Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works

Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works

LightGBM: A Highly Efficient Gradient Boosting Decision Tree

(2017) · HAL · Cited by 10,512 · see citing works

A detailed analysis of the KDD CUP 99 data set

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396 · see citing works

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018) · Proceedings of the 4th International Conference on Information Systems Security and Privacy · doi:10.5220/0006639801080116 · Cited by 5,318 · cited 1× · see citing works

UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works

Gate-variants of Gated Recurrent Unit (GRU) neural networks

Dey, R., & Salem, F. M. (2017) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas.2017.8053243 · Cited by 2,430 · see citing works

A comparison of random forest variable selection methods for classification prediction modeling

Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.028 · Cited by 1,561 · see citing works

Characterization of Encrypted and VPN Traffic using Time-related Features

Draper-Gil, G., Lashkari, A. H., Mamun, M. S. I., & A. Ghorbani, A. (2016) · Proceedings of the 2nd International Conference on Information Systems Security and Privacy · doi:10.5220/0005740700001242 · Cited by 1,213 · see citing works

Malware traffic classification using convolutional neural network for representation learning

Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017) · 2017 International Conference on Information Networking (ICOIN) · doi:10.1109/icoin.2017.7899588 · Cited by 1,177 · see citing works

An empirical comparison of botnet detection methods

García, S., Grill, M., Stiborek, J., & Zunino, A. (2014) · Computers & Security · doi:10.1016/j.cose.2014.05.011 · Cited by 962 · see citing works

CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope

Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021) · Electronics · doi:10.3390/electronics10202470 · Cited by 890 · see citing works

Characterization of Tor Traffic using Time based Features

Lashkari, A. H., Gil, G. D., Mamun, M., & Ghorbani, A. A. (2017) · International Conference on Information Systems Security and Privacy (ICISSP) · doi:10.5220/0006105602530262 · Cited by 874 · see citing works

A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Bansal, M., Goyal, A., & Choudhary, A. (2022) · Decision Analytics Journal · doi:10.1016/j.dajour.2022.100071 · Cited by 852 · see citing works

Research on K-Value Selection Method of K-Means Clustering Algorithm

Yuan, C., & Yang, H. (2019) · J — Multidisciplinary Scientific Journal · doi:10.3390/j2020016 · Cited by 850 · see citing works

On the Number of Linear Regions of Deep Neural Networks

Montúfar, G., Pascanu, R., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1402.1869 · Cited by 840 · see citing works

FS-Net: A Flow Sequence Network For Encrypted Traffic Classification

Liu, C., He, L., Xiong, G., Cao, Z., & Li, Z. (2019) · IEEE INFOCOM 2019 - IEEE Conference on Computer Communications · doi:10.1109/infocom.2019.8737507 · Cited by 630 · see citing works

AdaBoost-Based Algorithm for Network Intrusion Detection

Hu, W., Hu, W., & Maybank, S. J. (2008) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2007.914695 · Cited by 395 · see citing works

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24412 · Cited by 339 · see citing works

Logistic Regression Model Optimization and Case Analysis

Zou, X., Hu, Y. Q., Tian, Z., & Shen, K. (2019) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt47585.2019.8962457 · Cited by 294 · see citing works

Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic

MontazeriShatoori, M., Davidson, L., Kaur, G., & Lashkari, A. H. (2020) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00026 · Cited by 265 · see citing works

$Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework

Zeng, Y., Gu, H., Wei, W., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2908225 · Cited by 261 · see citing works

Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study

Wang, Z., Fok, K. W., & Thing, V. L. L. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102542 · Cited by 162 · see citing works

A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015Preprint

Özgür, A., & Erdem, H. (2016) · doi:10.7287/peerj.preprints.1954v1 · Cited by 146 · see citing works

Naïve Bayes Classifier Models for Predicting the Colon Cancer

Salmi, N., & Rustam, Z. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/546/5/052068 · Cited by 119 · see citing works

EntropLyzer: Android Malware Classification and Characterization Using Entropy Analysis of Dynamic Characteristics

Keyes, D. S., Li, B., Kaur, G., Lashkari, A. H., Gagnon, F., & Massicotte, F. (2021) · doi:10.1109/rdaaps48126.2021.9452002 · Cited by 119 · see citing works

A novel permission-based Android malware detection system using feature selection based on linear regression

Şahi̇n, D. Ö., Kural, O. E., Akleylek, S., & Kılıç, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05875-1 · Cited by 101 · see citing works

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization

Arpit, D., Wang, H., Zhou, Y., & Xiong, C. (2022) · neural information processing systems · doi:10.52202/068431-0601 · Cited by 90 · see citing works

Encrypted Malware Traffic Detection via Graph-based Network Analysis

Fu, Z., Liu, M., Qin, Y., Zhang, J., Zou, Y., Yin, Q., Li, Q. L., & Duan, H. (2022) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3545948.3545983 · Cited by 87 · see citing works

Distributed real-time SlowDoS attacks detection over encrypted traffic using Artificial Intelligence

García, N. F., Alcañiz, T., González-Vidal, A., Bernabé, J. B., Rivera, D., & Skármeta, A. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102871 · Cited by 70 · see citing works

Graph based encrypted malicious traffic detection with hybrid analysis of multi-view features

Hong, Y., Li, Q., Yang, Y., & Shen, M. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119229 · Cited by 65 · see citing works

Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

Wang, Z., & Thing, V. L. L. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103143 · Cited by 64 · see citing works

GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus

Islam, R., Devnath, M. K., Samad, M. D., & Kadry, S. M. J. A. (2021) · Vehicular Communications · doi:10.1016/j.vehcom.2021.100442 · Cited by 59 · see citing works

ERNN: Error-Resilient RNN for Encrypted Traffic Detection towards Network-Induced Phenomena

Zhao, Z., Li, Z., Jiang, J., Yu, F., Zhang, F., Xu, C., Zhao, X., Zhang, R., & Guo, S. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3242134 · Cited by 56 · see citing works

Detection of Encrypted Malicious Network Traffic using Machine Learning

Lucia, M. J. D., & Cotton, C. (2019) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom47813.2019.9020856 · Cited by 55 · see citing works

Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning

Bakhshi, T., & Ghita, B. (2021) · Security and Communication Networks · doi:10.1155/2021/5363750 · Cited by 55 · see citing works

Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain GeneralizationPreprint

Arpit, D., Wang, H., Zhou, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10832 · Cited by 47 · see citing works

A novel approach based on adaptive online analysis of encrypted traffic for identifying Malware in IIoT

Niu, Z., Xue, J., Qu, D., Wang, Y., Zheng, J., & Zhu, H. (2022) · Information Sciences · doi:10.1016/j.ins.2022.04.018 · Cited by 43 · see citing works

MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification

Bader, O., Lichy, A., Hajaj, C., Dubin, R., & Dvir, A. Z. (2022) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc49033.2022.9700625 · Cited by 41 · see citing works

THS-IDPC: A three-stage hierarchical sampling method based on improved density peaks clustering algorithm for encrypted malicious traffic detection

Chen, L., Gao, S., Liu, B., Lü, Z., & Jiang, Z. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03372-1 · Cited by 38 · see citing works

Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation

Wu, Q.-W., Cao, R., Xia, J., Ni, J., Zheng, C.-H., & Su, Y. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3113122 · Cited by 38 · see citing works

A Distance-Based Method for Building an Encrypted Malware Traffic Identification Framework

Liu, J., Tian, Z., Zheng, R., & Liu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2930717 · Cited by 36 · see citing works

GCN-ETA: High-Efficiency Encrypted Malicious Traffic Detection

Zheng, J., Zeng, Z., & Feng, T. (2022) · Security and Communication Networks · doi:10.1155/2022/4274139 · Cited by 32 · see citing works

Encrypted Malicious Traffic Detection Based on Word2Vec

Ferriyan, A., Thamrin, A. H., Takeda, K., & Murai, J. (2022) · Electronics · doi:10.3390/electronics11050679 · Cited by 28 · see citing works

A deep‐learning‐ and reinforcement‐learning‐based system for encrypted network malicious traffic detection

Yang, J., Liang, G., Li, B., Wen, G., & Gao, T. (2021) · Electronics Letters · doi:10.1049/ell2.12125 · Cited by 26 · see citing works

Detecting Anomalies in Encrypted Traffic via Deep Dictionary Learning

Xing, J., & Wu, C. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162940 · Cited by 25 · see citing works

Detection of DoH Traffic Tunnels Using Deep Learning for Encrypted Traffic Classification

Alzighaibi, A. R. (2023) · Computers · doi:10.3390/computers12030047 · Cited by 23 · see citing works

Deep-Forest-Based Encrypted Malicious Traffic Detection

Zhang, X., Zhao, M., Wang, J., Li, S., Zhou, Y., & Zhu, S. (2022) · Electronics · doi:10.3390/electronics11070977 · Cited by 19 · see citing works

Spatial-Temporal Feature with Dual-Attention Mechanism for Encrypted Malicious Traffic Detection

Liu, J.-Y., Wang, L., Hu, W., Gao, Y., Cao, Y., Lin, B., & Zhang, R. (2023) · Security and Communication Networks · doi:10.1155/2023/7117863 · Cited by 19 · see citing works

Refinements In Zeek Intrusion Detection System

Tiwari, A., Saraswat, S., Dixit, U., & Pandey, S. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785047 · Cited by 16 · see citing works

Transfer Learning for Encrypted Malicious Traffic Detection Based on Efficientnet

Zhang, S., Bu, Y., Chen, B., & Lǚ, X. (2021) · International Conference on Advances in Computer Technology, Information Science and Communications (CTISC) · doi:10.1109/ctisc52352.2021.00021 · Cited by 13 · see citing works

TCMal: A Hybrid Deep Learning Model for Encrypted Malicious Traffic Classification

Li, M., Song, X., Zhao, J., & Cui, B. (2022) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc56324.2022.10065869 · Cited by 10 · see citing works

AS‐DMF: A Lightweight Malware Encrypted Traffic Detection Method Based on Active Learning and Feature Selection

Huo, Y., Zhao, F., Zhang, H., Zhuang, S., & Sun, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1556768 · Cited by 8 · see citing works

Light-weight Unsupervised Anomaly Detection for Encrypted Malware Traffic

Han, S., Wu, Q., Zhang, H., & Qin, B. (2022) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc55868.2022.00034 · Cited by 8 · see citing works

MEMTD: Encrypted Malware Traffic Detection Using Multimodal Deep Learning

Zhang, X., Lu, J., Sun, J., Xiao, R., & Jin, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09917-5_24 · Cited by 7 · see citing works

A Novel Malware Encrypted Traffic Detection Framework Based On Ensemble Learning

Zhao, C., Li, S., Wu, X., Han, W., Tian, Z., & Chen, M. (2021) · 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC) · doi:10.1109/dsc53577.2021.00097 · Cited by 6 · see citing works

A Few-Shot Malicious Encrypted Traffic Detection Approach Based on Model-Agnostic Meta-Learning

Wang, Z., Li, M., Ou, H., Pang, S., & Yue, Z. (2023) · Security and Communication Networks · doi:10.1155/2023/3629831 · Cited by 6 · see citing works

Deep Learning Detection Method of Encrypted Malicious Traffic for Power Grid

Chen, L., Jiang, Y., Kuang, X., & Xu, A. (2020) · IEEE International Conference on Energy Internet (ICEI) · doi:10.1109/icei49372.2020.00024 · Cited by 4 · see citing works

A Weakly-supervised Method for Encrypted Malicious Traffic Detection

Liu, J., Li, Z., Wang, J., Yan, T., An, D., Zhou, C., & Chen, G. (2022) · doi:10.22323/1.415.0027 · Cited by 4 · see citing works

Malicious Encrypted Network Traffic Detection Using Deep Auto-Encoder with a Custom Reconstruction Loss

Bahlali, A. R., Bachir, A., & Cheriet, A. (2023) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc58260.2023.10323710 · Cited by 4 · see citing works

Adversarial Malicious Encrypted Traffic Detection Based on Refined Session Analysis

Li, M., Wu, Z., Chen, K., & Wang, W. (2022) · Symmetry · doi:10.3390/sym14112329 · Cited by 2 · see citing works

A Mining Policy based Malicious Encrypted Traffic Detection Scheme

Chao, D. (2020) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3436369.3436479 · Cited by 1 · see citing works

An anomaly detection method of encrypted traffic based on user behavior

Gong, X., Zhao, X., Xin, H., Gu, L., Meng, Y., Xin, M., Dong, C., Xiaorong, D., Haichuan, S., & Wang, L. (2021) · International Conference on Control and Intelligent Robotics (ICCIR) · doi:10.1145/3473714.3473724 · Cited by 1 · see citing works

Real time malware detection in encrypted network traffic using machine learning with time based features

Singh, A. P., & Singh, M. (2023) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.47974/jdmsc-1760 · Cited by 1 · see citing works

Web Traffic Anomaly Detection Using Isolation Forest

Chua, W., Pajas, A. L. D., Castro, C. S., Panganiban, S. P., Pasuquin, A. J., Purganan, M. J., Malupeng, R., Pingad, D. J., Orolfo, J. P., Lua, H. H., et al. (2024) · Informatics · doi:10.3390/informatics11040083 · Cited by 49 · see citing works

Artificial Intelligence in Maritime Cybersecurity: A Systematic Review of AI-Driven Threat Detection and Risk Mitigation Strategies

Miller, T. I., Durlik, I., Kostecka, E., Sokołowska, S., Kozlovska, P., & Zwolak, R. (2025) · Electronics · doi:10.3390/electronics14091844 · Cited by 32 · see citing works

Exploring Encryption Algorithms and Network Protocols: A Comprehensive Survey of Threats and Vulnerabilities

Jemin, A., Hussain, R., Kang, K., & Son, J. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3526605 · Cited by 29 · see citing works

A comprehensive survey on intrusion detection in internet of medical things: Datasets, federated learning, blockchain, and future research directions

Hassan, S. R., Tanveer, M. U., Prajapat, S., & Shabaz, M. (2025) · ICT Express · doi:10.1016/j.icte.2025.11.005 · Cited by 30 · see citing works

The Iceberg Model for Integrated Aircraft Health Monitoring Based on AI, Blockchain, and Data Analytics

Kabashkin, I. (2024) · Electronics · doi:10.3390/electronics13193822 · Cited by 21 · see citing works

A comprehensive survey on intrusion detection algorithms

Li, Y., Li, Z., & Li, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109863 · Cited by 20 · see citing works

Deep Learning Approach for Protocol Anomaly Detection Using Status Code Sequences

Zhang, C., Zhu, H., Zhu, A., Liao, J., Xiao, Y., & Zhang, Z. (2026) · doi:10.1145/3813808.3813810 · Cited by 19 · see citing works

Generative AI in Network Security and Intrusion Detection

Sindiramutty, S. R., Prabagaran, K. R. V., Jhanjhi, N. Z., Murugesan, R. K., Brohi, S. N., & Masud, M. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5415-5.ch003 · Cited by 18 · see citing works

UNSW HomeNet: A network traffic flow dataset for AI-based smart home device classification

Rahman, M. M., Bouhafs, F., Hoseini, S. A., & den Hartog, F. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111041 · Cited by 17 · see citing works

The role of artificial intelligence (AI) in shaping data privacy

Lami, B., Hussein, S. M., Rajamanickam, R., & Kaka, G. E. (2024) · International Journal of Law and Management · doi:10.1108/ijlma-07-2024-0242 · Cited by 17 · see citing works

Semi-Supervised Encrypted Malicious Traffic Detection Based on Multimodal Traffic Characteristics

Liu, M., Yang, Q., Wang, W., & Liu, S. (2024) · Sensors · doi:10.3390/s24206507 · Cited by 16 · see citing works

Diagnosis to dissection: AI’s role in early detection and surgical intervention for gastric cancer

Pattilachan, T. M., Christodoulou, M., & Ross, S. B. (2024) · Journal of Robotic Surgery · doi:10.1007/s11701-024-02005-6 · Cited by 15 · see citing works

Artificial Intelligence and Deep Learning in Sensors and Applications

Yuan, S., Hong, Z., & Cheng, W. K. (2024) · Sensors · doi:10.3390/s24103258 · Cited by 14 · see citing works

Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities

Li, Z., Wang, J., Zhao, S., Wang, Q., & Wang, Y. (2025) · Applied Sciences · doi:10.3390/app15062920 · Cited by 13 · see citing works

AI-Based Embedded Framework for Cyber-Attack Detection Through Signal Processing and Anomaly Analysis

Drăguşin, S., Boştinaru, R.-N., Bizon, N., & Iana, G. (2026) · Applied Sciences · doi:10.3390/app16031416 · Cited by 12 · see citing works

AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review

Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works

AI ‐Driven Traffic Flow Prediction and Anomaly Detection in Smart Cities: A Multi‐Agent Approach

Shabaz, M., & Raju, K. N. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70279 · Cited by 8 · see citing works

Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution

Duarte, J. D., Chagas, P., da Costa, J. P. C. L., da Costa, E. J., de Melo, L. P., Nunes, R. R., Soares, C. V. N. G., & Silva, T. E. D. C. (2025) · IEEE Access · doi:10.1109/access.2025.3599454 · Cited by 7 · see citing works

Unified AI Framework for Real-Time Customer Churn Prediction Using Behavioral and Event-Log Anomaly Signals

Shah, J. K., Patel, N., Devi, D. U., Iragala, M. B., Kunkala, A. K., & Chouhan, B. (2026) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic67076.2026.11395834 · Cited by 7 · see citing works

A Hybrid Deep Learning-Based Architecture for Network Traffic Anomaly Detection via EFMS-Enhanced KMeans Clustering and CNN-GRU Models

Yagual, D. Q., Fernández, D., & Novoa, F. J. (2025) · Applied Sciences · doi:10.3390/app152010889 · Cited by 6 · see citing works

DLET-Classifier: A Dynamic and Lightweight Method for Encrypted Traffic Classification

Wu, J., Niu, W., Wei, F., Li, S., Huang, S., Gong, J., & Zhang, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3593143 · Cited by 5 · see citing works

The Impact of Artificial Intelligence in Detecting Manipulations in Financial Statements

Jaafar, M. A. (2025) · Futurity Economics&Law · doi:10.57125/fel.2025.06.25.03 · Cited by 4 · see citing works

Improving VPN Security Using FFNN-Based Cybersecurity Models

Al-Doori, A. S. B., Albonda, H. D. R., Abbood, Z. A., & Ahmed, S. R. (2026) · International Conference on Computing and Emerging Sciences · doi:10.1145/3797491.3797510 · Cited by 4 · see citing works

Machine Learning-Powered Malware Detection in Encrypted IoT Traffic

Farhad, A., Khan, M. I., Sodhro, A. H., Ehsan, M. K., & Djebbar, F. (2025) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2025-spring65109.2025.11174383 · Cited by 4 · see citing works

Synthetic Minority Over-sampling Technique for detecting Malicious Traffic targeting Internet of Things' devices

Duarte, J. D., Bispo, G. D., Rodrigues, G. A. P., Serrano, A. L. M., Saiki, G. M., & Gonçalves, V. P. (2025) · Journal of Internet Services and Applications · doi:10.5753/jisa.2025.5251 · Cited by 4 · see citing works

AI-Powered Data Encryption Techniques: Safeguarding Cloud Infrastructure

Patel, A., Ragothaman, H., Shah, M., Sheth, A., Upadhyay, C., Pandey, P., & Udayakumar, S. K. (2025) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai64502.2025.00153 · Cited by 4 · see citing works

Novel way to predict stock movements using multiple models and comprehensive analysis: leveraging voting meta-ensemble techniques

Kayit, A. D., & Tahir, M. I. (2024) · Journal of the Nigerian Society of Physical Sciences · doi:10.46481/jnsps.2024.2039 · Cited by 3 · see citing works

A Multi-Feature Semantic Fusion Machine Learning Architecture for Detecting Encrypted Malicious Traffic

Tang, S., Du, F., Diao, Z., & Fan, W. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5030047 · Cited by 3 · see citing works

Securing Academic Library VPN Access with AI-Powered Anomaly Detection Models

S.Balachandran, & C.Jayakumar (2025) · International Conference on Soft Computing for Security Applications (ICSCSA) · doi:10.1109/icscsa66339.2025.11171070 · Cited by 2 · see citing works

History covert channels with relative timing pointers: Design, error resilience, and detectability

Weissenborn, C., & Wendzel, S. (2026) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2026.104431 · Cited by 2 · see citing works

TLS-Aware Anomaly Detection for Encrypted IoT Traffic Using a β-Variational Autoencoder with ANOVA–Mutual Information Feature Selection

Nouman, M., Ujjan, R. M. A., & Saleh, M. H. (2026) · Future Internet · doi:10.3390/fi18060310 · Cited by 2 · see citing works

AI Driven Cloud Security and Anomaly Detection in Saudi ArabiaPreprint

Kayani, M. A. (2025) · doi:10.14293/pr2199.002087.v2 · Cited by 2 · see citing works

AI-Enhanced Anomaly Detection and Automated Root-Cause Analysis for Enterprise Data Platforms

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