Using IDDs for Packet Filtering

Firewalls are one of the key technologies used to control the traffic going in and out of a network. A central feature of the firewall is the packet filter. In this paper, we propose a complete framework for packet classification. Through two applications we demonstrate that both performance and security can be improved. We show that a traditional ordered rule set can always be expressed as a first-order logic formula on integer variables. Moreover, we emphasize that, with such specification, the packet filtering problem is known to be constant time. We propose to represent the first-order logic formula as Interval Decision Diagrams. This structure has several advantages. First, the algorithm for removing redundancy and unnecessary tests is very simple. Secondly, it allows us to handle integer variables which makes it efficient on a generic CPUs. And, finally, we introduce an extension of IDDs called Multi-Terminal Interval Decision Diagrams in order to deal with any number of policies. In matter of efficiency, we evaluate the performance our framework through a prototype toolkit composed by a compiler and a packet filter. The results of the experiments shows that this method is efficient in terms of CPU usage and has a low storage requirements. Finally, we outline a tool, called Network Access Verifier. This tool demonstrates how the IDD representation can be used for verifying access properties of a network. In total, potentially improving the security of a network

Fuzzy-logic framework for future dynamic cellular systems

There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data

Abduction-Based Explanations for Machine Learning Models

The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper develops a constraint-agnostic solution for computing explanations for any ML model. The proposed solution exploits abductive reasoning, and imposes the requirement that the ML model can be represented as sets of constraints using some target constraint reasoning system for which the decision problem can be answered with some oracle. The experimental results, obtained on well-known datasets, validate the scalability of the proposed approach as well as the quality of the computed solutions

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments