tailed distribution of object popularity.

A Simple Effective Scheme to Enhance the Capability of Web Servers Using P2P Networks

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Mitigating application layer distributed denial of service attacks via effective trust management

A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks

Monitoring, analyzing and characterizing lookup traffic in a large-scale DHT

A Simple Effective Scheme to Enhance the Capability of Web Servers Using P2P Networks