Distinguishing DDoS attacks from flash crowds using probability metrics

Both Flash crowds and DDoS (Distributed Denial-of-Service) attacks have very similar properties in terms of internet traffic, however Flash crowds are legitimate flows and DDoS attacks are illegitimate flows, and DDoS attacks have been a serious threat to internet security and stability. In this paper we propose a set of novel methods using probability metrics to distinguish DDoS attacks from Flash crowds effectively, and our simulations show that the proposed methods work well. In particular, these mathods can not only distinguish DDoS attacks from Flash crowds clearly, but also can distinguish the anomaly flow being DDoS attacks flow or being Flash crowd flow from Normal network flow effectively. Furthermore, we show our proposed hybrid probability metrics can greatly reduce both false positive and false negative rates in detection.

Effective DDoS attacks detection using generalized entropy metric

In information theory, entropies make up of the basis for distance and divergence measures among various probability densities. In this paper we propose a novel metric to detect DDoS attacks in networks by using the function of order α of the generalized (Rényi) entropy to distinguish DDoS attacks traffic from legitimate network traffic effectively. Our proposed approach can not only detect DDoS attacks early (it can detect attacks one hop earlier than using the Shannon metric while order α=2, and two hops earlier to detect attacks while order α=10.) but also reduce both the false positive rate and the false negative rate clearly compared with the traditional Shannon entropy metric approach.

Traceback of DDoS attacks using entropy variations

Distributed Denial-of-Service (DDoS) attacks are a critical threat to the Internet. However, the memoryless feature of the Internet routing mechanisms makes it extremely hard to trace back to the source of these attacks. As a result, there is no effective and efficient method to deal with this issue so far. In this paper, we propose a novel traceback method for DDoS attacks that is based on entropy variations between normal and DDoS attack traffic, which is fundamentally different from commonly used packet marking techniques. In comparison to the existing DDoS traceback methods, the proposed strategy possesses a number of advantagesit is memory nonintensive, efficiently scalable, robust against packet pollution, and independent of attack traffic patterns. The results of extensive experimental and simulation studies are presented to demonstrate the effectiveness and efficiency of the proposed method. Our experiments show that accurate traceback is possible within 20 seconds (approximately) in a large-scale attack network with thousands of zombies.

CALD : surviving various application-layer DDoS attacks that mimic flash crowd

Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when suchattacks mimic or occur during the flash crowd event of a popular Website. In this paper, we present the design and implementation of CALD, an architectural extension to protect Web servers against various DDoS attacks that masquerade as flash crowds. CALD provides real-time detection using mess tests but is different from other systems that use resembling methods. First, CALD uses a front-end sensor to monitor thetraffic that may contain various DDoS attacks or flash crowds. Intense pulse in the traffic means possible existence of anomalies because this is the basic property of DDoS attacks and flash crowds. Once abnormal traffic is identified, the sensor sends ATTENTION signal to activate the attack detection module. Second, CALD dynamically records the average frequency of each source IP and check the total mess extent. Theoretically, the mess extent of DDoS attacks is larger than the one of flash crowds. Thus, with some parameters from the attack detection module, the filter is capable of letting the legitimate requests through but the attack traffic stopped. Third, CALD may divide the security modules away from the Web servers. As a result, it keeps maximum performance on the kernel web services, regardless of the harassment from DDoS. In the experiments, the records from www.sina.com and www.taobao.com have proved the value of CALD.

Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

Keynote : Detection of and defense against distributed denial-of-service (DDoS) attacks

Distributed denial-of-service (DDoS) attacks typically exhaust bandwidth, processing capacity, or memory of a targeted machine, service or network. Despite enormous efforts in combating DDoS attacks in the past decade, DDoS attacks are still a serious threat to the security of cyberspace. In this talk I shall outline the recent efforts of my research group in detection of and defence against DDoS attacks. In particular, this talk will concentrate on the following three critical issues related to DDoS attacks: (1) Traceback of DDoS attacks; (2) Detection of low-rate DDoS attacks; and (3) Discriminating DDoS attacks from flash crowds.

Detection and defense of application-layer DDoS attacks in backbone web traffic

Web servers are usually located in a well-organized data center where these servers connect with the outside Internet directly through backbones. Meanwhile, the application-layer distributed denials of service (AL-DDoS) attacks are critical threats to the Internet, particularly to those business web servers. Currently, there are some methods designed to handle the AL-DDoS attacks, but most of them cannot be used in heavy backbones. In this paper, we propose a new method to detect AL-DDoS attacks. Our work distinguishes itself from previous methods by considering AL-DDoS attack detection in heavy backbone traffic. Besides, the detection of AL-DDoS attacks is easily misled by flash crowd traffic. In order to overcome this problem, our proposed method constructs a Real-time Frequency Vector (RFV) and real-timely characterizes the traffic as a set of models. By examining the entropy of AL-DDoS attacks and flash crowds, these models can be used to recognize the real AL-DDoS attacks. We integrate the above detection principles into a modularized defense architecture, which consists of a head-end sensor, a detection module and a traffic filter. With a swift AL-DDoS detection speed, the filter is capable of letting the legitimate requests through but the attack traffic is stopped. In the experiment, we adopt certain episodes of real traffic from Sina and Taobao to evaluate our AL-DDoS detection method and architecture. Compared with previous methods, the results show that our approach is very effective in defending AL-DDoS attacks at backbones. © 2013 Elsevier B.V. All rights reserved.

Can we beat DDoS attacks in clouds?

Cloud is becoming a dominant computing platform. Naturally, a question that arises is whether we can beat notorious DDoS attacks in a cloud environment. Researchers have demonstrated that the essential issue of DDoS attack and defense is resource competition between defenders and attackers. A cloud usually possesses profound resources and has full control and dynamic allocation capability of its resources. Therefore, cloud offers us the potential to overcome DDoS attacks. However, individual cloud hosted servers are still vulnerable to DDoS attacks if they still run in the traditional way. In this paper, we propose a dynamic resource allocation strategy to counter DDoS attacks against individual cloud customers. When a DDoS attack occurs, we employ the idle resources of the cloud to clone sufficient intrusion prevention servers for the victim in order to quickly filter out attack packets and guarantee the quality of the service for benign users simultaneously. We establish a mathematical model to approximate the needs of our resource investment based on queueing theory. Through careful system analysis and real-world data set experiments, we conclude that we can defeat DDoS attacks in a cloud environment. © 2013 IEEE.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

Ensemble-based DDoS detection and mitigation model

This work-in-progress paper presents an ensemble-based model for detecting and mitigating Distributed Denial-of-Service (DDoS) attacks, and its partial implementation. The model utilises network traffic analysis and MIB (Management Information Base) server load analysis features for detecting a wide range of network and application layer DDoS attacks and distinguishing them from Flash Events. The proposed model will be evaluated against realistic synthetic network traffic generated using a software-based traffic generator that we have developed as part of this research. In this paper, we summarise our previous work, highlight the current work being undertaken along with preliminary results obtained and outline the future directions of our work.

Detecting distributed Denial-of-Service attacks and Flash Events

This thesis investigates and develops techniques for accurately detecting Internet-based Distributed Denial-of-Service (DDoS) Attacks where an adversary harnesses the power of thousands of compromised machines to disrupt the normal operations of a Web-service provider, resulting in significant down-time and financial losses. This thesis also develops methods to differentiate these attacks from similar-looking benign surges in web-traffic known as Flash Events (FEs). This thesis also addresses an intrinsic challenge in research associated with DDoS attacks, namely, the extreme scarcity of public domain datasets (due to legal and privacy issues) by developing techniques to realistically emulate DDoS attack and FE traffic.

A framework for generating realistic traffic for distributed denial-of-service attacks and flash events

An intrinsic challenge associated with evaluating proposed techniques for detecting Distributed Denial-of-Service (DDoS) attacks and distinguishing them from Flash Events (FEs) is the extreme scarcity of publicly available real-word traffic traces. Those available are either heavily anonymised or too old to accurately reflect the current trends in DDoS attacks and FEs. This paper proposes a traffic generation and testbed framework for synthetically generating different types of realistic DDoS attacks, FEs and other benign traffic traces, and monitoring their effects on the target. Using only modest hardware resources, the proposed framework, consisting of a customised software traffic generator, ‘Botloader’, is capable of generating a configurable mix of two-way traffic, for emulating either large-scale DDoS attacks, FEs or benign traffic traces that are experimentally reproducible. Botloader uses IP-aliasing, a well-known technique available on most computing platforms, to create thousands of interactive UDP/TCP endpoints on a single computer, each bound to a unique IP-address, to emulate large numbers of simultaneous attackers or benign clients.

Evaluation of TFTP DDoS Amplification Attack.

Web threats are becoming a major issue for both governments and companies. Generally, web threats increased as much as 600% during last year (WebSense, 2013). This appears to be a significant issue, since many major businesses seem to provide these services. Denial of Service (DoS) attacks are one of the most significant web threats and generally their aim is to waste the resources of the target machine (Mirkovic & Reiher, 2004). Dis-tributed Denial of Service (DDoS) attacks are typically executed from many sources and can result in large traf-fic flows. During last year 11% of DDoS attacks were over 60 Gbps (Prolexic, 2013a). The DDoS attacks are usually performed from the large botnets, which are networks of remotely controlled computers. There is an increasing effort by governments and companies to shut down the botnets (Dittrich, 2012), which has lead the attackers to look for alternative DDoS attack methods. One of the techniques to which attackers are returning to is DDoS amplification attacks. Amplification attacks use intermediate devices called amplifiers in order to amplify the attacker's traffic. This work outlines an evaluation tool and evaluates an amplification attack based on the Trivial File Transfer Proto-col (TFTP). This attack could have amplification factor of approximately 60, which rates highly alongside other researched amplification attacks. This could be a substantial issue globally, due to the fact this protocol is used in approximately 599,600 publicly open TFTP servers. Mitigation methods to this threat have also been consid-ered and a variety of countermeasures are proposed. Effects of this attack on both amplifier and target were analysed based on the proposed metrics. While it has been reported that the breaching of TFTP would be possible (Schultz, 2013), this paper provides a complete methodology for the setup of the attack, and its verification.

A Decade of DDoS

A summary timeline by Arbor Networks of how DDoS attacks have evolved.