A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Networks <s> Abstract In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to «forget» little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan's ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not require effort to optimize any system parameters. <s> BIB001 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Networks <s> This work presents omni-aiNet, an immune-inspired algorithm developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. The search engine is capable of automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem. This proposal unites the concepts of omni-optimization, already proposed in the literature, with distinctive procedures associated with immune-inspired concepts. Due to the immune inspiration, the omni-aiNet presents a population capable of adjusting its size during the execution of the algorithm, according to a predefined suppression threshold, and a new grid mechanism to control the spread of solutions in the objective space. The omni-aiNet was applied to several optimization problems and the obtained results are presented and analyzed. <s> BIB002 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Networks <s> In this paper we present an Abstract Immune System Algorithm, based on the model introduced by Farmer et al, inspired on the theory of Clonal Selection and Idiotypic Network due to Niels Jerne. The proposed algorithm can be used in order to solve problems much in the way that Evolutionary Algorithms or Artificial Neural Networks do. Besides presenting the Algorithm itself, we briefly discuss its various parameters, how to encode input data and how to extract the output data from its outcome. The reader can do their own experiments using the workbench found in the address http://ctp.di.fct.unl.pt/˜jddp/immune/. <s> BIB003 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Networks <s> AiNet is an immune-inspired algorithm for data compression, i.e. the reduction of redundancy in data sets. In this paper we investigate the compression quality of aiNet. Therefore, a similarity measure between input set and reduced output set is presented which is based on the Parzen window estimation and the Kullback-Leibler divergence. Four different artificially generated data sets are created and the compression quality is investigated. Experiments reveal that aiNet produced reasonable results on an uniformly distributed data set, but poor results on non-uniformly distributed data sets, i.e. data sets which contain dense point regions. This effect is caused by the optimization criterion of aiNet <s> BIB004 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Artificial Immune Networks <s> Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Given that biclustering requires the optimization of two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, will be proposed in this paper. To illustrate the capabilities of this novel algorithm, MOM-aiNet was applied to the extraction of biclusters from two datasets, one taken from a well-known gene expression problem and the other from a collaborative filtering application. A comparative analysis has also been accomplished, with the obtained results being confronted with the ones produced by two popular biclustering algorithms from the literature (FLOC and CC) and also by another immune-inspired approach for biclustering (BIC-aiNet). <s> BIB005
Jerne  suggested that the immune system is capable of attaining immunological memory due to the presence of B-cells. These B-cells prompt each other as well as restrain connected cells to control over production of B-cells. This is needed to maintain a stable memory. Hunt and Cooke BIB001 proposed a system comprising a bone marrow object, a network of B-cell objects and antigen population. Bone marrow objects randomly initialise the B-cell population. The antigen population that is present in the system will be randomly picked and inserted to a point in the B-cell network. Cloning of B-cell objects occur if they are able to bind the antigen population. Pacheo et al. BIB003 designed an Abstract Immune System Algorithm. There are four strategies necessary for the effectiveness of this model: 1) the affinity between the epitope of an antibody or prototype of an antibody; 2) the restraining of an antibody during epitope recognition; 3) the affinity between antigen and antibody; and 4) the nature of cells to die in the absence of communication. A given antibody type will be prompted or deleted by referring to the recruitment threshold or death threshold. Omni-aiNet BIB002 is used to solve singular and multi-objective problems. The advantages identified were: 1) a new grid mechanism to control the spread of a solution in the objective space; 2) adjusting the size of the search space based on a predefined suppression threshold; and 3) axiomatically adapting the investigation of the search space. Taking advantage of the multi-population property of aiNet, the Multi-objective Multi population Artificial Immune Network (MOM-aiNet) for bi clustering was designed BIB005 . The advantage of MOM-aiNet is that several sets of non-subjugated solutions are returned in contradiction to a single set of non-subjugated solutions. The subjugation is used to compare the quality of solutions for a given issue, thereby enabling it to measure the solution set given by MOM-aiNet. Out of the data set one row and one column is randomly chosen so that MOM-aiNet produces y subpopulation of one bi cluster. In the algorithm for each subpopulation y clones subject to the mutation process will be produced. Three steps are involved in mutation which would be randomly chosen with equal probability: 1) delete a row of the column; 2) incorporate a row; and 3) incorporate a column. Whenever the number of non-subjugated elements becomes greater than y clones a distance based restraining process occurs so that a small and locally diverse sub-population is maintained. Stibor et al. explored the compression quality of aiNet BIB004 . Using the Parzen window estimation and Kullback-Leibler divergence a similarity measure between the data set (input) and aiNet dataset (output) was presented. A Parzen window estimator helps find the probability densities of the input and output datasets.