TY - GEN
T1 - A comparative analysis of different soft computing techniques for intrusion detection system
AU - Varghese, Josy Elsa
AU - Muniyal, Balachandra
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this internet era, the data are flooded with malicious activities. The role of soft computing techniques to classify highly vulnerable, complex and uncertain network data by devising an intrusion detection system is so significant. The proposed work emphasizes on the classification of normal and anomaly packets in the networks by carrying out the comparative performance evaluation of different soft computing tools including Genetic Programming (GP), Fuzzy logic, Artificial neural network (ANN) and Probabilistic model with Clustering methods using NSL-KDD dataset. Here, Fuzzy logic runs the first place in the performance metrics and the clustering algorithms and Genetic programming deliver the worst performances. Fuzzy Unordered Rule Induction Algorithm (FURIA) in Fuzzy logic gives a high detection rate of accuracy (99.69%) with the low rate of false alarms (0.31%). The computational time of FURIA (78.14 s) is not so expectant. So Fuzzy Rough Nearest Neighbor(FRNN) is recommended as an optimistic model with a sensible accuracy rate of 99.51% and tolerable false alarm rate of 0.49% along with a pretty good computational time of 0.33 s.
AB - In this internet era, the data are flooded with malicious activities. The role of soft computing techniques to classify highly vulnerable, complex and uncertain network data by devising an intrusion detection system is so significant. The proposed work emphasizes on the classification of normal and anomaly packets in the networks by carrying out the comparative performance evaluation of different soft computing tools including Genetic Programming (GP), Fuzzy logic, Artificial neural network (ANN) and Probabilistic model with Clustering methods using NSL-KDD dataset. Here, Fuzzy logic runs the first place in the performance metrics and the clustering algorithms and Genetic programming deliver the worst performances. Fuzzy Unordered Rule Induction Algorithm (FURIA) in Fuzzy logic gives a high detection rate of accuracy (99.69%) with the low rate of false alarms (0.31%). The computational time of FURIA (78.14 s) is not so expectant. So Fuzzy Rough Nearest Neighbor(FRNN) is recommended as an optimistic model with a sensible accuracy rate of 99.51% and tolerable false alarm rate of 0.49% along with a pretty good computational time of 0.33 s.
UR - https://www.scopus.com/pages/publications/85061162719
UR - https://www.scopus.com/inward/citedby.url?scp=85061162719&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-5826-5_44
DO - 10.1007/978-981-13-5826-5_44
M3 - Conference contribution
AN - SCOPUS:85061162719
SN - 9789811358258
T3 - Communications in Computer and Information Science
SP - 563
EP - 577
BT - Security in Computing and Communications - 6th International Symposium, SSCC 2018, Revised Selected Papers
A2 - Thampi, Sabu M.
A2 - Rawat, Danda B.
A2 - Alcaraz Calero, Jose M.
A2 - Madria, Sanjay
A2 - Wang, Guojun
PB - Springer Verlag
T2 - 6th International Symposium on Security in Computing and Communications, SSCC 2018
Y2 - 19 September 2018 through 22 September 2018
ER -