A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> A novel cell type has been identified in adherent cell populations prepared from mouse peripheral lymphoid organs (spleen, lymph node, Peyer's patch). Though present in small numbers (0.1–1.6% of the total nucleated cells) the cells have distinct morphological features. The nucleus is large, retractile, contorted in shape, and contains small nucleoli (usually two). The abundant cytoplasm is arranged in processes of varying length and width and contains many large spherical mitochondria. In the living state, the cells undergo characteristic movements, and unlike macrophages, do not appear to engage in active endocytosis. The term, dendritic cell, is proposed for this novel cell type. <s> BIB001 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> Dendritic cells (DCs) are central in the orchestration of the various forms of immunity and tolerance. Their immunoregulatory role mainly relies on the ligation of specific receptors that initiate and modulate DC maturation resulting in the development of functionally different effector DC subsets that selectively promote T helper 1 (TH1)-, TH2- or regulatory T-cell responses. These DC-priming receptors include pattern recognition receptors (PRRs), which discriminate between (groups of) pathogens, as well as receptors that bind tissue factors that are produced either constitutively or in response to infection with pathogens, and characterize the type of tissue and the pathogen-specific response pattern of this tissue. Although it is becoming increasingly clear that the selective development of T-cell-polarizing DC subsets is related to the ligation of particular receptors that are involved in DC maturation, several inconsistencies with this concept remain. <s> BIB002 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> There is a list of unique immune features that are currently absent from the existing artificial immune systems and other intelligent paradigms. We argue that some of AIS features can be inherent in an application itself, and thus this type of application would be a more appropriate substrate in which to develop and integrate the benefits brought by AIS. We claim here that sensor networks are such an application area, in which the ideas from AIS can be readily applied. The objective of this paper is to illustrate how closely a Danger Theory based AIS – in particular the Dendritic Cell Algorithm matches the structure and functional requirements of sensor networks. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and discusses how the DCA can be applied to detect this attack. <s> BIB003 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> The Dendritic Cell algorithm (DCA) is inspired by recent work in innate immunity. In this paper a formal description of the DCA is given. The DCA is described in detail, and its use as an anomaly detector is illustrated within the context of computer security. A port scan detection task is performed to substantiate the influence of signal selection on the behaviour of the algorithm. Experimental results provide a comparison of differing input signal mappings. <s> BIB004 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm. <s> BIB005 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance 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 DCA, including 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 contant-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. <s> BIB006 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Dendritic Cell Based <s> The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested. <s> BIB007
The main role of Dendritic Cells (DC) as antigen presenting cells were identified by Steinman and Cohn BIB001 where DC are comprised of leukocytes which are present in all tissues. They are endowed with a disparate hematopoietic lineage and function in various tissues. Inside various tissues, DC segregate and mature when triggered appropriately; later they relocate to secondary lymphoid tissues where they present antigen to T-cells in order to induce an immune response. The immature DC occupy body surfaces and are commonly present in an immature state and are unable to stimulate T-cells. Once the foreign pathogens are processed and obtained by the immature DC they migrate to the thymus and the spleen where the immature DC mature and stimulate an immune response. As explained in BIB002  inflection between the various states of DC is enabled by the recognition of signals including pathogen associated molecular patterns (PAMP), danger signals, apoptotic signals (safe signals) and inflammatory cytokines. These signals are explained as follows: • PAMPs transform immature DC to mature DC • Danger signals are given out when a tissue cell is damaged; their strength is lower than PAMPs. • Safe signals are given out when regulated cell death occurs • Inflammatory cytokines are given out when general tissue distress occurs and amplify the effect of the other three signals. The response of the T-cell is determined by the corresponding concentrations of these four types of signals. Semi-mature DCs have a suppressive effect while mature DC have an accentuating effect. The first Dendritic Cell Algorithm (DCA) was presented by Greensmith et al. BIB005 and it involved combining various signals to investigate the current circumstance of the environment and non-parallel sampling of another data stream (antigen). A fuzzy margin derives in accordance with the concentration of co-stimulatory molecules is an indicator for a DC to stop antigen collection and migrate to a virtual lymph node. The DCA works on the input signals with presumed weights to produce output signals. A value of 1 is assigned if the cumulative mature signal is greater than the cumulative semi-mature signal and vice versa. The mature context presentation of that antigen is calculated relative to the total number of antigens. The DC is designed as a Libitissue BIB004 tissue server. There are three stages in this algorithm: initialization, update and aggregation. Initialization deals with setting initial values and the update stage is sub-divided into tissue update and the cell cycle. The Libitissue tissue server comprises the tissue update and cell cycle. Data from the source is given to tissue server through the tissue client. The appearance of new data in the system leads to the provision of input signals for the population of DCs. The cell cycle is a distinct process that occurs at a user defined rate. When all of the antigen data is processed the cell cycle and tissue update process stops. In the final stage aggregation of the collected antigens occurs together with analysis and the Mature Context Antigen Value (MCAV) per antigen is derived. Gu et al. BIB006 used DCA on the KDD 99  data set after two additional functions were added to the system for optimization: antigen multiplier and a moving time window. The antigen multiplier makes several copies of the antigen, to overcome the problem of "antigen deficiency" that can be given to DCs. In each iteration, new signals are calculated using the moving time window. Based on the results the antigen multiplier and moving time window have equal effect on the DCA using the KD 99 data set. Oates et al. BIB007 devised a DCA approach for a robot classification problem. Robotic DCA is designed as a stand-alone physiological module for compatibility with comprisal design. The Advanced Robot Interface for Applications (Aria) library's [52] "wander" design is extended with two extra modules: image processing and DCA execution. MCAV coefficients are output by the DCA module approximately once per second. PAMP, safe and danger signals are used as input to the DCA. PAMP originates from the image processing module and the safe signal originates from the Laser Range Finder (LRF). A sonar array having a 360 degree field of view (FOV) is the source of danger signal and the antigen is an integer number which can be uniquely identified by the segment of the test pen. The DCA approach used helped the robot to steer away from obstacles in its path. The authentic DCA is highly speculative and the Deterministic Dendritic Cell Algorithm (dDCA) BIB005 attempts to overcome this by using two sets of input signals as well as antigens. The DC is subjected to identical input signals. Here an array is used in order to store the antigen value and count of times the DCs have collected the antigen. There are three parameters in the dDCA-weight scheme for processing signals, outputting DC values and the number of DCs. The work in BIB003 depicts the affinity of DCA towards the architecture and operational requirements of sensor networks. Based on this variation, ubiquitous DCA (UDCA) was proposed to detect attacks on sensor networks and its features include: • Signals from multiple data sources are collected by DC. New output cytokines are accumulated at the maturation stage of each DC. • Linking of antigens with context information is done by UDCA. • Extent of node misbehaviour is detected by UDCA via signals generated.