Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking

Feng, Bohao; Tian, Aleteng; Yu, Shui; Li, Jianhua; Zhou, Huachun; Zhang, Hongke

doi:10.1109/jiot.2022.3163776

Cited by 37 publications

(10 citation statements)

References 26 publications

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“…At present, the existing detection methods for DDoS attacks are mainly based on ML or DL. Build an intrusion detection classifier by selecting an appropriate ML or DL model [ 14 , 15 , 16 , 17 ] and analyze the difference in features between normal flow data and abnormal flow of the network, so as to judge the type of attack. Jing et al [ 18 ] investigated the existing DDoS-related data and detection methods of DDoS and worm attacks, divided the datasets into packet level datasets and flow level datasets, and introduced the detection technologies from application layer DDoS, network layer DDoS, LDDoS and botnet DDoS.…”

Section: Related Workmentioning

confidence: 99%

Trusted Multi-Domain DDoS Detection Based on Federated Learning

Yin

Hong-jun

2022

Sensors

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Aiming at the problems of single detection target of existing distributed denial of service (DDoS) attacks, incomplete detection datasets and privacy caused by shared datasets, we propose a trusted multi-domain DDoS detection method based on federated learning. Firstly, we divide the types of DDoS attacks into different sub-attacks, design the federated learning dataset for DDoS detection in each domain, and use them to realize a more comprehensive detection method of DDoS attacks on the premise of protecting the data privacy of each domain. Secondly, in order to improve the robustness of federated learning and alleviate poisoning attack, we propose a reputation evaluation method based on blockchain, which estimates interaction reputation, data reputation and resource reputation of each participant comprehensively, so as to obtain the trusted federated learning participants and identify the malicious participants. In addition, we also propose a combination scheme of multi-domain detection and distributed knowledge base and design a feature graph of malicious behavior based on a knowledge graph to realize the memory of multi-domain feature knowledge. The experimental results show that the accuracy of most categories of the multi-domain DDoS detection method can reach more than 95% with the protection of datasets, and the reputation evaluation method proposed in this paper has a higher ability to identify malicious participants against the data poisoning attack when the threshold is set to 0.6.

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Section: Related Workmentioning

confidence: 99%

Trusted Multi-Domain DDoS Detection Based on Federated Learning

Yin

Hong-jun

2022

Sensors

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“…Anomaly detection has been widely used in various fields, including cyber security [ 13 , 14 ], communications security [ 15 , 16 , 17 ], IoT [ 18 , 19 , 20 ], video surveillance [ 21 , 22 ], etc. In general, anomaly detection refers to finding special instances that differ from given normal instances.…”

Section: Introductionmentioning

confidence: 99%

“…Under these circumstances, it is more appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. Anomaly detection has been widely used in various fields, including cyber security [13,14], communications security [15][16][17], IoT [18][19][20], video surveillance [21,22], etc. In general, anomaly detection refers to finding special instances that differ from given normal instances.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention-Based Industrial Surface Defect Detection with Consistency Loss

Zheng

Chen

et al. 2022

Sensors

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In industrial production, flaws and defects inevitably appear on surfaces, resulting in unqualified products. Therefore, surface defect detection plays a key role in ensuring industrial product quality and maintaining industrial production lines. However, surface defects on different products have different manifestations, so it is difficult to regard all defective products as being within one category that has common characteristics. Defective products are also often rare in industrial production, making it difficult to collect enough samples. Therefore, it is appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. In this paper, we propose an anomaly detection method that is based on dual attention and consistency loss to accomplish the task of surface defect detection. At the reconstruction stage, we employed both channel attention and pixel attention so that the network could learn more robust normal image reconstruction, which could in turn help to separate images of defects from defect-free images. Moreover, we proposed a consistency loss function that could exploit the differences between the multiple modalities of the images to improve the performance of the anomaly detection. Our experimental results showed that the proposed method could achieve a superior performance compared to the existing anomaly detection-based methods using the Magnetic Tile and MVTec AD datasets.

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“…In recent years, many traditional packetdropping attack detection algorithms have been proposed, but they are not suitable for the emerging resource-constrained IoT networks. For instance, traditional machine learning (ML)-based detection algorithms [6][7][8][9] identify malicious nodes by training detection models. e performance of the detection models depends on the size of the training dataset.…”

Section: Introductionmentioning

confidence: 99%

Detection of Packet Dropping Attack Based on Evidence Fusion in IoT Networks

Ding

Zhai

Liu

et al. 2022

Security and Communication Networks

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Internet of Things (IoT) is widely used in environmental monitoring, smart healthcare, and other fields. Due to its distributed nature, IoT is vulnerable to various internal attacks. One of these attacks is the packet-dropping attack, which is very harmful. The existing packet-dropping attack detection algorithms are unsuitable for emerging resource-constrained IoT networks. For example, ML-based algorithms always inject numerous packets to obtain the training dataset. However, it is heavyweight for energy-limited nodes to forward these extra packets. In this paper, we propose a lightweight evidence fusion-based detection algorithm (EFDA), which leverages the packet forwarding evidence to identify malicious nodes. Firstly, EFDA finds the sequence numbers of dropped packets and their corresponding source nodes. Then, it traces the routing path of each dropped packet and collects evidence for detection. The evidence stored by nodes around the path record the node’s forwarding behaviors. Finally, the collected evidence is fused to evaluate the trust of nodes. Based on nodes’ trust, the K-means clustering is used to distinguish between malicious nodes and benign nodes. We conduct simulation experiments to compare EFDA with ML-based algorithms. The experimental results demonstrate that EFDA can detect the packet-dropping attack without injecting packets and achieve a higher detection accuracy.

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Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking

Cited by 37 publications

References 26 publications

Trusted Multi-Domain DDoS Detection Based on Federated Learning

Trusted Multi-Domain DDoS Detection Based on Federated Learning

Dual Attention-Based Industrial Surface Defect Detection with Consistency Loss

Detection of Packet Dropping Attack Based on Evidence Fusion in IoT Networks

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