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A wrapper‐based feature selection for improving performance of intrusion detection systems
Author(s) -
Samadi Bonab Maryam,
Ghaffari Ali,
Soleimanian Gharehchopogh Farhad,
Alemi Payam
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4434
Subject(s) - computer science , intrusion detection system , feature selection , data mining , ant colony optimization algorithms , feature (linguistics) , sensitivity (control systems) , selection (genetic algorithm) , key (lock) , the internet , machine learning , artificial intelligence , computer security , linguistics , philosophy , electronic engineering , world wide web , engineering
Summary Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.