Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

Abstract

In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.

Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets

“…In equitation (11), we have the S1(V1) transfer function which first applied an arbitrary ind i to V1 function, the outcome is then applied to the S1 function. A similar operation is designed for the S2(V1), S1(V2), and S2(V2) transfer functions, which are defined in Eqs (12)(13)(14).…”

“…These methods aim to reduce the number of features to the bearest minimum without information loss [5]. Feature selection (FS) methods have been successfully applied to many domains, including computational medicine [6,7], clustering [8,9], intrusion and spam detection [10][11][12][13], and genomics [14].…”

i-2NIDS Novel Intelligent Intrusion Detection Approach for a Strong Network Security

“…Multilayer Perceptron has an ability to handle large amount of data. Furthermore, it works well in solving the complex non-linear issues (Khater et al, 2021). Therefore, Aurora et al have been based on MLP neural network and other algorithms: Logistic Regression (LR), Voted Perceptron (VPP) and Radial Base Function (RBF) over the NSL-KDD dataset, in order to improve the intrusion detection task (Arora & Chauhan, 2017).…”

Binary Approaches of Quantum-Based Avian Navigation Optimizer to Select Effective Features from High-Dimensional Medical Data

“…To tackle this issue, many effective methods have been proposed to select effective features by reducing such disadvantageous features [6][7][8][9][10]. Feature selection (FS) is employed in a wide range of real-world applications, including disease diagnosis [11][12][13][14][15], text clustering [16,17], intrusion detection systems [18][19][20][21], e-mail spam detection [22][23][24][25], and genomic analysis [25][26][27][28][29].…”