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.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

Subtyping based on readiness and confidence: the identification of help-seeking profiles for gamblers accessing web-based counselling

BACKGROUND AND AIMS: Problem gamblers are not a homogeneous group and recent data suggest that subtyping can improve treatment outcomes. This study administered three readiness rulers and aimed to identify subtypes of gamblers accessing a national web-based counselling service based on these rulers. METHODS: Participants were 1204 gamblers (99.4% problem gamblers) who accessed a single session of web-based counselling in Australia. Measures included three readiness rulers (importance, readiness and confidence to resist an urge to gamble), demographics and the Problem Gambling Severity Index (PGSI). RESULTS: Gamblers reported high importance of change [mean = 9.2, standard deviation (SD) = 1.51] and readiness to change (mean = 8.86, SD = 1.84), but lower confidence to resist an urge to gamble (mean = 3.93, SD = 2.44) compared with importance and readiness. The statistical fit indices of a latent class analysis identified a four-class model. Subtype 1 was characterized by a very high readiness to change and very low confidence to resist an urge to gamble (n = 662, 55.0%) and subtype 2 reported high readiness and low confidence (n = 358, 29.7%). Subtype 3 reported moderate ratings on all three rulers (n = 139, 11.6%) and subtype 4 reported high importance of change but low readiness and confidence (n = 45, 3.7%). A multinomial logistic regression indicated that subtypes differed by gender (P < 0.001), age (P = 0.01), gambling activity (P < 0.05), preferred mode of gambling (P < 0.001) and PGSI score (P < 0.001). CONCLUSIONS: Problem gamblers in Australia who seek web-based counselling comprise four distinct subgroups based on self-reported levels of readiness to change, confidence to resist the urge to gamble and importance of change.

Exploring the effectiveness of true abnormal data elimination in context-aware web services recommendation

Recent years have witnessed a growing interest in context-aware recommender system (CARS), which explores the impact of context factors on personalized Web services recommendation. Basically, the general idea of CARS methods is to mine historical service invocation records through the process of context-aware similarity computation. It is observed that traditional similarity mining process would very likely generate relatively big deviations of QoS values, due to the dynamic change of contexts. As a consequence, including a considerable amount of deviated QoS values in the similarity calculation would probably result in a poor accuracy for predicting unknown QoS values. In allusion to this problem, this paper first distinguishes two definitions of Abnormal Data and True Abnormal Data, the latter of which should be eliminated. Second, we propose a novel CASR-TADE method by incorporating the effectiveness of True Abnormal Data Elimination into context-aware Web services recommendation. Finally, the experimental evaluations on a real-world Web services dataset show that the proposed CASR-TADE method significantly outperforms other existing approaches.