Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm

Abstract

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

“…e Smart Detection system has reached high accuracy and low false-positive rate. Experiments were conducted using two Virtual Linux boxes, Define all the descriptor database variables as the current variables; (5) while True do (6) Split dataset in training and test partitions; (7) Create and train the model using training data partition; (8) Select the most important variables from the trained model; (9) Calculate the cumulative importance of variables from the trained model; (10) if max (cumulative importance of variables) < Variable importance threshold then (11) Exit loop; (12) end (13) Train the model using only the most important variables; (14) Test the trained model and calculate the accuracy; (15) if Calculated accuracy < Accuracy threshold then (16) Exit loop; (17) end (18) Add current model to optimized model set; (19) Define the most important variables from the trained model as the current variables; (20) end (21) end (22) Group the models by number of variables; (23) Remove outliers from the grouped model set; (24) Select the group of models with the highest frequency and their number of variables "N"; (25) Rank the variables by the mean of the importance calculated in step 7; (26) Return the "N" most important variables; [2004][2005] have been used by the researchers to evaluate the performance of their proposed intrusion detection and prevention approaches. However, many such datasets are out of date and unreliable to use [25].…”

“…Finding the balance between academic propositions and the industrial practice of combating DDoS is a big challenge. e academy invests in techniques such as machine learning (ML) and proposes to apply them in areas such as DDoS detection in Internet of ings (IoT) [20,21] sensors, wireless sensors [22], cloud computing [23] and softwaredefined networking (SDN) [18] and work on producing more realistic datasets [24,25] and more effective means of result validation [26,27]. On the other hand, industry segments gradually invested in new paradigms in their solutions such as network function virtualization (NFV) and SDN [28,29] to apply scientific discoveries and modernize network structures.…”

Security and Privacy in Wireless Sensor Networks: Advances and Challenges

“…The intrusion detection (ID) is an important subject in the field of the security of WSNs. Reference [11] presented an ID method based on the synthetic minority oversampling technique (SMOTE) and random forest algorithm. The random forest algorithm combined with the SMOTE provided an effective solution to solve the problem of class imbalance and improves the classification accuracy of ID.…”

An Efficient Network Threat Detection and Classification Method using Anp-Mvps Algorithm in Wireless Sensor Networks

“…Clones are identified and blocked for further communication. Xiaopeng Tan et al [10] demonstrate that intrusion detection for wireless sensor networks is a significant subject in the security domain of WSN. Owing to the imbalance class in KDD Cup 99 dataset, this investigation combines SMOTE with random forest algorithm, and anticipates an ensemble classifier for imbalanced datasets.…”