This paper presents the advantages of information foraging theory matched with traditional information retrieval theory and user behavior analysis theory, a search content framework for information foraging theory is ...This paper presents the advantages of information foraging theory matched with traditional information retrieval theory and user behavior analysis theory, a search content framework for information foraging theory is described, on a thor- ough review of the two research branches i.e. the basic concept of information foraging theory and the elementary mod- els of information foraging theory, an extended framework is proposed,. Several problems for future research are also identified through.展开更多
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....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 bat‑tery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifcally targets neighboring nodes through DIO messages and poses a signifcant 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 consecu‑tively: (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 efectiveness 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.展开更多
文摘This paper presents the advantages of information foraging theory matched with traditional information retrieval theory and user behavior analysis theory, a search content framework for information foraging theory is described, on a thor- ough review of the two research branches i.e. the basic concept of information foraging theory and the elementary mod- els of information foraging theory, an extended framework is proposed,. Several problems for future research are also identified through.
基金funded by a Royal Scholarship from Her Royal Highness Prin‑cess Maha Chakri Sirindhorn Education Project to Cambodia for 2020,faculty of College of Computing,Khon Kaen University.
文摘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 bat‑tery life. This causes them to face various vulnerabilities and potential attacks, such as DIO neighbor suppression attacks. This type of attack specifcally targets neighboring nodes through DIO messages and poses a signifcant 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 consecu‑tively: (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 efectiveness 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.
