• A.ALAGULAKSHMI

A new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation of the search space.The input data is a set of events, a set of actions and the number of states in the target FSM. Designing an efficient and scalable packet filter for modern computer networks becomes more challenging each day. Most current packet filtering mechanisms cannot deal with those requirements because their optimization algorithms either cannot scale with the increased size of the filtering code or exploit simple domain-specific optimizations that cannot guarantee to operate properly in case of complex filters. A novel packet filtering model based on (multilevel) finite state automata augmented with predicates. A new method of learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation of the search space.

Finite State Automata (FSA), Packet filters, Ant Colony Optimization (ACO).

"Efficient and Scalable Packet Filter with Finite State Automata Based On Ant Colony Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.141-145, February-2019, Available :http://www.jetir.org/papers/JETIR1902524.pdf

"Efficient and Scalable Packet Filter with Finite State Automata Based On Ant Colony Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp141-145, February-2019, Available at : http://www.jetir.org/papers/JETIR1902524.pdf