Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human signals from the host tissue and correlate these signals with proteins know as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

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.

The role of T-cells within the immune system is to confirm and assess anomalous situations and then either respond to or tolerate the source of the effect. To illustrate how these mechanisms can be harnessed to solve real-world problems, we present the blueprint of a T-cell inspired algorithm for computer security worm detection. We show how the three central T-cell processes, namely T-cell maturation, differentiation and proliferation, naturally map into this domain and further illustrate how such an algorithm fits into a complete immune inspired computer security system and framework.

Internet and expert systems have offered new ways of sharing and distributing knowledge, but there is a lack of researches in the area of web based expert systems. This paper introduces a development of a web-based expert system for the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. It is the first time to develop such system (application of civil service regulations) as well the development of it using web based approach. The proposed system considers 17 regulations of the civil service system. The different phases of developing the RCSES system are presented, as knowledge acquiring and selection, ontology and knowledge representations using XML format. XML Rule-based knowledge sources and the inference mechanisms were implemented using ASP.net technique. An interactive tool for entering the ontology and knowledge base, and the inferencing was built. It gives the ability to use, modify, update, and extend the existing knowledge base in an easy way. The knowledge was validated by experts in the domain of civil service regulations, and the proposed RCSES was tested, verified, and validated by different technical users and the developers staff. The RCSES system is compared with other related web based expert systems, that comparison proved the goodness, usability, and high performance of RCSES.

A client-server architecture to simultaneously solve multiple learning tasks from distributed datasets is described. In such architecture, each client is associated with an individual learning task and the associated dataset of examples. The goal of the architecture is to perform information fusion from multiple datasets while preserving privacy of individual data. The role of the server is to collect data in real-time from the clients and codify the information in a common database. The information coded in this database can be used by all the clients to solve their individual learning task, so that each client can exploit the informative content of all the datasets without actually having access to private data of others. The proposed algorithmic framework, based on regularization theory and kernel methods, uses a suitable class of mixed effect kernels. The new method is illustrated through a simulated music recommendation system.

The RADAR project developed a large multi-agent system with a mixed-initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user's workflow without frustrating the user when the system's suggestions were either unhelpful or simply incorrect. A large evaluation of RADAR demonstrated that novice users confronted with an email overload test performed significantly better, achieving a 37% better overall score when assisted by RADAR.

Although AI and HCI explore computing and intelligent behavior and the fields have seen some cross-over, until recently there was not very much. This article outlines a history of the fields that identifies some of the forces that kept the fields at arm’s length. AI was generally marked by a very ambitious, long-term vision requiring expensive systems, although the term was rarely envisioned as being as long as it proved to be, whereas HCI focused more on innovation and improvement of widely-used hardware within a short time-scale. These differences led to different priorities, methods, and assessment approaches.  A consequence was competition for resources, with HCI flourishing in AI winters and moving more slowly when AI was in favor. The situation today is much more promising, in part because of platform convergence: AI can be exploited on widely-used systems.

This is an opinion piece about the relationship between the fields of human-computer interaction (HCI), and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier to use for people. But historically, they have disagreed about whether "intelligence" or "direct manipulation" is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces are merely cosmetic. This disagreement is counterproductive.This article argues that AI's goals of intelligent interfaces would benefit enormously by the user-centered design and testing principles of HCI. It argues that HCI's stated goals of meeting the needs of users and interacting in natural ways, would be best served by application of AI. Peace.

These unintended negative consequences of the introduction of intelligence often have no direct relationship with the intended benefits, just as the adverse effects of a medication may bear no obvious relationship to the intended benefits of taking that medicine. Therefore, these negative consequences can be seen as side effects. The purpose of this article is to give designers, developers, and users of interactive intelligent systems a detailed awareness of the potential side effects of AI. As with medications, awareness of the side effects can have different implications: We may be relieved to see that a given side effect is unlikely to occur in our particular case. We may become convinced that it will inevitably occur and therefore decide not to "take the medicine" (that is, decide to stick with mainstream systems). Or most likely and most constructively, by looking carefully at the causes of the side effects and the conditions under which they can occur, we can figure out how to exploit the benefits of AI in interactive systems while avoiding the side effects.

The development of intelligent environments is considered an important step toward the realization of the ambient intelligence vision. Greece, served as program chairs. The previous four editions of the IE conference have been held at the University of Essex, UK (in 2005), at the National Technical University of Athens, Greece (in 2006), at the University of Ulm, Germany (in 2007), and at the University of Washington campus in Seattle, Washington, USA (in 2008). The development of intelligent environments is About 120 delegates attended the workshops considered the first and primary step toward the and the conference. These included representatives realization of the ambient intelligence vision.