The objective of this research is to show an analytical intrusion detection framework (AIDF) comprised of (i) a probability model discovery approach, and (ii) a probabilistic inference mechanism for generating the most probable forensic explanation based on not only just the observed intrusion detection alerts, but also the unreported signature rules that are revealed in the probability model. ...

Brain-inspired spiking neural networks (SNNs) have recently drawn more and attention due to their event-driven energy-efficient characteristics. The integration of storage computation paradigm on neuromorphic hardwares makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we argue that may not benefit the weight-sharing mechanism, which can effectively reduce parameters imp...

ÐThis paper investigates the problem of inference channels that occur when database constraints are combined with nonsensitive data to obtain sensitive information. We present an integrated security mechanism, called the Disclosure Monitor, which guarantees data confidentiality by extending the standard mandatory access control mechanism with a Disclosure Inference Engine. The Disclosure Infere...

This research focuses on the development of an inference mechanism based on a particular variety of non-axiomatic systems known as Ontological Semantics. Systems with a heavy semantics emphasis and dynamic learning capabilities indicate a greater potential in inference-related applications, largely due to the structure of the resources which allows implementation of mixed methods: traditional d...

This paper deals with inference using multiple patterns. Inference among vague information, e.g. patterns, has caused \explosion of vagueness". This paper proposes a new mechanism to suppress the increase of vagueness. This paper also proposes a modi cation method for pattern space based on a new concept of vagueness in patterns. Simulations are done to demonstrate the potentiality of the propo...

Systems of set constraints describe relations between sets of ground terms. They have been successfuly used in program analysis and type inference. So far two proofs of decidability of mixed set constraints have been given: by R. Gilleron, S. Tison and M. Tommasi [12] and A. Aiken, D. Kozen, and E.L. Wimmers [3], but both these proofs are very long, involved and difficult to follow. We first gi...

Probabilistic inference is a key mechanism for reasoning about probabilistic programs. Since exact inference is theoretically expensive, most probabilistic inference systems today have adopted approximate inference techniques, which trade precision for better performance (but often without guarantees). As a result, while desirable for its ultimate precision, the practical effectiveness of exact...

We investigate the usage of so-called inference rights. We point out the problems arising from the innexibility of existing approaches to heuristically control the search of automated deduction systems, and we propose the application of inference rights that are well-suited for controlling the search more exibly. Moreover , inference rights allow for a mechanism of \partial forgetting" of facts...

Focused reasoning is a method for continuing a specific inference task as soon as rules or facts which may assist in the reasoning are added to the knowledge base without repeating completed inference, re-posing queries, or performing unnecessary inference. Determining if focused reasoning should commence uses very few computational resources above those used normally to add a term to a knowled...