G-IDCS: Graph-Based Intrusion Detection and Classification System for CAN Protocol

Park, Sung Bum; Jo, Hyo Jin; Lee, Dong Hoon

doi:10.1109/access.2023.3268519

Cited by 19 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This precision in node detection surpasses traditional clustering methods, highlighting its criticality across diverse applications. For instance, in anomaly detection scenarios [51][52][53], the precise identification of unusual nodes is indispensable for tasks like fraud detection in financial transactions [54,55], intrusion detection in computer networks [56,57], and rare disease identification in biological networks [58,59]. Similarly, within network resource allocation frameworks [60,61]such as transportation or social networks, the ability to pinpoint nodes with specific characteristics is crucial for optimizing traffic flow, efficiently allocating resources, and upholding infrastructure integrity.…”

Section: -3-discussionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

View full text Add to dashboard Cite

Graph partitioning is essential for uncovering cohesive communities within complex networks. This paper introduces LouvainSplit, an innovative algorithm designed to enhance graph partitioning efficiency and accuracy. LouvainSplit leverages advanced techniques in feature representation, community detection, and evaluation, providing a robust framework for addressing challenges inherent in graph partitioning tasks across diverse domains. At its core, LouvainSplit utilizes a feature pyramid representation approach to extract both basic and summary features from input graphs at multiple granularity levels. This methodology ensures a subtle evaluation of graph data by capturing fundamental graph information alongside intricate structural patterns, thus offering a comprehensive representation of underlying community structures. A key innovation of our approach is the integration of the Louvain algorithm, renowned for its efficacy in community detection. By leveraging pairwise cosine similarities computed from node feature vectors, the Louvain algorithm optimizes modularity iteratively, effectively partitioning graphs into cohesive communities. This iterative process facilitates the identification of significant network structures within complex networks, providing valuable insights into their organizational dynamics. To comprehensively evaluate LouvainSplit's performance, we integrated diverse graph partitioning algorithms, including PyMetis, Genetic Algorithm (GA), DBSCAN, KMeans, OPTICS, and spectral clustering to evaluate the effectiveness of LouvainSplit and using popular evaluation metrics across diverse benchmarks spanning various domains and graphs. The results demonstrate LouvainSplit's superiority in terms of recall score, modularity, and scalability compared to existing methods. Moreover, LouvainSplit maintains competitive average runtime values, ensuring efficient processing even with large-scale datasets.

show abstract

Section: -3-discussionmentioning

confidence: 99%

Efficient Graph Partitioning using LouvainSplit Algorithm

Javadi,

Aghighi,

Azadbakht

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Park et al [24] introduce G-IDCS, a graph-based intrusion detection and classification system, to improve in-vehicle network security. It overcomes the limitations of existing intrusion detection systems, such as requiring large CAN messages and not classifying attack types.…”

Section: B Contributionmentioning

confidence: 99%

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach

Sadia,

Farhan,

Haq

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In this era, there are plenty of wireless devices that are being used with the support of WI-FI (Wireless Fidelity) and they need to be maintained and authorized. Wireless Sensor Networks (WSN), a cornerstone of modern wireless technology, offer cost-efficient solutions for diverse monitoring tasks but are simultaneously exposed to a myriad of security threats, including unauthorized access, attacks, and suspicious activities. These vulnerabilities can significantly degrade the performance and reliability of WSNs, making the early detection and mitigation of such threats imperative. Intrusion Detection Systems (IDS) emerge as crucial tools in safeguarding WSNs against these challenges. Numerous studies focus on enhanced Intrusion Detection model accuracy and decrease in loss with higher Detection Rate and lower False Alarm Rate, because of this, eliminating the repetitive feature of the dataset is exhibited. This study introduces a sophisticated Network Intrusion Detection System (NIDS) designed to safeguard Wi-Fi-based WSNs from prevalent cyber threats, such as impersonation, flooding, and injection attacks. At the heart of our approach is a meticulous feature selection process that enhances the dataset's utility by eliminating null values, substituting unknown entries with a placeholder ('NONE'), and refining the feature set to include only the most relevant indicators of potential security breaches. Initially, from a pool of 154 features, a subset of 76 is selected, which is further narrowed down to 13 pivotal features, ensuring a focused and efficient analysis. Employing standard scaler function for feature scaling and preprocessing, this research train proposed a Convolutional Neural Network (CNN) based approach aiming for optimal intrusion detection and prevention across multiclass classifications within WSN environments. The study aims to enhance detection accuracy, reduce loss values, and decrease false alarm rates, comparing it to CNN, Deep Neural Network (DNN) (5), DNN (3), and (Long Short-Term Memory) LSTM networks. The model's performance is evaluated using various metrics, including precision, recall, support, F1 score, and macro-average. The culmination of our research efforts is evidenced by the exceptional performance of the CNN model, achieving an impressive accuracy rate of 97% and a loss metric of 0.14, all while maintaining a minimal False Alarm Rate. This study significantly advances IDS accuracy while simultaneously reducing false alarms, thus fortifying the security posture of WSNs in the face of evolving cyber threats. INDEX TERMSWSN, Wi-Fi, NIDS, WIDS attacks, Security issues, Network threats, Feature engineering, multiclass classification I. INTRODUCTION W IRELESS Sensor Network (WSN) is one of the ultimate sources for communication of wireless base network domain [1]. WSNs integrate with the help of sensor nodes that establish with different topologies such as star, tree, or mesh [2], it senses the data flow and transmission within the wireless network and the dominant serviceabilities ...

show abstract

“…There are many existing methods that use shallow learning models or their own algorithms. The authors of [10] proposed G-IDCS, a graph-based intrusion detection and classification system that combines threshold-based IDS with a machine-learning-based classifier to overcome computation limitations. Derhab et al [11] proposed H-IDFS, a Histogram-based Intrusion Detection and Filtering framework.…”

Section: Related Workmentioning

confidence: 99%

“…These previous approaches [10][11][12][13][14][15][16][17][18][19][20] use specific features for anomaly detection and extract information from the entire frame sequence of the CAN bus. However, this may lead to false positives when encountering event frames that do not present during training.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Anomaly Detection Method Based on Multiple LSTM-Autoencoder Models for In-Vehicle Network

Kim,

You

2023

Electronics

View full text Add to dashboard Cite

The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system’s high precision with a 99% detection rate in identifying anomalies.

show abstract

G-IDCS: Graph-Based Intrusion Detection and Classification System for CAN Protocol

Cited by 19 publications

References 33 publications

Efficient Graph Partitioning using LouvainSplit Algorithm

Efficient Graph Partitioning using LouvainSplit Algorithm

Intrusion Detection System for Wireless Sensor Networks: A Machine Learning Based Approach

An Anomaly Detection Method Based on Multiple LSTM-Autoencoder Models for In-Vehicle Network

Contact Info

Product

Resources

About