Jun He

Dr. Jun He


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 July 2024 in Drones
Verified
Journal Article
Open access
PDF file
涛夏涛 夏
涛 夏
MWMenglin Wang
Menglin Wang
JHJun He
Jun He
GYGang Yang
Gang Yang
LFLinna Fan
Linna Fan
GWGuoheng Wei
Guoheng Wei
Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.

3 July 2023 in Electronics
Verified
Journal Article
Open access
PDF file
涛夏涛 夏
涛 夏
MWMenglin Wang
Menglin Wang
JHJun He
Jun He
SLShaofeng Lin
Shaofeng Lin
YSYongqi Shi
Yongqi Shi
LGLiyuan Guo
Liyuan Guo
Unmanned aerial vehicles (UAV) play a vital role in many fields, such as agricultural planting, security patrol, emergency rescue, and so on. The development and implementation of these devices have become vital in terms of reachability and usability. Unfortunately, as drones become more widely used in various fields, they become more and more vulnerable to attacks and security threats, including, but not limited to, eavesdropping, man-in-the-middle attacks, and known session key attacks. In order to deal with these attacks and security threats and meet the needs of lightweight UAV communication, a secure and efficient authentication scheme is essential. To meet the security and lightweight requirements of an identity authentication scheme in a UAV communication network, this paper proposes an identity authentication scheme sdronelig based on an elliptic curve cryptosystem. The scheme realizes the mutual authentication and session key agreement configuration between the UAV and the ground station, and the authentication and key agreement between the UAVs can be realized with the help of the control station. The sdronelig authentication scheme is based on the ECDH key exchange protocol in the elliptic curve cryptography algorithm and adopts the MAC message authentication code technology and the method of pre-calculating part of the process. Under the premise of ensuring the security of the UAV communication network, the authentication efficiency is improved, the communication overhead and communication times are reduced, and the lightweight requirement of the UAV authentication scheme is met. Additionally, a formal verification tool is used to verify the security of the sdronelig scheme under the Dolev-Yao threat model, which is suitable for UAV networks. Finally, a detailed comparative study was conducted on security features, communication overhead, the number of communications, and computational overhead. The results show that the proposed sdronelig authentication scheme not only provides superior security features but also has better or comparable overhead compared to other existing authentication schemes.

28 October 2024 in IEEE Transactions on Mobile Computing
Verified
Journal Article
Closed access
LFLinna Fan
Linna Fan
BWBo Wu
Bo Wu
XSXuan Shen
Xuan Shen
JHJun He
Jun He
GSGuanglei Song
Guanglei Song
YHYongfeng HUANG
Yongfeng HUANG
IoT device identification is vital for network asset and security management. However, existing methods use statistical features that can not identify IoT devices accurately in complex network environments. GraphIoT proposes using non-statistical features and building a heterogeneous graph neural network to identify IoT devices accurately. However, heterogeneous graph neural networks lack interpretability, which reduces trust in the model. Besides, it is difficult to deploy on resource-constrained devices, limiting the broad application of IoT device identification. To make IoT device identification interpretable, easy to deploy, and with high accuracy, we get the interpretation results of GraphIoT through interpretability and further build the rule set based on the interpretation results. Considering there is no suitable interpreter for GraphIoT with many nodes and edges, we propose HGExplainer , which reduces the time complexity by splitting the interpretation target into important relation solving and edge solving and uses a novel solution method, ExpandTree. Then, we also designed a rule extractor, which can build rule sets based on the interpretation results. Experimental results on Yourthings and UNSW datasets show that HGExplainer can build high fidelity, concise sample-level explanations in less than 3 seconds, and the established rule set can precisely identify IoT devices.

1 June 2021 in Security and Communication Networks
Verified
Journal Article
Open access
PDF file
XSXuan Shen
Xuan Shen
LCLei Cheng
Lei Cheng
BSBing Sun
Bing Sun
JHJun He
Jun He
Impossible differential attack is one of the most effective cryptanalytic methods for block ciphers. Its key step is to construct impossible differential distinguishers as long as possible. In this paper, we mainly focus on constructing longer impossible differential distinguishers for two kinds of generalized Feistel structures which are -dataline CAST256-like and MARS-like structures. When their round function takes Substitution Permutation and Substitution Permutation Substitution types, they are called and , respectively. For , the best known result for the length of the impossible differential distinguisher was rounds, respectively. With the help of the linear layer , we can construct - round impossible differential distinguishers, where and are non-negative numbers if satisfies some restricted conditions. For

9 January 2025 in Drones
Verified
Journal Article
Open access
PDF file
涛夏涛 夏
涛 夏
MWMenglin Wang
Menglin Wang
JHJun He
Jun He
GYGang Yang
Gang Yang
LFLinna Fan
Linna Fan
GWGuoheng Wei
Guoheng Wei
There was an error in the authorship of the original publication

2 May 2026 in Electronics
Verified
Journal Article
Open access
PDF file
XPXiang Peng
Xiang Peng
JHJun He
Jun He
LNLin Ni
Lin Ni
GYGang Yang
Gang Yang
Domain Generation Algorithms (DGAs) pose a persistent threat by enabling malware to dynamically generate numerous command-and-control domains, evading traditional blocklists. While machine learning-based detectors have achieved high accuracy, they operate as statistical pattern matchers and lack the human-like anomaly perception that enables security experts to intuitively recognize unnatural domains. This paper introduces CogNormDGA, a cognitive-driven framework that models normal domain characteristics from a defender’s perspective while also anticipating how attackers might exploit cognitive blind spots. Inspired by dual-process theory, CogNormDGA combines intuitive, pattern-based screening (System 1) with analytical, rule-based evaluation of phonotactic, morphological, and semantic violations (System 2). The cognitive principles of System 1 and System 2 are computationally realized as two distinct pathways: an Attentional Salience Network and a Linguistic Constraint Evaluator, respectively. The framework produces interpretable outputs via attention saliency maps and cognitive violation reports. Extensive experiments on 400,000 domains spanning 33 DGA families demonstrate that CogNormDGA achieves competitive detection performance (F1-score 0.941) while establishing a cognitive-driven detection paradigm that produces human-aligned explanations—a property critical for practical security. It shows promising results on low-entropy and novel DGA families. Human subject studies confirm strong alignment between the model’s internal explanations and expert reasoning. Furthermore, CogNormDGA is particularly effective against low-entropy DGA families that exploit cognitive blind spots. By bridging cognitive science and cybersecurity, our work offers an interpretable and human-aligned approach to threat detection, with promising resilience that requires further validation.