Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System

Currently, cloud computing and its application is a popular area of research in which, intrusion detection has become an imperative system for detecting several security breaches. The main motivation of this research is to develop an automated intrusion detection system (IDS) for the detection of intrusions in the cloud and internet of things (IoT) systems. After the acquisition of intrusion data from the NSL-KDD, CICIDS2017, Kyoto 2006+, and UNSW-NB15 databases, the Min-Max normalization approach is employed for data rescaling. Then, the relevant attributes/features are selected by proposing pareto optimality based grasshopper optimization algorithm (POGOA), where the selection of relevant features efficiently reduces the system's complexity and computational time. In the POGOA, the relevant features are selected based on pareto optimal solutions that help in enhancing the premature convergence and distribution rate of the traditional GOA. Further, the selected features are given to the stacked autoencoder model for classifying the normal and attack classes. The proposed POGOA with stacked autoencoder model's experiment is conducted using the Matlab environment. The proposed model shows better performance by means of precision, f1-score, accuracy, and recall when related to the comparative models. The proposed POGOA with stacked autoencoder model has obtained 99.32%, 99.84%, 99.56%, and 97.24% of detection accuracy on the CICIDS2017, NSL-KDD, Kyoto 2006+, and UNSW-NB15 databases.

Section: Related Workmentioning

confidence: 99%

Intrusion Detection Using Pareto Optimality Based Grasshopper Optimization Algorithm with Stacked Autoencoder in Cloud and IoT Networks

2023

“…The bird knows its surroundings and uses them to locate and manage food resources [22]. The global optimization approaches are used to calculate the learning approach from the animal's behavior, where the swarm or crowd will be known as a particle [23,24]. The PSO technique updates the Eq.…”

Section: The Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Enhanced PSO Algorithm for Detecting DRDoS Attacks on LDAP Servers

2023

In recent years, there has been an increase in distributed reflective denial of service (DRDoS) attacks, particularly those that target open lightweight directory access protocol (LDAP) servers. These attacks involve transmitting a small request to a large number of available LDAP servers, seeking information from all users. Consequently, the servers respond with significantly more data than the original request, amplifying the traffic and overwhelming the target with massive amounts of data. Therefore, this paper proposes a novel model for detecting LDAP-based DRDoS attacks by utilizing an enhanced particle swarm optimization (PSO) algorithm based on an adaptive weighted threshold (AWTPSO) model. The proposed AWTPSO model incorporates network traffic features and LDAP protocol characteristics to identify attack patterns. It further employs an adaptive weighted threshold model to dynamically adjust the threshold value for each feature. The enhanced PSO algorithm optimizes the threshold values, thereby improving the detection accuracy of the proposed model. The proposed AWTPSO detection model has been validated using the recent CICDDoS2019 dataset (LDAP sub-dataset). The experimental results demonstrate that the AWTPSO model effectively detects LDAP-based DRDoS attacks with exceptional accuracy of 99.99% and minimal false positives of 0.01%, surpassing other state-of-the-art techniques. Consequently, the proposed model presents a highly promising and robust solution for detecting the threat of LDAP-based DRDoS attacks on enterprise networks.

“…Krishna & Arunkumar [27] proposed a hybrid intrusion-detection model that combines particle swarms and Gray Wolf optimization algorithms to select highly relevant features. This combination produces a mixed low-level co-evolutionary functionality by merging both variants with low functionalities to improve the computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection of The Anomaly Network Intrusion Detection Based on Restoration Particle Swarm Optimization

2022

Intrusion detection systems are vital for detecting networking attacks due to their ability to analyze network data and find different types of attacks. The high-dimensional internet data leads to feature selection becoming a fundamental process in network intrusion detection systems. The current approaches are insufficient to determine the most effective features in the network data due to the nature of intrusion attacks appearance compared to the normal data. Moreover, the wrapper feature selection methods suffer from the search time complexity such as the standard PSO algorithm for feature selection. The standard Particle Swarms Optimization (PSO) algorithm suffers from the stagnation effect in local optima. This paper proposes a new wrapper feature selection model called Restoration Particle Swarms Optimization (RPSO) to select highly relevant feature data taking into consideration the limitation of the premature convergence between the particles which results in a stagnation problem during the iteration for the optimal features. Moreover, we utilize the randomness value to overcome the stagnation problem, reduce data volume and decrease the processing time. The Random Forest algorithm uses to classify the feature selected with our solution. As a result, we consider the NSL-KDD benchmark dataset to evaluate the proposed solution. The experiments show that the performance evaluation achieves high results in general accuracy (85%) compared to standard PSO up to 83.86%. Additionally, the results show that the proposed solution increased the detecting rate of low distributing classes in the training data up to (521 classes) compared with standard PSO by (79 classes).