Title :
A Novel Anomaly Detection Method for Worms
Author :
Xiaojun Tong ; Zhu Wang ; Miao Zhang ; Yang Liu ; Hui Xu
Author_Institution :
Comput. Sci. & Technol., Harbin Inst. of Technol., Weihai, China
Abstract :
This paper proposes a novel anomaly detection method of network worms. The algorithm detects unknown worms by multidimensional worm abnormal detection technology, extracts its feature string via analyzing worm data with leap-style and creates new rules to detect the corresponding worm in case that the unknown worm attacks again. The paper has realized the automatic detection of unknown worms. Experiment data has showed that the method has high success detection rate and low false alarm rate.
Keywords :
data analysis; invasive software; anomaly detection method; false alarm rate; leap-style analysis; multidimensional worm abnormal detection technology; network worms; success detection rate; worm data analysis; Data mining; Databases; Feature extraction; Grippers; Real-time systems; Switches; Topology; Anomaly detection; Automatic detection of worms; Feature extraction; Network worms;
Conference_Titel :
Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2014 13th International Symposium on
Conference_Location :
Xian Ning
Print_ISBN :
978-1-4799-4170-4
DOI :
10.1109/DCABES.2014.55
