Intrusion Detection Using Fuzzy Stochastic Local Search Classifier

2012 ◽  
Author(s):  
Bachir Bahamida ◽  
Dalila Boughaci
Keyword(s):  
Intrusion Detection ◽  
Local Search ◽  
Stochastic Local Search

Intrusion Detection Using Fuzzy Meta-Heuristic Approaches

2014 ◽  
Vol 5 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Bachir Bahamida ◽  
Dalila Boughaci
Keyword(s):  
Tabu Search ◽  
Intrusion Detection ◽  
Local Search ◽  
Knowledge Base ◽  
Fuzzy Rule ◽  
Intrusion Detection Systems ◽  
Stochastic Local Search ◽  
Traffic Data ◽  
Detection Systems ◽  
The Third

Due to a growing number of intrusion events, organizations are increasingly implementing various intrusion detection systems that classify network traffic data as normal or anomaly. In this paper, three intrusion detection systems based fuzzy meta-heuristics are proposed. The first one is a fuzzy stochastic local search (FSLS). The second one is a fuzzy tabu search (FTS) and the third one is a fuzzy deferential evolution (FDE). These classifiers are built on a knowledge base modelled as a fuzzy rule “if-then”. The main purpose of these methods is to get the highest quality solutions by optimizing the fuzzy rules generation. The proposed classifiers FSLS, FTS and FDE are tested on the benchmark KDD'99 intrusion dataset and compared with some well-known existing techniques for intrusion detection. The results show the efficiency of the proposed approaches in the intrusion detection field.

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local Search

2017 ◽  
Vol 44 (4) ◽  
pp. 32-37
Author(s):  
Shohei Sassa ◽  
Kenji Kanazawa ◽  
Shaowei Cai ◽  
Moritoshi Yasunaga
Keyword(s):  
Local Search ◽  
Stochastic Local Search
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