A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree

Abstract

ABSTRACT:Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent years to protect computer systems from intruders. Since intrusion detection systems deal with a large amount of data, keeping the best quality of features that represent the whole data and removing the redundant and irrelevant features are important tasks in these systems. In this paper, a novel DIDS based on the combination of Cuttlefish Optimization Algorithm (CFA) and Decision Tree (DT) is proposed. The proposed system uses an agent called Rule and Feature Generator Agent (RFGA) for reducing the dimensionality of the data by generating a subset of features with their corresponding rules. RFGA agent uses CFA to search for optimal subset of features, while DT is used as a measurement on the selected features. The proposed model is tested on the KDD Cup 99 dataset. The obtained results show that the proposed system gives a better performance even with a small subset of 5 features when compared with the using all 41 features.

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

“…Feature extraction is less exposed to overfitting and perform good accuracy for the classification in comparison to the feature selection methods. However, the data description is lost occasionally after the transformation, and the cost of this process is expensive in several datasets [43,44].…”

“…Through the literature and table III there are three methods ( [51,58,63]) dependent on the clustering technique using K-means. The authors in [51] used K-means for removing non-relevant features, while [58] in the similarity value was used to separate the features in multiple clusters, and in [44] the algorithm was used to divide the features into the most relevant and noisy clusters. The mentioned three methods were performed in the highly dimension datasets such as text, microarray and texture image classification.…”

Optimization Algorithms for Intrusion Detection System: A Review

“…ID is a wide field of research area, so there are many common algorithms that have been used in field of the intrusion problems such as evolutionary algorithms this include biology inspired algorithms such as Genetic algorithm [11], [12], Practical Swarm Optimization PSO [13], [14], [15], [16], [17], [18] Cuttlefish CFA [19], [20], [21], [2], Artificial Bee Colony [22], [23], [24] and Ant Colony [25], [26]. The paper provides an overview of applying some optimization algorithms to the problem of intrusion detection in past ten years.…”

“…In like manner, authors in [21] proposed a distributed intrusion detection system (DIDS) based on cuttlefish optimization algorithm (CFA) and decision tree DT. The system used an agent called rule and feature generator agent (RFGA) which is used for generating a subset of features by using CFA.…”

A new clustering method based on the bio‐inspired cuttlefish optimization algorithm

“…Bio-inspired optimization algorithms have been used to solve different types of problems such as engineering problems (Karagöz & Yıldız, 2017), (A. R. Yıldız, Kurtuluş, Demirci, Yıldız, & Karagöz, 2016), (B. S. Yıldız & Yıldız, 2017), (B. S. Yıldız, 2017), (B. S. Yıldız & Yıldız, 2018), (Kiani & Yıldız, 2016), (Yıldız, 2013), and (Yıldız, 2012), feature selection (Adel S. Eesa, Orman, & Brifcani, 2015), (Rostami & Moradi, 2014), (Ahmad, Salah, Sabry, ALhabib, & Shaikhow, 2018) and (Adel S. Eesa, Abdulazeez, & Orman, 2017), data mining (Shanghooshabad & Abadeh, 2016), (Shi, Tian, Kou, Peng, & Li, 2011) and (Parpinelli, Lopes, & Freitas, 2002), and image processing (Bejinariu, Costin, Rotaru, Luca, & Nita, 2015), (Jino Ramson, Lova Raju, Vishnu, & Anagnostopoulos, 2019), and (Hemanth & Balas, 2019).…”