Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.

In a previous paper the authors argued the case for incorporating ideas from innate immunity into articficial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were hughlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

Biologically-inspired methods such as evolutionary algorithms and neural networks are proving useful in the field of information fusion. Artificial Immune Systems (AISs) are a biologically-inspired approach which take inspiration from the biological immune system. Interestingly, recent research has show how AISs which use multi-level information sources as input data can be used to build effective algorithms for real time computer intrusion detection. This research is based on biological information fusion mechanisms used by the human immune system and as such might be of interest to the information fusion community. The aim of this paper is to present a summary of some of the biological information fusion mechanisms seen in the human immune system, and of how these mechanisms have been implemented as AISs

Network Intrusion Detection Systems (NDIS) monitor a network with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS's rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alters, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performances in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DXA, including the antigen multiplier and moving time windows are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with constant-sized detectors is not applicable to the data set, and the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.

Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be expected in such cases that learning algorithms will have to deal with manipulated data aimed at hampering decision making. Although some previous work addressed the handling of malicious data in the context of supervised learning, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution we analyze the performance of a particular method -- online centroid anomaly detection -- in the presence of adversarial noise. Our analysis addresses the following security-related issues: formalization of learning and attack processes, derivation of an optimal attack, analysis of its efficiency and constraints. We derive bounds on the effectiveness of a poisoning attack against centroid anomaly under different conditions: bounded and unbounded percentage of traffic, and bounded false positive rate. Our bounds show that whereas a poisoning attack can be effectively staged in the unconstrained case, it can be made arbitrarily difficult (a strict upper bound on the attacker's gain) if external constraints are properly used. Our experimental evaluation carried out on real HTTP and exploit traces confirms the tightness of our theoretical bounds and practicality of our protection mechanisms.

Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the the fused data signals with a secondary data stream. Aggregate output of a population of cells, is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

Botnets, which consist of thousands of compromised machines, can cause significant threats to other systems by launching Distributed Denial of Service (SSoS) attacks, keylogging, and backdoors. In response to these threats, new effective techniques are needed to detect the presence of botnets. In this paper, we have used an interception technique to monitor Windows Application Programming Interface (API) functions calls made by communication applications and store these calls with their arguments in log files. Our algorithm detects botnets based on monitoring abnormal activity by correlating the changes in log file sizes from different hosts.

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a 'bot' - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the 'botmaster of a botnet'. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single bot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single bot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.