TY - GEN
T1 - Unsupervised feature selection method for intrusion detection system
AU - Ambusaidi, Mohammed A.
AU - He, Xiangjian
AU - Nanda, Priyadarsi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/2
Y1 - 2015/12/2
N2 - This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an Extended Laplacian score, EL in short. Specifically, two main phases are involved in EL to complete the selection procedures. In the first phase, the Laplacian score algorithm is applied to select the features that have the best locality preserving power. In the second phase, EL proposes a Redundancy Penalization (RP) technique based on mutual information to eliminate the redundancy among the selected features. This technique is an enhancement over Battiti's MIFS. It does not require a user-defined parameter such as beta to complete the selection processes of the candidate feature set as it is required in MIFS. After tackling the feature selection problem, the final selected subset is then used to build an Intrusion Detection System. The effectiveness and the feasibility of the proposed detection system are evaluated using three well-known intrusion detection datasets: KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results confirm that our feature selection approach performs better than the Laplacian score method in terms of classification accuracy.
AB - This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an Extended Laplacian score, EL in short. Specifically, two main phases are involved in EL to complete the selection procedures. In the first phase, the Laplacian score algorithm is applied to select the features that have the best locality preserving power. In the second phase, EL proposes a Redundancy Penalization (RP) technique based on mutual information to eliminate the redundancy among the selected features. This technique is an enhancement over Battiti's MIFS. It does not require a user-defined parameter such as beta to complete the selection processes of the candidate feature set as it is required in MIFS. After tackling the feature selection problem, the final selected subset is then used to build an Intrusion Detection System. The effectiveness and the feasibility of the proposed detection system are evaluated using three well-known intrusion detection datasets: KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results confirm that our feature selection approach performs better than the Laplacian score method in terms of classification accuracy.
KW - Intrusion detection system
KW - Mutual information
KW - Supervised feature selection
KW - Unsupervised feature selection
UR - http://www.scopus.com/inward/record.url?scp=84967153384&partnerID=8YFLogxK
U2 - 10.1109/Trustcom.2015.387
DO - 10.1109/Trustcom.2015.387
M3 - Conference contribution
AN - SCOPUS:84967153384
T3 - Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
SP - 295
EP - 301
BT - Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
Y2 - 20 August 2015 through 22 August 2015
ER -
