AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Eliminating receive livelock in an interrupt-driven kernel

Abstract: Most operating systems use interface interrupts to schedule network tasks. Interrupt-driven systems can provide low overhead and good latency at low offered load, but degrade significantly at higher arrival rates unless care is taken to prevent several pathologies. These are various forms ofreceive livelock, in which the system spends all of its time processing interrupts, to the exclusion of other necessary tasks. Under extreme conditions, no packets are delivered to the user application or the output of the system. To avoid livelock and related problems, an operating system must schedule network interrupt handling as carefully as it schedules process execution. We modified an interrupt-driven networking implementation to do so; this modification eliminates receive livelock without degrading other aspects of system performance. Our modifications include the use of polling when the system is heavily loaded, while retaining the use of interrupts ur.Jer lighter load. We present measurements demonstrating the success of our approach.

Wide-scale botnet detection and characterization

Abstract: Malicious botnets are networks of compromised computers that are controlled remotely to perform large-scale distributed denial-of-service (DDoS) attacks, send spam, trojan and phishing emails, distribute pirated media or conduct other usually illegitimate activities. This paper describes a methodology to detect, track and characterize botnets on a large Tier-1 ISP network. The approach presented here differs from previous attempts to detect botnets by employing scalable non-intrusive algorithms that analyze vast amounts of summary traffic data collected on selected network links. Our botnet analysis is performed mostly on transport layer data and thus does not depend on particular application layer information. Our algorithms produce alerts with information about controllers. Alerts are followed up with analysis of application layer data, that indicates less than 2% false positive rates.

Similarity-Based Models of Word Cooccurrence Probabilities

Abstract: In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination For example, a speech recognizer may need to determine which of the two word combinations “eat a peach” and ”eat a beach” is more likely Statistical NLP methods determine the likelihood of a word combination from its frequency in a training corpus However, the nature of language is such that many word combinations are infrequent and do not occur in any given corpus In this work we propose a method for estimating the probability of such previously unseen word combinations using available information on “most similar” words We describe probabilistic word association models based on distributional word similarity, and apply them to two tasks, language modeling and pseudo-word disambiguation In the language modeling task, a similarity-based model is used to improve probability estimates for unseen bigrams in a back-off language model The similarity-based method yields a 20% perplexity improvement in the prediction of unseen bigrams and statistically significant reductions in speech-recognition error We also compare four similarity-based estimation methods against back-off and maximum-likelihood estimation methods on a pseudo-word sense disambiguation task in which we controlled for both unigram and bigram frequency to avoid giving too much weight to easy-to-disambiguate high-frequency configurations The similarity-based methods perform up to 40% better on this particular task

Optimizing dialogue management with reinforcement learning: experiments with the NJFun system

Abstract: Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.