Paper Info
Title
A Few-Shot Deep Learning Approach For Improved Intrusion Detection
Abstract
Our generation has seen the boom and ubiquitous advent of Internet connectivity. Adversaries have been exploiting this omnipresent connectivity as an opportunity to launch cyber attacks. As a consequence, researchers around the globe devoted a big attention to data mining and machine learning with emphasis on improving the accuracy of intrusion detection system (IDS). In this paper, we present a few-shot deep learning approach for improved intrusion detection. We first trained a deep convolutional neural network (CNN) for intrusion detection. We then extracted outputs from different layers in the deep CNN and implemented a linear support vector machine (SVM) and 1-nearest neighbor (1-NN) classifier for few-shot intrusion detection. few-shot learning is a recently developed strategy to handle situation where training samples for a certain class are limited. We applied our proposed method to the two well-known datasets simulating intrusion in a military network: KDD 99 and NSL-KDD. These datasets are imbalanced, and some classes have much less training samples than others. Experimental results show that the proposed method achieved better performances than the state-of-the-art on those two datasets.
Year
Venue
Keywords
2017
2017 IEEE 8TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (UEMCON)
Intrusion Detection System(IDS), low shot learning, CNN, SVM
Field
DocType
Citations 
Anomaly detection,Computer science,Convolutional neural network,Support vector machine,Feature extraction,Human–computer interaction,Artificial intelligence,Deep learning,Classifier (linguistics),Artificial neural network,Intrusion detection system,Machine learning
Conference
2
PageRank 
References 
Authors
0.39
6
6
Name
Order
Citations
PageRank
Md Moin Uddin Chowdhury120.39
Frederick Hammond220.39
Glenn Konowicz320.39
ChunSheng Xin446439.25
Hongyi Wu547352.02
jiang li6239.88