A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach
Abstract
:1. Introduction
1.1. Motivation and Problem Statement
1.2. Our Contributions
- Recursive Clustering for Data Condensation: We propose a recursive clustering method that integrates compactness and entropy-driven sampling to select a small, highly representative set of observations from the larger training dataset, while preserving key behavioral patterns.
- Monte Carlo Cross-Entropy-Based Feature Selection: We adopt a Monte Carlo Cross-Entropy approach combined with a stability metric of features to consistently select the most stable and relevant features, enhancing the performance of a lightweight IoT-based IDS.
- Efficient IoT-based lightweight IDS: This approach utilizes data condensation and feature selection to reduce computational overhead while preserving or enhancing classification performance. Such efficiency is critical for IoT environments, where both computational resources and memory are often limited.
1.3. Structure
2. Related Work
2.1. IoT Security Mechanisms
2.2. Feature Selection in IDS
2.3. Observation Reduction in IDS
3. The Proposed Approach
Algorithm 1: The general steps for proposed approach |
3.1. Representative Observations
3.1.1. Recursive Clustering
Algorithm 2: Recursive k-means clustering |
3.1.2. Cluster Compactness
3.1.3. Cluster Entropy
3.1.4. Sample Size Determination
An Illustrative Example
Algorithm 3: Representative observation selection. |
3.2. Dimensionality Reduction
Algorithm 4: Dimensionality reduction. |
3.3. Complexity Analysis
4. Evaluation
4.1. Training and Testing Data
4.2. The Selected Classifiers
- 1.
- LinearSVC is a discriminative model that employs an optimization algorithm known as “hinge loss” combined with L2 regularization [37]. The hinge loss penalizes both misclassified observations and those within the margin, while L2 regularization prevents overfitting by adding a penalty for large coefficients. LinearSVC utilizes the “liblinear” solver to find the optimal hyperplane that maximizes the margin between classes in high-dimensional data [38].
- 2.
- Naïve Bayes is a simple Bayesian classifier based on Bayes’ theorem, which assumes that features are independent of each other. It is widely used in many real-world applications, such as spam filtering, text classification, and intrusion detection systems. This classifier selects the class label with the highest probability, making it well suited for large databases and real-time applications due to its low computational cost [39].
- 3.
- J48 is a classification algorithm that employs a tree-based approach, building upon the C4.5 decision tree algorithm [40]. It constructs a tree-like structure leveraging the training data features, based on the concept of information gain. The algorithm recursively divides the dataset into subsets until each subset either belongs to a single class or can no longer be split further. Additionally, this classifier can handle both categorical and numerical data, and it is known for its robustness in dealing with noisy observations.
- 4.
- RandomForest is an ensemble learning classifier that constructs multiple decision trees using the training dataset. It achieves this by randomly selecting subsets of the training data through a process known as bootstrapping. Additionally, during the construction of each tree, random subsets of features are chosen at each split—a process known as feature bagging—which introduces another layer of randomness [41]. This random selection of features helps reduce correlation between the trees and mitigates overfitting by averaging the predictions of many diverse decision trees. As a result, random forests are more robust and less sensitive to noise in the training data, providing higher accuracy and better generalization compared to individual decision trees.
- 5.
- The K-Nearest Neighbor (KNN) is a simple, non-parametric algorithm widely used for both classification and regression tasks. This algorithm classifies a query observation based on its “k” nearest neighbors, using the majority class for classification or the average for regression. While KNN can achieve high accuracy, it is computationally expensive as the size of the training dataset increases. To address this, various optimization techniques, such as KD-trees, Ball-trees, etc. [42], have been developed to speed up the search process and reduce the need to examine the entire dataset. The performance of KNN is influenced by the choice of “k” and the distance metric, with traditional KNN often relying on the Euclidean distance to determine neighborhood boundaries.
- 6.
- Logistic Regression is a statistical method widely used for both binary and multiclass classification problems. In its simplest form, it models the relationship between input features and the probability of a specific outcome using the logistic function, also known as the sigmoid function, which maps any real-valued number into the range [0, 1]. For multiclass classification, logistic regression can be extended using techniques such as “one-vs-rest” or “softmax regression”. During training, logistic regression learns decision boundaries to separate classes by calculating the weights and bias, which are optimized using algorithms like Gradient Descent. In the prediction phase, the model uses the calculated probabilities to predict the class of new observations.
4.3. Performance Evaluation Metrics
4.4. Experimental Settings
5. Experimental Results and Comparisons
5.1. ClassificationPerformance Analysis
5.2. Runtime Performance
5.3. RAM Usage
6. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
References
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Acronym | Full Form |
---|---|
IoT | Internet of Things |
IDS | Intrusion Detection System |
ML | Machine Learning |
DoS | Denial of Service |
DDoS | Distributed Denial of Service |
IIoT | Industrial Internet of Things |
CE | Cross-Entropy |
IC | Intra-Cluster Compactness |
SES | Spatial Entropy Score |
KL | Kullback–Leibler (Divergence) |
FSD | Feature-Selected Data |
CD | Condensed Dataset |
RAM | Random Access Memory |
KNN | K-Nearest Neighbor |
SVM | Support Vector Machine |
TCP/IP | Transmission Control Protocol/Internet Protocol |
N-BaIoT | Network-Based IoT Botnet Dataset |
Edge-IIoTset | Edge-Industrial IoT Dataset |
CICIoT2023 | Collaborative and Intelligent Cybersecurity for IoT 2023 Dataset |
FL | Federated Learning |
Dataset | Total | Normal | Abnormal | Attack Types | Features |
---|---|---|---|---|---|
Edge-IIoTset-Training | 525,273 | 335,606 | 189,667 | 13 | 70 |
Edge-IIoTset-Testing | 225,117 | 143,831 | 81,286 | 13 | 70 |
N-BaIoT-Training | 146,618 | 61,566 | 85,052 | 8 | 115 |
N-BaIoT-Testing | 62,837 | 26,386 | 36,451 | 8 | 115 |
CICIoT2023-Training | 352,282 | 7687 | 344,595 | 7 | 46 |
CICIoT2023-Testing | 150,978 | 3294 | 147,684 | 7 | 46 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
ack | 1.00 | 0.99 | 1.00 | 0.96 | 0.75 | 1.00 | 0.95 | 0.41 | 0.36 | 0.96 | 0.43 | 0.48 | 0.32 | 0.40 | 0.31 | 1.00 | 1.00 | 1.00 |
combo | 1.00 | 0.96 | 1.00 | 0.98 | 0.91 | 0.93 | 0.81 | 0.66 | 0.65 | 0.80 | 0.67 | 0.74 | 0.71 | 0.76 | 0.68 | 1.00 | 0.98 | 1.00 |
junk | 1.00 | 0.94 | 1.00 | 0.95 | 0.93 | 0.86 | 0.61 | 0.42 | 0.52 | 0.60 | 0.53 | 0.49 | 0.50 | 0.58 | 0.03 | 1.00 | 0.97 | 1.00 |
normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.99 | 1.00 | 0.99 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
scan | 1.00 | 1.00 | 1.00 | 0.99 | 0.99 | 1.00 | 0.99 | 0.95 | 0.82 | 0.99 | 0.98 | 0.77 | 0.27 | 0.40 | 0.18 | 1.00 | 1.00 | 1.00 |
syn | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 1.00 | 1.00 | 0.77 | 0.72 | 0.99 | 0.89 | 0.84 | 0.53 | 0.78 | 0.47 | 1.00 | 1.00 | 1.00 |
tcp | 0.38 | 0.20 | 0.67 | 0.50 | 0.00 | 1.00 | 0.25 | 0.00 | 0.00 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.40 | 1.00 | 0.67 |
udp | 1.00 | 0.99 | 1.00 | 0.97 | 0.85 | 1.00 | 0.88 | 0.64 | 0.64 | 0.93 | 0.67 | 0.67 | 0.43 | 0.67 | 0.01 | 1.00 | 1.00 | 1.00 |
udpplain | 1.00 | 0.98 | 1.00 | 0.99 | 0.91 | 1.00 | 0.97 | 0.86 | 0.97 | 0.97 | 0.84 | 0.96 | 0.95 | 0.61 | 0.87 | 1.00 | 1.00 | 1.00 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
ack | 1.00 | 0.99 | 1.00 | 0.93 | 0.67 | 1.00 | 0.75 | 0.18 | 0.20 | 0.86 | 0.22 | 0.27 | 1.00 | 0.44 | 1.00 | 1.00 | 1.00 | 1.00 |
combo | 1.00 | 0.97 | 1.00 | 0.98 | 0.94 | 0.93 | 0.79 | 0.75 | 0.74 | 0.79 | 0.75 | 0.71 | 0.90 | 0.79 | 0.81 | 1.00 | 0.98 | 1.00 |
junk | 1.00 | 0.93 | 0.99 | 0.95 | 0.89 | 0.86 | 0.64 | 0.28 | 0.26 | 0.61 | 0.41 | 0.32 | 0.04 | 0.17 | 0.02 | 1.00 | 0.97 | 1.00 |
normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.63 | 0.66 | 0.51 | 1.00 | 1.00 | 1.00 |
scan | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.83 | 0.69 | 1.00 | 0.96 | 0.89 | 0.37 | 0.97 | 0.33 | 1.00 | 1.00 | 1.00 |
syn | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 | 1.00 | 0.86 | 0.88 | 0.99 | 0.87 | 0.80 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
tcp | 0.27 | 0.18 | 0.73 | 0.09 | 0.00 | 0.09 | 0.18 | 0.00 | 0.00 | 0.09 | 0.00 | 0.00 | 0.55 | 0.00 | 0.27 | 0.18 | 0.09 | 0.55 |
udp | 1.00 | 0.99 | 1.00 | 0.98 | 0.89 | 1.00 | 0.97 | 0.90 | 0.90 | 0.98 | 0.84 | 0.85 | 0.00 | 0.66 | 0.00 | 1.00 | 1.00 | 1.00 |
udpplain | 1.00 | 0.99 | 1.00 | 1.00 | 0.94 | 1.00 | 1.00 | 0.64 | 0.63 | 0.99 | 0.87 | 0.88 | 0.62 | 0.59 | 0.62 | 1.00 | 1.00 | 1.00 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
ack | 1.00 | 0.99 | 1.00 | 0.94 | 0.71 | 1.00 | 0.83 | 0.25 | 0.25 | 0.91 | 0.29 | 0.34 | 0.48 | 0.42 | 0.47 | 1.00 | 1.00 | 1.00 |
combo | 1.00 | 0.97 | 1.00 | 0.98 | 0.93 | 0.93 | 0.80 | 0.70 | 0.69 | 0.80 | 0.71 | 0.72 | 0.79 | 0.77 | 0.74 | 1.00 | 0.98 | 1.00 |
junk | 1.00 | 0.93 | 0.99 | 0.95 | 0.91 | 0.86 | 0.63 | 0.34 | 0.35 | 0.61 | 0.46 | 0.39 | 0.07 | 0.26 | 0.03 | 1.00 | 0.97 | 1.00 |
normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 0.99 | 0.99 | 0.78 | 0.80 | 0.67 | 1.00 | 1.00 | 1.00 |
scan | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.89 | 0.75 | 1.00 | 0.97 | 0.83 | 0.31 | 0.57 | 0.23 | 1.00 | 1.00 | 1.00 |
syn | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 | 1.00 | 0.82 | 0.79 | 0.99 | 0.88 | 0.82 | 0.70 | 0.88 | 0.64 | 1.00 | 1.00 | 1.00 |
tcp | 0.32 | 0.19 | 0.70 | 0.15 | 0.00 | 0.17 | 0.21 | 0.00 | 0.00 | 0.12 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.25 | 0.17 | 0.60 |
udp | 1.00 | 0.99 | 1.00 | 0.97 | 0.87 | 1.00 | 0.93 | 0.75 | 0.75 | 0.95 | 0.75 | 0.75 | 0.00 | 0.66 | 0.00 | 1.00 | 1.00 | 1.00 |
udpplain | 1.00 | 0.99 | 1.00 | 0.99 | 0.93 | 1.00 | 0.98 | 0.73 | 0.77 | 0.98 | 0.86 | 0.92 | 0.75 | 0.60 | 0.72 | 1.00 | 1.00 | 1.00 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
BenignTraffic | 0.84 | 0.81 | 0.57 | 0.56 | 0.50 | 0.54 | 0.56 | 0.45 | 0.52 | 0.56 | 0.54 | 0.55 | 0.22 | 0.39 | 0.32 | 0.85 | 0.82 | 0.63 |
Brute Force | 0.62 | 0.55 | 0.13 | 0.33 | 0.11 | 0.17 | 1.00 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.37 | 0.02 | 0.00 | 0.94 | 0.79 | 0.46 |
DDoS | 1.00 | 1.00 | 0.89 | 0.97 | 0.97 | 0.89 | 0.81 | 0.82 | 0.79 | 0.81 | 0.83 | 0.80 | 0.99 | 0.98 | 1.00 | 1.00 | 1.00 | 0.90 |
DoS | 1.00 | 1.00 | 0.67 | 0.91 | 0.76 | 0.65 | 0.73 | 0.36 | 0.20 | 0.78 | 0.33 | 0.80 | 0.31 | 0.21 | 0.27 | 1.00 | 1.00 | 0.71 |
Mirai | 1.00 | 1.00 | 0.99 | 1.00 | 0.99 | 1.00 | 0.97 | 0.90 | 0.96 | 0.98 | 0.95 | 0.98 | 1.00 | 0.10 | 1.00 | 1.00 | 1.00 | 1.00 |
Recon | 0.86 | 0.83 | 0.63 | 0.66 | 0.63 | 0.61 | 0.54 | 0.53 | 0.45 | 0.54 | 0.45 | 0.44 | 0.87 | 0.91 | 0.46 | 0.84 | 0.82 | 0.64 |
Spoofing | 0.82 | 0.80 | 0.58 | 0.71 | 0.58 | 0.69 | 0.79 | 0.59 | 0.66 | 0.62 | 0.62 | 0.70 | 0.80 | 0.58 | 0.68 | 0.86 | 0.81 | 0.75 |
Web-based | 0.73 | 0.70 | 0.34 | 0.44 | 0.31 | 0.36 | 0.23 | 0.00 | 0.13 | 0.36 | 0.00 | 0.13 | 0.30 | 0.04 | 0.10 | 0.73 | 0.72 | 0.47 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
BenignTraffic | 0.84 | 0.81 | 0.57 | 0.70 | 0.60 | 0.67 | 0.64 | 0.55 | 0.46 | 0.63 | 0.49 | 0.47 | 0.97 | 0.59 | 0.51 | 0.90 | 0.88 | 0.72 |
Brute Force | 0.67 | 0.56 | 0.13 | 0.19 | 0.10 | 0.10 | 0.15 | 0.00 | 0.00 | 0.15 | 0.00 | 0.00 | 0.18 | 0.90 | 0.00 | 0.48 | 0.44 | 0.08 |
DDoS | 1.00 | 1.00 | 0.94 | 0.98 | 0.93 | 0.93 | 0.99 | 0.83 | 0.99 | 0.99 | 0.82 | 0.99 | 0.46 | 0.12 | 0.34 | 1.00 | 1.00 | 0.94 |
DoS | 1.00 | 1.00 | 0.53 | 0.87 | 0.90 | 0.51 | 0.12 | 0.40 | 0.00 | 0.12 | 0.39 | 0.04 | 0.95 | 0.25 | 0.96 | 1.00 | 1.00 | 0.55 |
Mirai | 1.00 | 1.00 | 0.99 | 0.99 | 0.96 | 0.99 | 0.99 | 0.94 | 0.99 | 0.98 | 0.90 | 0.98 | 0.99 | 0.99 | 0.99 | 1.00 | 1.00 | 0.99 |
Recon | 0.86 | 0.84 | 0.62 | 0.71 | 0.68 | 0.69 | 0.60 | 0.37 | 0.61 | 0.61 | 0.45 | 0.65 | 0.21 | 0.20 | 0.02 | 0.88 | 0.87 | 0.75 |
Spoofing | 0.82 | 0.78 | 0.59 | 0.58 | 0.54 | 0.52 | 0.34 | 0.23 | 0.25 | 0.50 | 0.25 | 0.31 | 0.06 | 0.27 | 0.16 | 0.80 | 0.78 | 0.61 |
Web-based | 0.73 | 0.70 | 0.35 | 0.33 | 0.16 | 0.24 | 0.01 | 0.00 | 0.01 | 0.04 | 0.00 | 0.01 | 0.34 | 0.01 | 0.80 | 0.72 | 0.61 | 0.39 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
BenignTraffic | 0.84 | 0.81 | 0.57 | 0.62 | 0.54 | 0.60 | 0.60 | 0.49 | 0.49 | 0.59 | 0.51 | 0.51 | 0.36 | 0.47 | 0.39 | 0.87 | 0.85 | 0.67 |
Brute Force | 0.64 | 0.55 | 0.13 | 0.24 | 0.11 | 0.12 | 0.26 | 0.00 | 0.00 | 0.26 | 0.00 | 0.00 | 0.24 | 0.03 | 0.00 | 0.63 | 0.56 | 0.14 |
DDoS | 1.00 | 1.00 | 0.91 | 0.97 | 0.95 | 0.91 | 0.89 | 0.82 | 0.88 | 0.89 | 0.82 | 0.88 | 0.63 | 0.22 | 0.50 | 1.00 | 1.00 | 0.92 |
DoS | 1.00 | 1.00 | 0.59 | 0.89 | 0.83 | 0.57 | 0.20 | 0.38 | 0.00 | 0.20 | 0.36 | 0.08 | 0.47 | 0.23 | 0.42 | 1.00 | 1.00 | 0.62 |
Mirai | 1.00 | 1.00 | 0.99 | 1.00 | 0.98 | 1.00 | 0.98 | 0.92 | 0.98 | 0.98 | 0.92 | 0.98 | 0.99 | 0.18 | 0.99 | 1.00 | 1.00 | 1.00 |
Recon | 0.86 | 0.83 | 0.63 | 0.69 | 0.65 | 0.65 | 0.57 | 0.44 | 0.52 | 0.57 | 0.45 | 0.53 | 0.33 | 0.33 | 0.03 | 0.86 | 0.85 | 0.69 |
Spoofing | 0.82 | 0.79 | 0.59 | 0.64 | 0.56 | 0.60 | 0.48 | 0.33 | 0.36 | 0.55 | 0.36 | 0.43 | 0.11 | 0.37 | 0.26 | 0.83 | 0.79 | 0.68 |
Web-based | 0.73 | 0.70 | 0.34 | 0.37 | 0.21 | 0.29 | 0.02 | 0.00 | 0.01 | 0.08 | 0.00 | 0.01 | 0.32 | 0.02 | 0.18 | 0.73 | 0.66 | 0.43 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
Backdoor | 0.98 | 0.91 | 0.58 | 0.95 | 0.69 | 0.24 | 0.82 | 0.00 | 0.00 | 0.83 | 0.00 | 0.00 | 0.05 | 0.00 | 0.44 | 0.99 | 1.00 | 0.65 |
DDoS_HTTP | 1.00 | 1.00 | 0.87 | 1.00 | 1.00 | 0.83 | 0.77 | 0.03 | 0.42 | 0.83 | 0.56 | 0.54 | 1.00 | 1.00 | 0.00 | 1.00 | 1.00 | 0.84 |
DDoS_ICMP | 1.00 | 1.00 | 1.00 | 0.99 | 0.99 | 1.00 | 0.99 | 0.98 | 0.97 | 0.99 | 0.97 | 0.97 | 1.00 | 1.00 | 0.97 | 1.00 | 1.00 | 1.00 |
DDoS_TCP | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 | 1.00 | 1.00 | 0.88 | 0.80 | 1.00 | 0.90 | 0.84 | 0.96 | 0.95 | 0.56 | 1.00 | 1.00 | 1.00 |
DDoS_UDP | 1.00 | 1.00 | 0.50 | 1.00 | 1.00 | 0.50 | 0.60 | 1.00 | 0.00 | 1.00 | 0.00 | 0.00 | 1.00 | 1.00 | 0.50 | 1.00 | 1.00 | 0.50 |
Normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 | 0.92 | 0.81 | 0.98 | 0.95 | 0.84 | 1.00 | 1.00 | 0.90 | 1.00 | 1.00 | 1.00 |
OS_Fingerprinting | 0.99 | 0.96 | 0.84 | 0.83 | 0.71 | 0.59 | 1.00 | 0.00 | 0.00 | 0.80 | 0.00 | 0.00 | 0.63 | 0.67 | 0.00 | 0.99 | 0.92 | 0.84 |
Password | 0.98 | 0.95 | 0.89 | 0.88 | 0.88 | 0.82 | 0.72 | 0.53 | 0.60 | 0.79 | 0.57 | 0.63 | 0.43 | 0.16 | 0.99 | 0.95 | 0.95 | 0.87 |
Port_Scanning | 0.96 | 0.94 | 0.82 | 0.93 | 0.91 | 0.92 | 0.85 | 0.00 | 1.00 | 0.93 | 0.00 | 0.99 | 0.09 | 0.29 | 0.06 | 0.96 | 0.97 | 0.84 |
Ransomware | 0.99 | 0.94 | 0.75 | 0.89 | 0.73 | 0.57 | 0.63 | 0.44 | 0.00 | 0.61 | 0.32 | 0.00 | 0.04 | 0.63 | 0.03 | 0.98 | 0.95 | 0.73 |
SQL_injection | 0.99 | 1.00 | 0.46 | 0.80 | 0.80 | 0.39 | 0.50 | 0.00 | 0.00 | 0.16 | 0.00 | 0.00 | 0.80 | 0.61 | 0.25 | 1.00 | 1.00 | 0.57 |
Uploading | 0.99 | 0.91 | 0.64 | 0.93 | 0.83 | 0.69 | 0.88 | 0.83 | 0.00 | 0.83 | 0.81 | 0.83 | 0.94 | 0.79 | 0.44 | 1.00 | 0.98 | 0.77 |
Vulnerability_scanner | 0.97 | 0.89 | 0.91 | 0.93 | 0.71 | 0.93 | 0.94 | 0.00 | 0.92 | 0.95 | 0.00 | 0.93 | 0.94 | 0.38 | 0.80 | 1.00 | 0.94 | 0.98 |
XSS | 0.55 | 0.21 | 0.18 | 0.67 | 0.00 | 0.89 | 0.51 | 0.00 | 0.00 | 1.00 | 0.00 | 0.00 | 0.71 | 0.01 | 0.00 | 0.88 | 0.21 | 0.65 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
Backdoor | 0.98 | 0.83 | 0.62 | 0.82 | 0.63 | 0.14 | 0.11 | 0.00 | 0.00 | 0.26 | 0.00 | 0.00 | 0.89 | 0.00 | 0.15 | 0.97 | 0.93 | 0.54 |
DDoS_HTTP | 1.00 | 1.00 | 0.91 | 0.99 | 1.00 | 0.94 | 0.54 | 0.00 | 0.01 | 0.73 | 0.32 | 0.17 | 0.97 | 1.00 | 0.00 | 1.00 | 1.00 | 0.94 |
DDoS_ICMP | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
DDoS_TCP | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.94 | 0.63 | 1.00 | 0.96 | 0.74 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
DDoS_UDP | 0.83 | 0.83 | 1.00 | 1.00 | 0.17 | 1.00 | 1.00 | 0.17 | 0.00 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.83 | 1.00 |
Normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.98 | 0.97 | 0.99 | 0.98 | 0.97 | 0.67 | 0.54 | 0.75 | 1.00 | 1.00 | 1.00 |
OS_Fingerprinting | 0.99 | 0.97 | 0.70 | 0.55 | 0.15 | 0.46 | 0.23 | 0.00 | 0.00 | 0.16 | 0.00 | 0.00 | 1.00 | 0.08 | 0.00 | 1.00 | 0.97 | 0.72 |
Password | 0.98 | 0.94 | 0.88 | 0.96 | 0.88 | 0.89 | 0.92 | 0.67 | 0.29 | 0.95 | 0.81 | 0.34 | 0.38 | 0.78 | 0.05 | 1.00 | 0.98 | 0.94 |
Port_Scanning | 0.97 | 0.96 | 0.82 | 0.79 | 0.69 | 0.75 | 0.76 | 0.00 | 0.01 | 0.76 | 0.00 | 0.12 | 1.00 | 0.06 | 0.36 | 0.96 | 0.94 | 0.82 |
Ransomware | 0.99 | 0.93 | 0.74 | 0.93 | 0.62 | 0.41 | 0.38 | 0.04 | 0.00 | 0.42 | 0.01 | 0.00 | 0.77 | 0.02 | 0.39 | 0.99 | 0.97 | 0.79 |
SQL_injection | 1.00 | 1.00 | 0.42 | 0.50 | 0.63 | 0.24 | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.42 | 1.00 | 0.14 | 1.00 | 1.00 | 0.23 |
Uploading | 1.00 | 0.93 | 0.66 | 0.69 | 0.64 | 0.56 | 0.90 | 0.58 | 0.00 | 0.99 | 0.33 | 0.02 | 0.44 | 1.00 | 0.10 | 1.00 | 0.99 | 0.58 |
Vulnerability_scanner | 0.97 | 0.91 | 0.90 | 0.83 | 0.84 | 0.83 | 0.83 | 0.00 | 0.83 | 0.83 | 0.00 | 0.83 | 0.83 | 1.00 | 0.81 | 0.91 | 0.91 | 0.88 |
XSS | 0.58 | 0.24 | 0.17 | 0.07 | 0.00 | 0.10 | 0.04 | 0.00 | 0.00 | 0.04 | 0.00 | 0.00 | 0.07 | 0.01 | 0.00 | 0.27 | 0.01 | 0.15 |
Class Label | J48 | KNN | LinearSVC | LogisticRegression | NaiveBayes | RandomForest | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
Backdoor | 0.98 | 0.87 | 0.60 | 0.88 | 0.66 | 0.18 | 0.20 | 0.00 | 0.00 | 0.40 | 0.00 | 0.00 | 0.09 | 0.00 | 0.23 | 0.98 | 0.96 | 0.59 |
DDoS_HTTP | 1.00 | 1.00 | 0.89 | 0.99 | 1.00 | 0.88 | 0.64 | 0.01 | 0.02 | 0.78 | 0.41 | 0.26 | 0.98 | 1.00 | 0.00 | 1.00 | 1.00 | 0.89 |
DDoS_ICMP | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 0.99 | 0.99 | 0.97 | 0.99 | 0.99 | 0.98 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 |
DDoS_TCP | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 0.91 | 0.71 | 1.00 | 0.92 | 0.79 | 0.98 | 0.97 | 0.71 | 1.00 | 1.00 | 1.00 |
DDoS_UDP | 0.91 | 0.91 | 0.67 | 1.00 | 0.29 | 0.67 | 0.75 | 0.29 | 0.00 | 0.67 | 0.00 | 0.00 | 1.00 | 1.00 | 0.67 | 1.00 | 0.91 | 0.67 |
Normal | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.95 | 0.88 | 0.99 | 0.97 | 0.90 | 0.80 | 0.70 | 0.82 | 1.00 | 1.00 | 1.00 |
OS_Fingerprinting | 0.99 | 0.97 | 0.76 | 0.66 | 0.25 | 0.52 | 0.37 | 0.00 | 0.00 | 0.27 | 0.00 | 0.00 | 0.77 | 0.14 | 0.00 | 1.00 | 0.94 | 0.77 |
Password | 0.98 | 0.95 | 0.88 | 0.92 | 0.88 | 0.86 | 0.81 | 0.59 | 0.39 | 0.86 | 0.67 | 0.44 | 0.41 | 0.27 | 0.09 | 0.98 | 0.97 | 0.90 |
Port_Scanning | 0.97 | 0.95 | 0.82 | 0.86 | 0.78 | 0.83 | 0.80 | 0.00 | 0.03 | 0.84 | 0.00 | 0.21 | 0.17 | 0.10 | 0.11 | 0.96 | 0.96 | 0.83 |
Ransomware | 0.99 | 0.93 | 0.74 | 0.91 | 0.67 | 0.48 | 0.48 | 0.07 | 0.00 | 0.50 | 0.03 | 0.00 | 0.07 | 0.04 | 0.05 | 0.99 | 0.96 | 0.76 |
SQL_injection | 1.00 | 1.00 | 0.44 | 0.61 | 0.70 | 0.30 | 0.00 | 0.00 | 0.00 | 0.09 | 0.00 | 0.00 | 0.55 | 0.76 | 0.18 | 1.00 | 1.00 | 0.32 |
Uploading | 1.00 | 0.92 | 0.65 | 0.79 | 0.72 | 0.62 | 0.89 | 0.68 | 0.00 | 0.90 | 0.47 | 0.03 | 0.60 | 0.88 | 0.16 | 1.00 | 0.98 | 0.66 |
Vulnerability_scanner | 0.97 | 0.90 | 0.91 | 0.88 | 0.77 | 0.88 | 0.88 | 0.00 | 0.87 | 0.88 | 0.00 | 0.88 | 0.88 | 0.55 | 0.80 | 0.95 | 0.92 | 0.93 |
XSS | 0.57 | 0.23 | 0.17 | 0.13 | 0.00 | 0.18 | 0.08 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.13 | 0.01 | 0.00 | 0.41 | 0.02 | 0.24 |
Dataset | J48 | KNN | LinearSVC | Logistic Regression | NaiveBayes | RandomForest | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
CD | FSD | CD | FSD | CD | FSD | CD | FSD | CD | FSD | CD | FSD | |
N-BaIoT | 8 / 9 | 9 / 9 | 6 / 9 | 9 / 9 | 1 / 9 | 1 / 9 | 2 / 9 | 3 / 9 | 6 / 9 | 6 / 9 | 8 / 9 | 9 / 9 |
CICIoT2023 | 7 / 8 | 2 / 8 | 4 / 8 | 5 / 8 | 3 / 8 | 3 / 8 | 3 / 8 | 3 / 8 | 3 / 8 | 3 / 8 | 7 / 8 | 2 / 8 |
Edge-IIoTset | 12 / 14 | 4 / 14 | 8 / 14 | 7 / 14 | 3 / 14 | 2 / 14 | 3 / 14 | 3 / 14 | 6 / 14 | 4 / 14 | 13 / 14 | 5 / 14 |
Classifier | N-BaIoT | CICIoT2023 | Edge-IIoTset | ||||||
---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
LinearSVC | 17,073 | 63 | 2325 | 107,377 | 449 | 23,941 | 120,607 | 269 | 151,133 |
NaiveBayes | 179 | 10 | 41 | 195 | 4 | 78 | 470 | 17 | 158 |
J48 | 5422 | 133 | 2919 | 2498 | 74 | 2941 | 2574 | 53 | 1357 |
RandomForest | 56,620 | 3247 | 47,878 | 35,287 | 1874 | 80,371 | 33,025 | 1376 | 43,326 |
KNN | 1759 | 15 | 184 | 2122 | 21 | 590 | 5485 | 40 | 954 |
LogisticRegression | 65,510 | 658 | 23,070 | 73,650 | 1011 | 46,532 | 394,870 | 4219 | 171,205 |
Classifier | N-BaIoT | CICIoT2023 | Edge-IIoTset | ||||||
---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
LinearSVC | 20 | 14 | 18 | 26 | 19 | 16 | 46 | 32 | 43 |
NaiveBayes | 396 | 48 | 53 | 347 | 142 | 120 | 1368 | 362 | 427 |
J48 | 15 | 6 | 19 | 30 | 6 | 29 | 47 | 33 | 32 |
RandomForest | 500 | 446 | 542 | 1392 | 1123 | 3049 | 2350 | 2100 | 3909 |
KNN | 207,015 | 1938 | 8999 | 2,204,285 | 10,745 | 331,032 | 10,658,362 | 26,787 | 295,290 |
LogisticRegression | 22 | 6 | 22 | 16 | 16 | 16 | 48 | 25 | 52 |
Classifier | N-BaIoT | CICIoT2023 | Edge-IIoTset | ||||||
---|---|---|---|---|---|---|---|---|---|
Full | CD | FSD | Full | CD | FSD | Full | CD | FSD | |
J48 | 2.88 | 0.0859 | 2.43 | 2.46 | 0.0039 | 5.88 | 1.15 | 0.3477 | 4.80 |
KNN | 140.60 | 1.15 | 20.21 | 135.11 | 3.02 | 47.58 | 307.16 | 5.05 | 72.89 |
LinearSVC | 5.61 | 0.0938 | 6.20 | 12.55 | 0.6836 | 0.0508 | 7.32 | 0.2109 | 4.64 |
LogisticRegression | 130.68 | 2.08 | 33.77 | 128.90 | 2.79 | 78.67 | 288.55 | 4.12 | 107.91 |
NaiveBayes | 1.19 | 0.2188 | 0.7383 | 1.14 | 0.1523 | 0.0508 | 7.76 | 2.47 | 0.2383 |
RandomForest | 5.97 | 0.3320 | 0.2148 | 146.45 | 8.27 | 488.91 | 102.88 | 18.41 | 7.38 |
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Almalawi, A. A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach. Sensors 2025, 25, 2235. https://doi.org/10.3390/s25072235
Almalawi A. A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach. Sensors. 2025; 25(7):2235. https://doi.org/10.3390/s25072235
Chicago/Turabian StyleAlmalawi, Abdulmohsen. 2025. "A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach" Sensors 25, no. 7: 2235. https://doi.org/10.3390/s25072235
APA StyleAlmalawi, A. (2025). A Lightweight Intrusion Detection System for Internet of Things: Clustering and Monte Carlo Cross-Entropy Approach. Sensors, 25(7), 2235. https://doi.org/10.3390/s25072235