The immune system is a remarkable information processing and self learning system that
offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a …

Proposes a novel method, called local non-negative matrix factorization (LNMF), for learning
a spatially localized, parts-based subspace representation of visual patterns. An objective …

In outbreak detection, one of the key issues is the need to deal with the weakness of early
outbreak signals because this causes the detection model to have has less capability in …

Anomaly detection is an important issue, which has been deeply studied in different
research domains and application fields. The dendritic cell algorithm (DCA) is one of the …

This paper presents a supervised methodology that detects malware based on positive
selection. Malware detection is a challenging problem due to the rapid growth of the number …

Abstract Artificial Immune System (AIS) achieved some success in malware detection with its
distributed, diverse and adaptive characteristics. However, in recent years, malware is …

The aim of this paper is to investigate the antigen sampling component of the deterministic
version of the dendritic cell algorithm (dDCA). To achieve this, a model is presented, and …

Run-time malware detection strategies are efficient and robust, which get more and more
attention. In this paper, we use I/O Request Package (IRP) sequences for malware detection …

Existing artificial immune optimization algorithms reflect a number of shortcomings, such as
premature convergence and poor local search ability. This paper proposes a danger‐theory …

Malware stand for Malicious Software became a major threat facing the massive amount of
data transmitted through the internet and the systems holding that data. Malware detection is …