TY - JOUR
T1 - Intelligent Intrusion Detection System Through Combined and Optimized Machine Learning
AU - Shah, Syed Ali Raza
AU - Issac, Biju
AU - Jacob, Seibu Mary
PY - 2018/6/28
Y1 - 2018/6/28
N2 - In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10Gbps worked quite well.
AB - In this paper, an existing rule-based intrusion detection system (IDS) is made more intelligent through the application of machine learning. Snort was chosen as it is an open source software and though it was performing well, it showed false positives (FPs). To find the best performing machine learning algorithms (MLAs) to use with Snort so as to improve its detection, we tested some algorithms on three available datasets. Support vector machine (SVM) was chosen along with fuzzy logic and decision tree based on their accuracy. Combined versions of algorithms through ensemble SVM along with other variants were tried on the generated traffic of normal and malicious packets at 10Gbps. Optimized versions of the SVM along with firefly and ant colony optimization (ACO) were also tried, and the accuracy improved remarkably. Thus, the application of combined and optimized MLAs to Snort at 10Gbps worked quite well.
UR - https://www.worldscientific.com/doi/abs/10.1142/S1469026818500074
UR - http://www.mendeley.com/research/intelligent-intrusion-detection-system-through-combined-optimized-machine-learning
U2 - 10.1142/S1469026818500074
DO - 10.1142/S1469026818500074
M3 - Article
AN - SCOPUS:85048697403
SN - 1469-0268
VL - 17
JO - International Journal of Computational Intelligence and Applications
JF - International Journal of Computational Intelligence and Applications
IS - 2
M1 - 1850007
ER -