This paper describes a technique for tracing anonymous packet flooding attacks in the Internet back towards their source. This work is motivated by the increased frequency and sophistication of denial-of-service attacks and by the difficulty in tracing packets with incorrect, or “spoofed”, source addresses. In this paper we describe a general purpose traceback mechanism based on probabilistic packet marking in the network. Our approach allows a victim to identify the network path(s) traversed by attack traffic without requiring interactive operational support from Internet Service Providers (ISPs). Moreover, this traceback can be performed “post-mortem ” – after an attack has completed. We present an implementation of this technology that is incrementally deployable, (mostly) backwards compatible and can be efficiently implemented using conventional technology. 1.

This paper presents a model for predicting the convergence quality of genetic algorithms. The model incorporates previous knowledge about decision making in genetic algorithms and the initial supply of building blocks in a novel way. The result is an equation that accurately predicts the quality of the solution found by a GA using a given population size. Adjustments for different selection intensities are considered and computational experiments demonstrate the effectiveness of the model. I. Introduction The size of the population in a genetic algorithm (GA) is a major factor in determining the quality of convergence. The question of how to choose an adequate population size for a particular domain is difficult and has puzzled GA practitioners for a long time. Hard questions are better approached using a divide-and-conquer strategy and the population sizing issue is no exception. In this case, we can identify two factors that influence convergence quality: the initial supply of build...

Parallel implementations of genetic algorithms (GAs) are common, and, in most cases, they succeed to reduce the time required to find acceptable solutions. However, the effect of the parameters of parallel GAs on the quality of their search and on their efficiency are not well understood. This insufficient knowledge limits our ability to design fast and accurate parallel GAs that reach the desired solutions in the shortest time possible. The goal of this dissertation is to advance the understanding of parallel GAs and to provide rational guidelines for their design. The research reported here considered three major types of parallel GAs: simple master-slave algorithms with one population, more sophisticated algorithms with multiple populations, and a hierarchical combination of the first two types. The investigation formulated simple models that predict accurately the quality of the solutions with different parameter settings. The quality predictors were transformed into population-sizing equations, which in turn were used to estimate the execution time of the algorithms.

When a joint distribution PF is given to a set F of facts in a logic program DB = F U R where R is a set of rules, we can further extend it to a joint distribution PDB over the set of possible least models of DB. We then define the semantics of DB with the associated distribution PF as PDB, and call it distribution semantics. While the

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In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that this learning procedure is asymptotically successful: with probability one, it will converge to the target distribution, given a sufficient number of samples. However, the rate of this convergence has been hitherto unknown. In this work we examine the sample complexity of MDL based learning procedures for Bayesian networks. We show that the number of samples needed to learn an ffl-close approximation (in terms of entropy distance) with confidence ffi is O i ( 1 ffl ) 4 3 log 1 ffl log 1 ffi log log 1 ffi j . This means that the sample complexity is a low-order polynomial in the error threshold and sublinear in the confidence bound. We also discuss how the constants in this term depend on the complexity of the target distribution. F...

Theories in “soft” areas of psychology lack the cumulative character of scientific knowledge. They tend neither to be refuted nor corroborated, but instead merely fade away as people lose interest. Even though intrinsic subject matter difficulties (20 listed) contribute to this, the excessive reliance on significance testing is partly responsible, being a poor way of doing science. Karl Popper’s approach, with modifications, would be prophylactic. Since the null hypothesis is quasi-always false, tables summarizing research in terms of patterns of “significant differences” are little more than complex, causally uninterpretable outcomes of statistical power functions. Multiple paths to estimating numerical point values (“consistency tests”) are better, even if approximate with rough tolerances; and lacking this, ranges, orderings, second-order differences, curve peaks and valleys, and function forms should be used. Such methods are usual in developed sciences that seldom report statistical significance. Consistency tests of a conjectural taxometric model yielded 94 % success with zero false negatives.

this paper, we use simple biophysical models dent unreliable synapses provides the drive to an integrate-and-fire of spike transduction and stochastic synaptic release to ex- neuron. Within this model, the mutual information between the plore the implications of synaptic unreliability on informa- synaptic drive and the resulting output spike train can be computed tion transmission and neural coding in the cortex. Our goal exactly from distributions that depend only on a single variable, is to provide a quantitative answer to the question: How the interspike interval. The reduction of the calculation to depen- much information can the output spike train provide about dence on only a single variable greatly reduces the amount of data the synaptic inputs? Our answer will be cast in an informa- required to obtain reliable information estimates. We consider two tion-theoretic framework. factors that govern the rate of information transfer: the synaptic reliability and the number of synapses connecting each presynaptic METHODS

Abstract In wireless ad-hoc broadcast networks the pairing problem consists of establishing a (long-term) connection between two specific physical nodes in the network that do not yet know each other. We focus on the ephemeral version of this problem. Ephemeral pairings occur, for example, when electronic business cards are exchanged between two people that meet, or when one pays at a check-out using a wireless wallet. This problem can, in more abstract terms, be phrased as an ephemeral key exchange problem: given a low bandwidth authentic (or private) communication channel between two nodes, and a high bandwidth broadcast channel, can we establish a high-entropy shared secret session key between the two nodes without relying on any a priori shared secret information. Apart from introducing this new problem, we present several ephemeral key exchange protocols, both for the case of authentic channels as well as for the case of private channels.

This paper presents a study of parallel genetic algorithms (GAs) with multiple populations (also called demes or islands). The study makes explicit the relation between the probability of reaching a desired solution with the deme size, the migration rate, and the degree of the connectivity graph. The paper considers arbitrary topologies with a fixed number of neighbors per deme. The demes evolve in isolation until each converges to a unique solution. Then, the demes exchange an arbitrary number of individuals and restart their execution. An accurate deme-sizing equation is derived, and it is used to determine the optimal configuration of an arbitrary number of demes that minimizes the execution time of the parallel GA. 1 INTRODUCTION Parallel genetic algorithms (GAs) with multiple populations are difficult to configure because they are controlled by many parameters that affect their efficiency and accuracy. Among other things, one must decide the number and the size of the populations...