Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks

Stealthy attackers move patiently through computer networks - taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a challenge. This paper presents an efficient monitoring technique for stealthy attacks. It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection. A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented. The Bayesian fusion along with traffic sampling is employed as a data reduction method. The proposed method has the ability to monitor stealthy activities using 10-20% size sampling rates without degrading the quality of detection.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

Fusion rules for merging uncertain information

In previous papers, we have presented a logic-based framework based on fusion rules for merging structured news reports. Structured news reports are XML documents, where the textentries are restricted to individual words or simple phrases, such as names and domain-specific terminology, and numbers and units. We assume structured news reports do not require natural language processing. Fusion rules are a form of scripting language that define how structured news reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured news reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. It is expected that a set of fusion rules is defined for any given application. In this paper we extend the approach to handling probability values, degrees of beliefs, or necessity measures associated with textentries in the news reports. We present the formal definition for each of these types of uncertainty and explain how they can be handled using fusion rules. We also discuss the methods of detecting inconsistencies among sources.

Fast heating of ultrahigh-density plasma as a step towards laser fusion ignition

ate studies(2) and fusion energy research(3,4). Laser-driven implosions of spherical polymer shells have, for example, achieved an increase in density of 1,000 times relative to the solid state(5). These densities are large enough to enable controlled fusion, but to achieve energy gain a small volume of compressed fuel (known as the 'spark') must be heated to temperatures of about 10(8) K (corresponding to thermal energies in excess of 10 keV). In the conventional approach to controlled fusion, the spark is both produced and heated by accurately timed shock waves(4), but this process requires both precise implosion symmetry and a very large drive energy. In principle, these requirements can be significantly relaxed by performing the compression and fast heating separately(6-10); however, this 'fast ignitor' approach(7) also suffers drawbacks, such as propagation losses and deflection of the ultra-intense laser pulse by the plasma surrounding the compressed fuel. Here we employ a new compression geometry that eliminates these problems; we combine production of compressed matter in a laser-driven implosion with picosecond-fast heating by a laser pulse timed to coincide with the peak compression. Our approach therefore permits efficient compression and heating to be carried out simultaneously, providing a route to efficient fusion energy production.

Fast Heating scalable to laser fusion ignition

Rapid heating of a compressed fusion fuel by a short-duration laser pulse is a promising route to generating energy by nuclear fusion1, and has been demonstrated on an experimental scale using a novel fast-ignitor geometry2. Here we describe a refinement of this system in which a much more powerful, pulsed petawatt (1015 watts) laser creates a fastheated core plasma that is scalable to fullscale ignition, significantly increasing the number of fusion events while still maintaining high heating efficiency at these substantially higher laser energies. Our findings bring us a step closer to realizing the production of relatively inexpensive, full-scale fast-ignition laser facilities.