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Machine learning approaches to intrusion detection in unmanned aerial vehicles (UAVs)

Neural Computing and Applications 2024年第29期36卷 18009-18041页

作者： AL-Syouf, Raghad A. Bani-Hani, Raed M. AL-Jarrah, Omar Y. Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

Unmanned Aerial Vehicles (UAVs) have been gaining popularity in various commercial, civilian, and military applications due to their efficiency and cost-effectiveness. However, the increasing demand for UAVs makes them vulnerable to various cyberattacks/intrusions that could have devastating consequences at an individual, organizational, and national level. To mitigate this, prompt detection of such threats is crucial in order to prevent potential damage and ensure safe and secure operations. In this work, we provide an overview of UAV systems’ architecture, security, and privacy requirements. We then analyze potential threats to UAVs, providing an evaluation of countermeasures for UAV-based attacks. We also present a comprehensive and timely exploration of state-of-the-art UAV Intrusion Detection Systems (IDSs), specifically focusing on Machine Learning (ML)-based approaches. We look at the increasing importance of using ML for detecting intrusions in UAVs, which have gained significant attention from both academia and industry. This study also takes a step forward by pointing out and classifying contemporary IDSs based on their detection methods, feature selection techniques, evaluation datasets, and performance metrics. By evaluating existing research, we aim to provide more insight into the issues and limitations of current UAV IDSs. Additionally, we identify research gaps and challenges while suggesting potential future research directions in this domain. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Intrusion detection

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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

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Computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of Information Engineering Institute of Disaster PreventionLangfang065201China Langfang Key Laboratory of Network Emergency Protection and Network Security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1139-1156页

作者： Kaili Qi Minqing Zhang Fuqiang Di Chao Jiang Key Laboratory of Network & Information Security under PAP Engineering University of PAPXi'an 710086China

Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding ***,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing ***,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding *** the data hiding phase,each encrypted image is sent to a data hider to embed the secret information *** r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be *** evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and *** last but the most important,it has higher embedding *** 512 ×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.

关键词： adaptive most significant bit(MSB)prediction password feedback secret sharing high embedding capacity reversible data hiding in encrypted images

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MarkNeRF:Watermarking for Neural Radiance Field

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Computers, Materials & Continua 2024年第7期80卷 1235-1250页

作者： Lifeng Chen Jia Liu Wenquan Sun Weina Dong Xiaozhong Pan Cryptographic Engineering Department Institute of Cryptographic EngineeringEngineering University of PAPXi’an710086China Key Laboratory of Network and Information Security of PAP Xi’an710086China

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)*** employ an embedding network to integrate the watermark into the images within the training ***,theNeRFmodel is utilized for *** copyright verification,a secret image is generated by inputting a confidential viewpoint into *** this basis,design an extraction network to extract embedded watermark images fromconfidential *** the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s *** experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

关键词： Neural radiation field 3D watermark robustness black box

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Generic attacks on small-state stream cipher constructions in the multi-user setting

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Cybersecurity 2024年第3期7卷 69-79页

作者： Jianfu Huang Ye Luo Qinggan Fu Yincen Chen Chao Wang Ling Song College of Cyber Security Jinan UniversityGuangzhou 510632China National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhou 510632China

Small-state stream ciphers(SSCs),which violate the principle that the state size should exceed the key size by a factor of two,still demonstrate robust security properties while maintaining a lightweight *** ciphers can be clas-sifed into several constructions and their basic security requirement is to resist generic attacks,ie.,the time-mem-ory-data tradeoff(TMDTO)*** this paper,we investigate the security of small-state constructions in the multi-user *** on it,the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a *** is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing ***,SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length,no matter whether the keystream length is limited or ***,We apply these two generic attacks to TinyJAMBU and DRACO in the multi-user *** TMDTO distinguish-ing attack on TinyJAMBU with a 128-bit key can be mounted with time,memory,and data complexities of 264,248,and 232,*** attack is comparable with a recent work on ToSC 2022,where partial key bits of TinyJAMBU are recovered with more than 250 users(or keys).As DRACO's IV length is smaller than its key length,itis vulnerable to the TMDTO key recovery *** resulting attack has a time and memory complexity of both 2112,which means DRACO does not provide 128-bit security in the multi-user setting.

关键词： Small-state stream ciphers TMDTO attacks Multi-user setting

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Method and Practice of Trusted Embedded Computing and Data Transmission Protection Architecture Based on Android

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Chinese Journal of Electronics 2024年第3期33卷 623-634页

作者： Yichuan WANG Wen GAO Xinhong HEI Yanning DU School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In recent years,the rapid development of Internet technology has constantly enriched people's daily life and gradually changed from the traditional computer terminal to the mobile *** with it comes the security problems brought by the mobile *** for Android system,due to its open source nature,malicious applications continue to emerge,which greatly threatens the data security of ***,this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and *** conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption,storage overhead and *** experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process *** with the native system,the additional performance overhead is small.

关键词： TrustZone Static measurement Data leakage Android

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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional networks

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Computers, Materials & Continua 2025年第2期82卷 3179-3201页

作者： Baoquan Liu Xi Chen Qingjun Yuan Degang Li Chunxiang Gu School of Cyberspace Security Zhengzhou UniversityZhengzhou450002China School of Cyberspace Security Information Engineering UniversityZhengzhou450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001China

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

关键词： Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks

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F3l:an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android

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Cybersecurity 2025年第1期8卷 45-60页

作者： Keya Xu Guang Cheng School of Cyber Science and Engineering Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Security for Ubiquitous Network NanjingChina Purple Mountain Laboratories NanjingChina

Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network *** learning and deep learning have become mainstream methods to analyze traffic,and labeled dataset construction is the *** occupies a huge share of the mobile operating system *** Messaging(IM)applications are important tools for people *** such applications have complex functions which frequently switched,so it is difficult to obtain function-level *** existing function-level public datasets in Android are rare and noisy,leading to research *** labeled samples are collected with WLAN devices,which cannot exclude the operating system background *** the same time,other datasets need to obtain root permission or use scripts to simulate user *** collecting methods either destroy the security of the mobile device or ignore the real operation features of users with *** work(Chen et *** Appl Sci 12(22):11731,2022)proposed a one-stop automated encrypted traffic labeled sample collection,construction,and correlation system,A3C,running at the application-level in *** paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android,*** supplementary method to A3C monitors UI controls and layouts of the Android system in the *** selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app *** deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding *** show that the method can match the correct label within 3 s after the user operation.

关键词： Encrypted traffic Deep learning Android Labeled dataset

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： Data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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Multi-class Bitcoin mixing service identification based on graph classification

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Digital Communications and networks 2024年第6期10卷 1881-1893页

作者： Xiaoyan Hu Meiqun Gui Guang Cheng Ruidong Li Hua Wu School of Cyber Science&Engineering Southeast UniversityNanjing 211189China Purple Mountain Laboratories for Network and Communication Security Nanjing 211111China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing 211189China Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationChina Research Base of International Cyberspace Governance Southeast UniversityNanjing 211189China Institute of Science and Engineering Kanazawa UniversityKakumaKanazawa920-1192Japan

Due to its anonymity and decentralization,Bitcoin has long been a haven for various illegal *** generally legalize illicit funds by Bitcoin mixing ***,it is critical to investigate the mixing services in cryptocurrency anti-money *** studies treat different mixing services as a class of suspicious Bitcoin ***,they are limited by relying on expert experience or needing to deal with large-scale *** far,multi-class mixing service identification has not been explored *** is challenging since mixing services share a similar procedure,presenting no sharp ***,mixing service identification facilitates the healthy development of Bitcoin,supports financial forensics for cryptocurrency regulation and legislation,and provides technical means for fine-grained blockchain *** paper aims to achieve multi-class Bitcoin Mixing Service Identification with a Graph Classification(BMSI-GC)***,BMSI-GC constructs 2-hop ego networks(2-egonets)of mixing services based on their historical ***,it applies graph2vec,a graph classification model mainly used to calculate the similarity between graphs,to automatically extract address features from the constructed ***,it trains a multilayer perceptron classifier to perform classification based on the extracted ***-GC is flexible without handling the full-size network and handcrafting address ***,the differences in transaction patterns of mixing services reflected in the 2-egonets provide adequate information for *** experimental study demonstrates that BMSI-GC performs excellently in multi-class Bitcoin mixing service identification,achieving an average identification F1-score of 95.08%.

关键词： Bitcoin Anti-money laundering Mixing service Ego network Graph classification

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