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GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper proposes a graph-based Gaussian naive Bayes (GGNB) intrusion detection system for CAN bus to detect any CAN-monitoring attacks, including mixed attacks without modifying the protocol.
2. The proposed methodology exhibits 98.57% detection accuracy, which is better or comparable with the state-of-the-art and yields many fold reduction in runtime compared to existing classification methods.
3. The GGNB algorithm integrates common graph properties with PageRank (PR)-related features into a GNB algorithm and requires fewer slices, LUTs, flip-flops, and DSP units than conventional neural network architecture.
Article analysis:
The article “GGNB: Graph-based Gaussian Naive Bayes Intrusion Detection System for CAN Bus” provides an overview of existing CAN bus attacks and intrusion detection systems (IDSs), as well as a proposed graph-based anomaly detection system to secure the CAN bus communication system. The article is written in an organized manner and provides detailed information on the topic at hand.
The article is reliable in terms of its content as it provides evidence for its claims by citing relevant research papers and studies conducted by various researchers and organizations such as NHTSA, OEMs, etc., which adds credibility to the article's claims. Furthermore, the authors provide a comprehensive comparison between their proposed IDS and existing schemes which further strengthens their argument that their proposed methodology is more efficient than existing ones.
However, there are some potential biases present in the article that should be noted. For instance, while discussing existing attack prevention methodologies, only those techniques that support the authors' argument are discussed while other techniques are not mentioned or explored in detail which could lead to one-sided reporting of the issue at hand. Additionally, some of the claims made by the authors lack evidence or supporting data which could weaken their argument if not addressed properly.
In conclusion, this article provides a comprehensive overview of existing CAN bus attacks and IDSs as well as a proposed graph-based anomaly detection system to secure CAN bus communication systems. While it is reliable in terms of its content due to citing relevant research papers and studies conducted by various researchers and organizations, there are some potential biases present in the article such as one-sided reporting of certain topics and lack of evidence for certain claims made by the authors which should be addressed properly for better understanding of the issue at hand.