An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks

Artificial immune systems applications in cancer research

Artificial Immune System (AIS) is a branch of computational intelligence that has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational problems. This work focuses on the application of AIS techniques to cancer research and specifically for prediction of the recurrence of cancer in patients. The objective is to test AIS models and algorithms for cancer research by validation against actual cancer datasets. © 2011 IEEE

Real valued negative selection for anomaly detection in wireless ad hoc networks

Wireless ad hoc network is one of the network technologies that have gained lots of attention from computer scientists for the future telecommunication applications. However it has inherits the major vulnerabilities from its ancestor (i.e., the fixed wired networks) but cannot inherit all the conventional intrusion detection capabilities due to its features and characteristics. Wireless ad hoc network has the potential to become the de facto standard for future wireless networking because of its open medium and dynamic features. Non-infrastructure network such as wireless ad hoc networks are expected to become an important part of 4G architecture in the future. In this paper, we study the use of an Artificial Immune System (AIS) as anomaly detector in a wireless ad hoc network. The main goal of our research is to build a system that can learn and detect new and unknown attacks. To achieve our goal, we studied how the real-valued negative selection algorithm can be applied in wireless ad hoc network network and finally we proposed the enhancements to real-valued negative selection algorithm for anomaly detection in wireless ad hoc network

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

The machine abnormal degree detection method based on SVDD and negative selection mechanism

As is well-known, fault samples are essential for the fault diagnosis and anomaly detection, but in most cases, it is difficult to obtain them. The negative selection mechanism of immune system, which can distinguish almost all nonself cells or molecules with only the self cells, gives us an inspiration to solve the problem of anomaly detection with only the normal samples. In this paper, we introduced the Support Vector Data Description (SVDD) and negative selection mechanism to separate the state space of machines into self, non-self and fault space. To estimate the abnormal level of machines, a function that could calculate the abnormal degree was constructed and its sensitivity change according to the change of abnormal degree was also discussed. At last, Iris-Fisher and ball bearing fault data set were used to verify the effectiveness of this method

Novelty detection across a small population of real structures: A negative selection approach

Vibration-based Structural Health Monitoring (SHM), exploits a variety of approaches for novelty detection. In particular, many data-based methods try to recognise patterns by exploiting analogies with the human body's natural defences at a cellular level. These algorithms fall within the Artificial Immune System (AIS) class and can be chosen, according to their peculiarities, to solve specific problems in diverse application areas. This study investigates the damage-detection process in different operational conditions, obtained by applying structural modifications to a laboratory-scale aeroplane, which follows the geometric features of the GARTEUR benchmark project. Damage identification is performed by exploiting the Negative Selection Algorithm (NSA), already applied by some of the authors on numerically-simulated case studies, and chosen for its capability of self/non-self discrimination under varying operational or environmental conditions. The research is expanded by using sparse autoencoders for feature dimensionality reduction. The method is applied to an experimental dataset acquired via Scanning Laser Doppler Vibrometer (SLDV) measurements, to identify consistent damage-sensitive features from the frequency response functions, and to obtain a reliable fault-detection performance

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

An intelligent fault diagnosis method using variable weight artificial immune recognizers (V-AIR)

The Artificial Immune Recognition System (AIRS), which has been proved to be a successful classification method in the field of Artificial Immune Systems, has been used in many classification problems and gained good classification effect. However, the network inhibition mechanisms used in these methods are based on the threshold inhibition and the cells with low affinity will be deleted directly from the network, which will misrepresent the key features of the data set for not considering the density information within the data. In this paper, we utilize the concept of data potential field and propose a new weight optimizing network inhibition algorithm called variable weight artificial immune recognizer (V-AIR) where we replace the network inhibiting mechanism based on affinity with the inhibiting mechanism based on weight optimizing. The concept of data potential field was also used to describe the data distribution around training samples and the pattern of a training data belongs to the class with the largest potential field. At last, we used this algorithm to rolling bearing analog fault diagnosis and reciprocating compressor valves fault diagnosis, which get a good classification effect