  Cyber attacks pose crucial threats to computer system security, and put
digital treasuries at excessive risks. This leads to an urgent call for an
effective intrusion detection system that can identify the intrusion attacks
with high accuracy. It is challenging to classify the intrusion events due to
the wide variety of attacks. Furthermore, in a normal network environment, a
majority of the connections are initiated by benign behaviors. The class
imbalance issue in intrusion detection forces the classifier to be biased
toward the majority/benign class, thus leave many attack incidents undetected.
Spurred by the success of deep neural networks in computer vision and natural
language processing, in this paper, we design a new system named DeepIDEA that
takes full advantage of deep learning to enable intrusion detection and
classification. To achieve high detection accuracy on imbalanced data, we
design a novel attack-sharing loss function that can effectively move the
decision boundary towards the attack classes and eliminates the bias towards
the majority/benign class. By using this loss function, DeepIDEA respects the
fact that the intrusion mis-classification should receive higher penalty than
the attack mis-classification. Extensive experimental results on three
benchmark datasets demonstrate the high detection accuracy of DeepIDEA. In
particular, compared with eight state-of-the-art approaches, DeepIDEA always
provides the best class-balanced accuracy.
