In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FBNSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFBNSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to el...

With the development of information technology, data on the Internet is growing even faster than Moore’s Law. At the age of big data, more and more services are created to deal with big data, which are called dataintensive services. In most cases, multiple data-intensive services are assembled into a service composition to meet complicated requirements. Since the big-data transmission, which is...

This paper initializes a two element concentration vector as a feature vector for classification and spam detection. Negative selection algorithm proposed by the immune system in solving problems in spam detection is used to distinguish spam from non-spam (self from non-self). Self concentration and non-self concentration are generated to form two element concentration vectors. In this approach...

In this paper, a novel negative selection algorithm for recognition problems was given. Compared with the traditional negative selection algorithm, a co-stimulation signal was added to start the detectors, which a key factor in immune response. Co-stimulation signal was calculated by the techniques of the statistics and the sliding window, which not only reduced time complexity of algorithm but...

The increased nature of email spam with the use of urge mailing tools prompt the need for detector generation to counter the menace of unsolocited email. Detector generation inspired by the human immune system implements particle swarm optimization (PSO) to generate detector in negative selection algorithm (NSA). Outlier detectors are unique features generated by local outlier factor (LOF). The...

Artificial Immune System (AIS) is a convoluted and complex arrangement derived from biological immune system (BIS). It possesses the abilities of self-adapting, self-learning and self-configuration. It has the basic function to distinguish self and non-self. Negative Selection Algorithm (NSA) over the years has shown to be competent for anomaly detection problems. In the past decade internet ha...

Aiming to develop an immune based system, the negative selection algorithm aid in solving complex problems in spam detection. This is been achieve by distinguishing spam from non-spam (self from non-self). In this paper, we propose an optimized technique for e-mail classification. This is done by distinguishing the characteristics of self and non-self that is been acquired from trained data set...

A new scheme of detector generation and matching mechanism for negative selection algorithm is introduced featuring detectors with variable properties. While detectors can be variable in different ways using this concept, the paper describes an algorithm when the variable parameter is the size of the detectors in real-valued space. The algorithm is tested using synthetic and realworld datasets,...

Network anomaly detection has become the promising aspect of intrusion detection. The existing anomaly detection models depict the detection profiles with a static way, which lack good adaptability and interoperability. Furthermore, the detection rate is low, so they are difficult to be deployed the realtime detection under the high-speed network environment. In this paper, the excellent mechan...

With the advancement of metagenome data mining science has become focused on microarrays. Microarrays are datasets with a large number of genes that are usually irrelevant to the output class; hence, the process of gene selection or feature selection is essential. So, it follows that you can remove redundant genes and increase the speed and accuracy of classification. After applying the gene se...