Browsing behavior mimicking attacks on popular web sites for large botnets

With the significant growth of botnets, application layer DDoS attacks are much easier to launch using large botnet, and false negative is always a problem for intrusion detection systems in real practice. In this paper, we propose a novel application layer DDoS attack tool, which mimics human browsing behavior following three statistical distributions, the Zipf-like distribution for web page popularity, the Pareto distribution for page request time interval for an individual browser, and the inverse Gaussian distribution for length of browsing path. A Markov model is established for individual bot to generate attack request traffic. Our experiments indicated that the attack traffic that generated by the proposed tool is pretty similar to the real traffic. As a result, the current statistics based detection algorithms will result high false negative rate in general. In order to counter this kind of attacks, we discussed a few preliminary solutions at the end of this paper.

DDoS discrimination by linear discriminant analysis (LDA)

In this paper, we propose an effective approach with a supervised learning system based on Linear Discriminant Analysis (LDA) to discriminate legitimate traffic from DDoS attack traffic. Currently there is a wide outbreak of DDoS attacks that remain risky for the entire Internet. Different attack methods and strategies are trying to challenge defence systems. Among the behaviours of attack sources, repeatable and predictable features differ from source of legitimate traffic. In addition, the DDoS defence systems lack the learning ability to fine-tune their accuracy. This paper analyses real trace traffic from publicly available datasets. Pearson's correlation coefficient and Shannon's entropy are deployed for extracting dependency and predictability of traffic data respectively. Then, LDA is used to train and classify legitimate and attack traffic flows. From the results of our experiment, we can confirm that the proposed discrimination system can differentiate DDoS attacks from legitimate traffic with a high rate of accuracy.

Distributed denial of service (DDoS) detection by traffic pattern analysis

In this paper, we propose a behavior-based detection that can discriminate Distributed Denial of Service (DDoS) attack traffic from legitimated traffic regardless to various types of the attack packets and methods. Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission rates and packet forms to beat defense systems. These various attack strategies lead to defense systems requiring various detection methods in order to identify the attacks. Moreover, DDoS attacks can craft the traffics like flash crowd events and fly under the radar through the victim. We notice that DDoS attacks have features of repeatable patterns which are different from legitimate flash crowd traffics. In this paper, we propose a comparable detection methods based on the Pearson’s correlation coefficient. Our methods can extract the repeatable features from the packet arrivals in the DDoS traffics but not in flash crowd traffics. The extensive simulations were tested for the optimization of the detection methods. We then performed experiments with several datasets and our results affirm that the proposed methods can differentiate DDoS attacks from legitimate traffics.

DDoS attack detection at local area networks using information theoretical metrics

DDoS attacks are one of the major threats to Internet services. Sophisticated hackers are mimicking the features of legitimate network events, such as flash crowds, to fly under the radar. This poses great challenges to detect DDoS attacks. In this paper, we propose an attack feature independent DDoS flooding attack detection method at local area networks. We employ flow entropy on local area network routers to supervise the network traffic and raise potential DDoS flooding attack alarms when the flow entropy drops significantly in a short period of time. Furthermore, information distance is employed to differentiate DDoS attacks from flash crowds. In general, the attack traffic of one DDoS flooding attack session is generated by many bots from one botnet, and all of these bots are executing the same attack program. As a result, the similarity among attack traffic should higher than that among flash crowds, which are generated by many random users. Mathematical models have been established for the proposed detection strategies. Analysis based on the models indicates that the proposed methods can raise the alarm for potential DDoS flooding attacks and can differentiate DDoS flooding attacks from flash crowds with conditions. The extensive experiments and simulations confirmed the effectiveness of our proposed detection strategies.

A dynamical deterministic packet marking scheme for DDoS traceback

DDoS attack source traceback is an open and challenging problem. Deterministic packet marking (DPM) is a simple and relatively effective traceback scheme among the available traceback methods. However, the existing DPM schemes inheret a critical drawback of scalability in tracing all possible attack sources, which roots at their static mark encoding and attempt to mark all Internet routers for their traceback purpose. We find that a DDoS attack session usually involves a limited number of attack sources, e.g. at the thousand level. In order to achieve the traceback goal, we only need to mark these attack related routers. We therefore propose a novel Marking on Demand (MOD) scheme based on the DPM mechanism to dynamical distribute marking IDs in both temporal and space dimensions. The proposed MOD scheme can traceback to all possible sources of DDoS attacks, which is not possible for the existing DPM schemes. We thoroughly compare the proposed MOD scheme with two dominant DPM schemes through theoretical analysis and experiments. The the results demonstrate that the MOD scheme outperforms the existing DPM schemes. © 2013 IEEE.

Detection on application layer DDoS using random walk model

Application Layer Distributed Denial of Service (ALDDoS) attacks have been increasing rapidly with the growth of Botnets and Ubiquitous computing. Differentiate to the former DDoS attacks, ALDDoS attacks cannot be efficiently detected, as attackers always adopt legitimate requests with real IP address, and the traffic has high similarity to legitimate traffic. In spite of that, we think, the attackers' browsing behavior will have great disparity from that of the legitimate users'. In this paper, we put forward a novel user behavior-based method to detect the application layer asymmetric DDoS attack. We introduce an extended random walk model to describe user browsing behavior and establish the legitimate pattern of browsing sequences. For each incoming browser, we observe his page request sequence and predict subsequent page request sequence based on random walk model. The similarity between the predicted and the observed page request sequence is used as a criterion to measure the legality of the user, and then attacker would be detected based on it. Evaluation results based on real collected data set has demonstrated that our method is very effective in detecting asymmetric ALDDoS attacks. © 2014 IEEE.

Fool me if you can: mimicking attacks and anti-attacks in cyberspace

Botnets have become major engines for malicious activities in cyberspace nowadays. To sustain their botnets and disguise their malicious actions, botnet owners are mimicking legitimate cyber behavior to fly under the radar. This poses a critical challenge in anomaly detection. In this paper, we use web browsing on popular web sites as an example to tackle this problem. First of all, we establish a semi-Markov model for browsing behavior. Based on this model, we find that it is impossible to detect mimicking attacks based on statistics if the number of active bots of the attacking botnet is sufficiently large (no less than the number of active legitimate users). However, we also find it is hard for botnet owners to satisfy the condition to carry out a mimicking attack most of the time. With this new finding, we conclude that mimicking attacks can be discriminated from genuine flash crowds using second order statistical metrics. We define a new fine correntropy metrics and show its effectiveness compared to others. Our real world data set experiments and simulations confirm our theoretical claims. Furthermore, the findings can be widely applied to similar situations in other research fields.

Protecting web services with service oriented traceback architecture

Service oriented architecture (SOA) is a way of reorganizing software infrastructure into a set of service abstracts. In the area of applying SOA to Web service security, there have been some well defined security dimensions. However, current Web security systems, like WS-Security are not efficient enough to handle distributed denial of service (DDoS) attacks. Our new approach, service oriented traceback architecture (SOTA), provides a framework to be able to identify the source of an attack. This is accomplished by deploying our defence system at distributed routers, in order to examine the incoming SOAP messages and place our own SOAP header. By this method, we can then use the new SOAP header information, to traceback through the network the source of the attack. According to our experimental performance evaluations, we find that SOTA is quite scaleable, simple and quite effective at identifying the source.

Protecting information systems from DDoS attack using multicore methodology

Previous work, in the area of defense systems has focused on developing a firewall like structure, in order to protect applications from attacks. The major drawback for implementing security in general, is that it affects the performance of the application they are trying to protect. In fact, most developers avoid implementing security at all. With the coming of new multicore systems, we might at last be able to minimize the performance issues that security places on applications. In our bodyguard framework we propose a new kind of defense that acts alongside, not in front, of applications. This means that performance issues that effect system applications are kept to a minimum, but at the same time still provide high grade security. Our experimental results demonstrate that a ten to fifteen percent speedup in performance is possible, with the potential of greater speedup.

Protecting web services from distributed denial of service attacks

The outcome of the research was the development of three network defence systems to protect corporate network infrastructure. The results showed that these defences were able to detect and filter around 94% of the DDoS attack traffic within a matter of seconds.

Discriminating DDoS flows from flash crowds using information distance

Discriminating DDoS flooding attacks from flash crowds poses a tough challenge for the network security community. Because of the vulnerability of the original design of the Internet, attackers can easily mimic the patterns of legitimate network traffic to fly under the radar. The existing fingerprint or feature based algorithms are incapable to detect new attack strategies. In this paper, we aim to differentiate DDoS attack flows from flash crowds. We are motivated by the following fact: the attack flows are generated by the same prebuilt program (attack tools), however, flash crowds come from randomly distributed users all over the Internet. Therefore, the flow similarity among DDoS attack flows is much stronger than that among flash crowds. We employ abstract distance metrics, the Jeffrey distance, the Sibson distance, and the Hellinger distance to measure the similarity among flows to achieve our goal. We compared the three metrics and found that the Sibson distance is the most suitable one for our purpose. We apply our algorithm to the real datasets and the results indicate that the proposed algorithm can differentiate them with an accuracy around 65%.

A microscopic competition model and its dynamics analysis on network attacks

Modeling network traffic has been a critical task in the development of Internet. Attacks and defense are prevalent in the current Internet. Traditional network models such as Poisson-related models do not consider the competition behaviors between the attack and defense parties. In this paper, we present a microscopic competition model to analyze the dynamics among the nodes, benign or malicious, connected to a router, which compete for the bandwidth. The dynamics analysis demonstrates that the model can well describe the competition behavior among normal users and attackers. Based on this model, an anomaly attack detection method is presented. The method is based on the adaptive resonance theory, which is used to learn the model by normal traffic data. The evaluation shows that it can effectively detect the network attacks.

Algebraic attacks over GF(q)

Recent algebraic attacks on LFSR-based stream ciphers and S-boxes have generated much interest as they appear to be extremely powerful. Theoretical work has been developed focusing around the Boo- lean function case. In this paper, we generalize this theory to arbitrary finite fields and extend the theory of annihilators and ideals introduced at Eurocrypt 2004 by Meier, Pasalic and Carlet. In particular, we prove that for any function f in the multivariate polynomial ring over GF(q), f has a low degree multiple precisely when two low degree functions appear in the same coset of the annihilator of f ^{q – 1} – 1. In this case, many such low degree multiples exist.

Detecting collusion attacks in security protocols

Security protocols have been widely used to safeguard secure electronic transactions. We usually assume that principals are credible and shall not maliciously disclose their individual secrets to someone else. Nevertheless, it is impractical to completely ignore the possibility that some principals may collude in private to achieve a fraudulent or illegal purpose. Therefore, it is critical to address the possibility of collusion attacks in order to correctly analyse security protocols. This paper proposes a framework by which to detect collusion attacks in security protocols. The possibility of security threats from insiders is especially taken into account. The case study demonstrates that our methods are useful and promising in discovering and preventing collusion attacks.