• TEJINDERSINGH RAMGADIYA
• PRAJWAL GAIKWAD
• REVATI SHELKE
• VIVEK SHELAKE
• PRACHI SHINDE, SHRAVANI JADHAV

System security is a crucial demand now-a-days for huge organizations. The Intrusion Detection frameworks (IDS) are going to be valuable thing for reassurance against attacks that are frequently changing in size and complexity. With information’s privacy and accessibility, it must be solid, simple to oversee and with low maintenance cost. Different adjustments are being connected to IDS consistently to recognize new attacks and handle them. This paper proposes a Fuzzy Genetic Algorithm (FGA) for finding the intruders. The FGA framework is a fluffy classifier, whose information base is demonstrated as a fluffy administer, for example, "if-then" and enhanced by a hereditary calculation. The system is tried on the standard KDD'99 dataset. The outcomes are allowing us to show the advantages of the proposed approach.

genetic algorithm, fuzzy logic, classification, intrusion detection, KDD’99 data set.

"Intrusion detection system based on Fast Learning Network, Particle Swarm Optimization and Fuzzy Logic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.626-631, May-2019, Available :http://www.jetir.org/papers/JETIR1905292.pdf

"Intrusion detection system based on Fast Learning Network, Particle Swarm Optimization and Fuzzy Logic", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp626-631, May-2019, Available at : http://www.jetir.org/papers/JETIR1905292.pdf