Baiardi, "CIDS: A Framework for
Intrusion Detection in Cloud Systems", Ninth International Conference on Information Technology - New Generations, Las Vegas-NV, pp.
Market Research Future published a research report on "Perimeter
Intrusion Detection Systems Market Research Report- Global Forecast till 2023" - Market Analysis, Scope, Stake, Progress, Trends and Forecast to 2023.
Based on these three characteristics several research has been proposed to enhance the performance of
intrusion detection such as: Decision Tree (DT), Support Vector Machine (SVM), and Naive Bayesian (NB) [5-8]; k-Nearest neighbor (KNN) [9, 10]; Fuzzy Logic (FL), Genetic Algorithm (GA), and Rough Set (RS) [2, 11, 12]; Artificial Neural Network (ANN) and K-means [13, 14, 77].
In this paper, in order to reduce the energy consumption of sensor nodes participating in
intrusion detection activities, we presented a lightweight
intrusion detection method based on Fuzzy Clustering Algorithm (LIDFCM) for resource-constrained WSNs.
The focus of our research on ML and DM methods is highly compatible with
intrusion detection with the cable-connected network.
In general,
intrusion detection approaches can be divided into two categories: misuse-based detection and anomaly-based detection [1-3].
Dorothy Denning [1] defined
intrusion detection in 1987.
This
intrusion detection system will give the user, better monitoring the network environment and provide an additional tool to make the computing systems secure.
This indicates that success of
intrusion detection is limited by the availability of the recent attack signatures in the database.