Anomaly Detection using Machine Learning Techniques in Wireless Sensor Networks

J. Panda S. Indu Gebrekiros Gebreyesus Gebremariam

22 Agustus 2023

Wireless sensor networks (WSNs) are an emerging military and civilian technology that uses sensors. Sensor networks are hierarchical and chaotic in remote, unmonitored sites. Wireless sensor networks pose unique security threats due to their public location and wireless transmission. WSNs are vulnerable to various routing attacks, including Black holes, Sybil, sinkholes and wormholes. In this paper, we proposed advanced intrusion detection systems based on hybrid machine learning (AIDS-HML) in wireless sensor networks to identify and classify attacks. Hybrid machine learning classifiers identify wireless sensor network dangers. Benchmark datasets are used to compare the proposed model to baseline models in terms of precision, recall, f1-score, and accuracy. The scheme is trained and evaluates prediction models. This confirms that the detection accuracy achieved 99.80% using the NSL-KDD benchmark dataset based on hybrid random forest and extreme gradient boost (RF-XGB). The hybrid cluster labelling K-Means (CLK-M) s achieved better classification accuracy of 100% using UNSW_NB15, and CICIDS2017 benchmark datasets for binary classification of label attacks. Different attack detection metrics were compared against various benchmark datasets to evaluate the quality of this work. The proposed system is efficient in simulations for feature extraction and route discovery and detection attacks achieving an accuracy of 99.46%.

Raniyah Wazirali Tarik Abu-Ain Rami Ahmad

23 Juni 2022

Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

Selina Sharmin Md. Ashraf Uddin Md. Manowarul Islam + 2 lainnya

17 Februari 2024

In the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.

Tariq Shahzad Umair Ahmad Salaria Tehseen Mazhar + 4 lainnya

2024

Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. This study also focuses on the use of wireless sensor networks in different fields. Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.

M. Ahmadi A. Arkan

3 Maret 2023

Wireless sensor networks are considered as the foundation of the Internet of Things. Inherent problems in wireless sensor networks such as power consumption, lack of flexibility, and disability in development and programming have led to serious challenges in these networks. Software-defined networking (SDN) is flexible with development and programming capabilities that decouple the control and data planes. The combination of wireless sensor networks and software-defined networks has created the idea of software-defined wireless sensor networks (SDWSNs). Security is considered as one of the most fundamental issues in any network. Due to their combinatorial nature, the software-defined wireless sensor networks faced a variety of security challenges for both wireless sensor networks and software-defined networks. This paper proposes a novel architecture with an unsupervised intrusion detection algorithm using a hierarchical approach to improve the security of integrated software-defined wireless sensor networks. In the proposed architecture, the sensors are not fully dependent on the SDWSN controller; instead, they run the appropriate intrusion detection algorithm module locally at the layer. The data analysis results in different zones, produced by clustering based on entropy and cumulative point similarity as criteria, are sent to the SDWSN controller, and decisions are made after the final check of data normality or abnormality. To examine the effectiveness of the proposed architecture and algorithm, the sensors were simulated on Cooja, WSN-DS and NSL-KDD standardized datasets. The results show that the proposed method is able to detect the abnormal traffic up to 97%.