FAAD: A Self-Optimizing Algorithm for Anomaly Detection
Adeel Hashmi and Tanvir Ahmad
Department of Computer Engineering, Jamia Millia Islamia, India
Abstract: Anomaly/Outlier detection is the process of
finding abnormal data points in a dataset or data stream. Most of the anomaly
detection algorithms require setting of some parameters which significantly
affect the performance of the algorithm. These parameters are generally set by
hit-and-trial; hence performance is compromised with default or random values.
In this paper, the authors propose a self-optimizing algorithm for anomaly
detection based on firefly meta-heuristic, and named as Firefly Algorithm for
Anomaly Detection (FAAD). The proposed solution is a non-clustering
unsupervised learning approach for anomaly detection. The algorithm is
implemented on Apache Spark for scalability and hence the solution can handle
big data as well. Experiments were conducted on various datasets, and the
results show that the proposed solution is much accurate than the standard
algorithms of anomaly detection.
Keywords: Anomaly detection, outliers, firefly algorithm,
big data, parallel algorithms and apache spark.
Received November 19, 2017; accepted April 28, 2019