FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks

Abstract The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics.

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Publication Details

Journal
Cybersecurity
Published
2024-09-03
DOI
https://doi.org/10.1186/s42400-024-00223-x
Citations
12
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
4.19

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article

FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks

Yanika Kongsorot, Chaeeon Kim, Phet Aimtongkham, Chakchai So–In
12 citations
Cybersecurity
Network Security and Intrusion Detection
4.19
article

FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks

Yanika Kongsorot, Chaeeon Kim, Phet Aimtongkham, Chakchai So–In
article en
12 citations

Abstract

Abstract The Internet of Things (IoT) has gained popularity and is widely used in modern society. The growth in the sizes of IoT networks with more internet-connected devices has led to concerns regarding privacy and security. In particular, related to the routing protocol for low-power and lossy networks (RPL), which lacks robust security functions, many IoT devices in RPL networks are resource-constrained, with limited computing power, bandwidth, memory, and battery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifically targets neighboring nodes through DIO messages and poses a significant security threat to RPL-based IoT networks. Recent studies have proposed methods for detecting and mitigating this attack; however, they produce high false-positive and false-negative rates in detection tasks and cannot fully protect RPL networks against this attack type. In this paper, we propose a novel fuzzy logic-based intrusion detection scheme to secure the RPL protocol (FLSec-RPL) to protect against this attack. Our method is built of three key phases consecutively: (1) it tracks attack activity variables to determine potential malicious behaviors; (2) it performs fuzzy logic-based intrusion detection to identify malicious neighbor nodes; and (3) it provides a detection validation and blocking mechanism to ensure that both malicious and suspected malicious nodes are accurately detected and blocked. To evaluate the effectiveness of our method, we conduct comprehensive experiments across diverse scenarios, including Static-RPL and Mobile-RPL networks. We compare the performance of our proposed method with that of the state-of-the-art methods. The results demonstrate that our method outperforms existing methods in terms of the detection accuracy, F1 score, power consumption, end-to-end delay, and packet delivery ratio metrics.

CybersecurityVol. 7(1)
Khon Kaen University (TH)
Khon Kaen University
Openalex Percentile: Top 5%
Network Security and Intrusion Detection
4.19
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