A Review of Anomaly based Intrusion Detection Systems <s> Detection Models Based on Computer Immunology <s> This paper describes an artificial immune system (AIS) approach to producing robust schedules for a dynamic job-shop scheduling problem in which jobs arrive continually, and the environment is subject to change due to practical reasons. We investigate whether an AIS can be evolved using a genetic algorithm (GA), and then used to produce sets of schedules which together cover a range of contingencies, both foreseeable and unforeseeable. We compare the quality of the schedules to those produced using a genetic algorithm specifically designed for tackling job-shop scheduling problems, and find that the schedules produced from the evolved AIS compare favourably to those produced by the GA. Furthermore, we find that the AIS schedules are robust in that there are large similarities between each schedule in the set, indicating that a switch from one schedule to another could be performed with minimal disruption if rescheduling is required. <s> BIB001 </s> A Review of Anomaly based Intrusion Detection Systems <s> Detection Models Based on Computer Immunology <s> This paper describes an application of one-class support vector machine (SVM) novelty detection for detecting seizures in humans. Our technique maps intracranial electroencephalogram (EEG) time series into corresponding novelty sequences by classifying short-time, energy-based statistics computed from one-second windows of data. We train a classifier on epochs of interictal (normal) EEG. During ictal (seizure) epochs of EEG, seizure activity induces distributional changes in feature space that increase the empirical outlier fraction. A hypothesis test determines when the parameter change differs significantly from its nominal value, signaling a seizure detection event. Outputs are gated in a .one-shot. manner using persistence to reduce the false alarm rate of the system. The detector was validated using leave-one-out cross-validation (LOO-CV) on a sample of 41 interictal and 29 ictal epochs, and achieved 97.1% sensitivity, a mean detection latency of -7.58 seconds, and an asymptotic false positive rate (FPR) of 1.56 false positives per hour (Fp/hr). These results are better than those obtained from a novelty detection technique based on Mahalanobis distance outlier detection, and comparable to the performance of a supervised learning technique used in experimental implantable devices (Echauz et al., 2001). The novelty detection paradigm overcomes three significant limitations of competing methods: the need to collect seizure data, precisely mark seizure onset and offset times, and perform patient-specific parameter tuning for detector training. <s> BIB002
Inspired by the ideas taken from the concept of immunology, the computational technique, Artificial Immune System (AIS) is developed in developing adaptive systems that are capable 30 of solving problems from various domains. This has now become a tool for research which studied well and applied in solving problems in the computer security field and particularly to detect the viruses  in computers and also network intruders  . It is also applied to give a solution to the scheduling problem BIB001 , to build support systems in taking decisions or to solve function optimization and combinatorial optimization problems BIB002 . Applying immunology to the basic computational model is the subject of research; the widely applied basic notions are antigen and antibody. The invaders which are foreign to the system and attack it in some way are antigens. A part of system, and which help in detecting and eliminating the antigens are called antibodies. The detection of antigens is done by matching them. The number of antigens is much higher than the number of antibodies in the system, so a perfect matching can never occur. To keep a less number of antibodies, that reliably detect large number of antigens that were never seen before, AIS-based system is used.