Biologically Motivated Approaches for Complex Problem Solving

Intrusion detection systems play increasingly essential roles in modern society. With all various approaches used to Anomaly Detection, we investigate Artificial Immune Systems (AIS) to model anomaly detection. One new field of AIS is called Danger Theory. Since traditional Anomaly Detection Methods produce a large number of false alarms, AIS methods based on Danger Theory are suitable options for intrusion detection that can handle the above problem. So in this paper, we propose a novel method in danger theory field for anomaly detection and decrease the amount of system false alarms by using danger signals which are produced through a KNN classifier. Our experimental results show that this proposed method has low false alarm, by using the danger signals. Use of this danger signal that created by danger theory can improved our Artificial Immune Network and help us to achieve low false alarm in our results.

A visual modelling approach and its computational technique were proposed to represent and simulate a kind of immune system, which is comprised of immune cells and immune molecules etc. To study natural immune system and artificial immune system according to information theories and computational methodologies, the hierarchical model of the immune system was proposed, more faithful and suitable for visual simulation than traditional models. The hierarchical immune system basically consisted of innate immune tier, adaptive immune tier and immune cell tier. Thus, the tri-tier model of the immune system was seamless and coherent with the architecture of the artificial immune system, so that the research on the natural immune system and the research on the artificial one could improve and synchronize each other. Though the structure and features of the natural immune system were difficult to measure and test, the tri-tier architecture and qualitative features of the artificial immune system were built, changed and verified. To validate the new approach to visualize and explore the natural immune system, many experiments were tested on the tri-tier artificial immune system. At last, the visual results of the simulations show that the visual modelling approach can provide an effective and better way of understanding the natural immune system.

In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper we investigate the use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR. The system is inspired by the natural immune system of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. We describe the first step of our design; it employs negative selection, an algorithm used by the natural immune system. We define how we map the natural immune system concepts such as self, antigen and antibody to a mobile ad-hoc network, and give the resulting algorithm for misbehavior detection. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters impact the results. Further steps will extend the design by using an analogy to the innate system, danger signals, costimulation and memory cells.

This paper presents a study of the flow of ice in wedge-shaped converging channels. Such flows are encountered in the relatively constricted waters of the Canadian Arctic Archipelago. Ridging, lead opening patterns, development of a highpressure area, and arch formation are some of the processes which take place during ice flow through converging channels. An idealized geometry and steady wind forcing were used in the testing. The results give ice cover velocity, distribution of stresses, ice thickness, area coverage and ridging. Some of the conditions leading to arch formation at the constricted exit of the channel are explored.

The current RFID systems are fragile to external attacks, due to the limitations of encryption authentication and physical protection methods used in implementation of RFID security systems. In this paper, we propose a collaborative RFID intrusion detection method that is based on an artificial immune system (AIS). The new method can enhance the security of RFID systems without need to amend the existing technical standards of RFID. Mimicking the immune cell collaboration in biological immune systems, RFID operations are defined as self and nonself antigens, representing legal and illegal RFID operations, respectively. Data models are defined for antigens' epitopes. Known RFID attacks are defined as danger signals represented by nonself antigens. We propose a method to collect RFID data for antigens and danger signals. With the antigen and danger signal data available, we use a negative selection algorithm to generate adaptive detectors for self antigens as RFID legal operations. We use an immune based clustering algorithm aiNet to generate collaborative detectors for danger signals of RFID intrusions. Simulation results have shown that the new RFID intrusion detection method has effectively reduced the false detection rate. The detection rate on known types of attacks was 98% and the detection rate on unknown type of attacks was 93%.

The adaptive definition of danger signal is the most important problem to be solved in application of danger theory in Artificial Immune Systems. Starting from the idea of change lead to danger, it use numerical differential in math to describe the law of resource occupation. Utilize partial differential to establish the formal method to describe the relationships between resource indicators. And given the explanation of its meaning and implementation, provided a new idea to improve the intelligence and self-adaptability of danger perception.

Care about automated documents classification has increased since the appearance of the digital documents and the wide diffusion of Internet. In the 1990's, the computer performance has greatly improved and has led to the methods of machine learning to establish automated classifiers. These methods have achieved good speed and classification's accuracy and researchers still investigate in this field to accomplish more accuracy and less time. Artificial immunologic systems have shown high performance in such as data clustering and anomaly detection which can be ascribed to the nature of the immunologic system in protecting the body. Some of the present methods and ways used in the training process of the document classification are time consuming and others have less accuracy rate concerned the classification of the related document as software engineering document classes For these reasons, this research deals with the study of Natural Immune System and using the dynamic process of the Innate Immune System work based on danger theory and Dendritic Cell (DC) technique in building Dendritic Cell Model (DCM) to classify Software Engineering documents as they comprise information related to developing the software systems, that makes it easy for the software engineer who works in maintenance. DCM has high classification's speed and accuracy besides easy and flexible use by designing interfaces that makes it easy for the user to deal with the system. In order to improve the quality and the efficiency of DCM method, it was compared to one of the best and well-known methods of classification referred to as, Naive Bayes(NB). After conducting several experiments on a various group of software engineering documents, evaluations results have shown that the accuracy of the innate immunologic method (DCM) has reached (DCM) (95%), whereas Naïve classification method has reached (90 %) with training and classification speed that doesn’t exceed one minutes. This shows the feasibility of using the algorithms of AIS systems in the field of information recovery and documents classification. This system was built and programmed in Java language and was implemented under an operating system environment Microsoft Windows7.

The conventional role of the immune system has been viewed as the first line of defence discriminating self from nonself. However, in the past decade, the innate immune response has proven to be more advanced sensing signals of danger, such as pathogen‐specific molecules ( PAMPs , pathogen‐associated molecular patterns) or endogenous host‐derived signals released during cellular damage, while remaining unresponsive to normal host molecules, dietary antigens or commensal bacteria. The host response to invading microbial pathogens relies on both the innate and adaptive immune response. The danger theory of Matzinger (1994) proposed a similar model for the adaptive immune response in discriminating self from nonself. This theory proposed that the immune system does not react to foreign substances but instead responds to situations that are potentially harmful. These danger signals, or damage‐associated molecular patterns ( DAMPs ), released by damaged cells, can provide for a second signal which is necessary to activate the immune response while avoiding collateral damage in situations in which harmless nonself is present. Key Concepts According to the danger theory of Matzinger, the immune system does not merely distinguish whether an entity is foreign or not, it mainly determines whether it will cause damage to the body. Damage‐associated molecular patterns (DAMP) are danger signals released during inflammatory stress such as burns, trauma and infection. Each pathogen is recognised by its specific molecular signature or pathogen‐associated molecular pattern (PAMP). Activation of the innate immune system relies on both pathogen‐specific molecules and endogenous danger signals. DAMPs can be recognised by pattern‐recognition receptors on antigen‐presenting cells or by distinct receptors such as the receptor for advanced glycation end products (RAGE). Key DAMPs that are recognised by Toll‐like receptors (TLRs) include heat shock proteins (HSPs), high‐mobility group box 1 (HMGB)‐1 and the S100 proteins.

Effective distribution outage cause identification can help expedite the restoration procedure and improve the system availability. The fuzzy classification E-algorithm and the immune system inspired classification algorithm, artificial immune recognition system (AIRS), have demonstrated good capabilities in outage cause identification, especially with the existence of imbalanced data. E-algorithm extracts inference rules but is computational demanding; AIRS has the quick searching capability but is lack of rule extraction capability. In this paper, fuzzy artificial immune recognition system (FAIRS) has been proposed to take advantage of the strengths of E-algorithm and AIRS. FAIRS is applied to Duke Energy outage data for cause identification using three major customer interruption causes (tree, animal, and lightning) as prototypes; and FAIRS achieves comparable fault diagnosis performance with two base algorithms while being able to extract linguistic rules to explain the inference within significantly reduced computing time than E-algorithm.

This paper presents a novel approach to an artificial immune system, which uses the rough set theory to improve its classification ability under uncertainty in data. The proposed approach is mainly based on a negative selection algorithm and is suitable to solve problems where the knowledge of non-self is scarce and noisy. The rough set theory is used to deal with uncertainty in data and to obtain rule sets necessary to specify both self and non-self classes. The proposed artificial immune system is implemented with rough valued lymphocytes, which emulate the approximate binding performed by natural immune systems.

The existing detection methods of Android mobile malware mainly include signature scanning, heuristic method and behavior monitoring method. These traditional detection methods have a common limitation: they are not adaptive. The detection methods based on artificial immune system, such as dendritic cell algorithm, have some self-adaptability, but they depend too much on artificial experience, and the self-adaptability is obviously insufficient. Therefore, in order to overcome the lack of self-adaptability of existing detection methods, this paper introduces a change perception method based on danger theory to detect malicious software by looking for change in Android mobile phone system, that is, danger signal. When studying the generation of dangerous signal, this paper uses the method of describing the law of function change in mathematics to describe the change in smartphone system with the concept of differential, and then defines and expresses dangerous signal. Considering the discrete type of data in Android mobile phone system, this paper realizes the expression of dangerous signal based on the theory of numerical differentiation, and puts forward the method of calculating dangerous signal in Android system.

Strategies to Modulate Immune Responses: A New Frontier for Gene Therapy

The artificial immune system (AIS) is an emerging research field of computational intelligence that is inspired by the principle of biological immune systems. With the adaptive learning ability and a self-organization and robustness nature, the immunology based AIS algorithms have successfully been applied to solve many engineering problems in recent years, such as computer network security analysis, fault detection, and data mining. The real-valued negative selection algorithm (RNSA) is a computational model of the self/non-self discrimination process performed by the T-cells in natural immune systems. In this research, three different real-valued negative selection algorithms (i.e., the detectors with fixed radius, the V-detector with variable radius, and the proliferating detectors) are studied and their applications in data classification and bioinformatics are investigated. A comprehensive study on various parameters that are related with the performance of RNSA, such as the dimensionality of input vectors, the estimation of detector coverage, and most importantly the selection of an appropriate distance metric, is conducted and the figure of merit (FOM) of each algorithm is evaluated using real-world datasets. As a comparison, a model based on artificial neural network is also included to further demonstrate the effectiveness and advantages of RNSA for specific applications.

A new method of intrusion detection based on the danger theory and the cloud model is presented in this paper. The main idea of danger signal generation mechanism of this method is stated as follows. Antigen apoptosis and necrosis will affect antibody concentrations. This paper has defined the concentration variability functions concerned and divided the risk levels. Changes of antibody concentrations in the immune system are determined by the cloud model, and then danger signals will be sent according to the changes. This method has successfully solved the problems of high false positive rate and high false negative rate. The theoretical analysis and experimental results show that the method is effective to intrusion detection with advantages of diversity, real-time and adaptability.

On-line optimization of power boilers is a very important and challenging issue in research and implementation, particularly in the context of increasing environmental requirements. Combustion process is a complex MIMO process (Multi-Input-Multi-Output). A large inertia and non-linearity of the combustion combined with frequent changes of disturbance signals like boiler load and fuel quality necessitated the searching for new solutions in the field of control and optimization. There are plenty of algorithms used for combustion optimization, ranging from MPC through neural network etc. Most of the algorithms ae inspired by phenomenon that could be observed in the nature. The newest algorithm, that has been successfully applied in combustion optimization project is artificial immune system. The artificial immune system like real immune system has ability of adaptation. Once a body is infected by a known pathogen (bacteria or virus) the immune response – antibody production is much faster and the illness is less painful to the body, compering to infection by a new pathogen. Artificial immune system is a self-learning algorithm – it searches and remembers most effective solutions for specific process conditions. The advantage of IT system with artificial immune algorithm is ability of fast adaptation to continuously changing conditions with multi-criteria optimization. The artificial immune algorithm has been applied combustion optimization projects in miscellaneous hard coal, lignite, gas and oil fired power boilers with capacity ranging from 300 t/h to 2300 t/h. This paper presets example combustion optimization results.