LLM-AE-MP: Web Attack Detection Using a Large Language Model with Autoencoder and Multilayer Perceptron
Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows...
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| Vydané v: | Expert systems with applications Ročník 274; s. 126982 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.05.2025
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| ISSN: | 0957-4174 |
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| Abstract | Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic. The AE framework is combined with a transductive long short-term memory (TLSTM) network, which is trained with an advanced generative adversarial network (GAN). Using GAN promotes an adaptive learning environment, significantly boosting the robustness and generalizability of our method against evolving web attack vectors. TLSTM uses transductive learning to focus on data points near the test set, improving the adaptability of the model to outperform traditional LSTM models. In our GAN, the generator purposely excludes gradients from the most influential batch elements, improving the ability of the model to generate diverse and generalized outputs. After training the AE, its latent representations are passed to a multilayer perceptron (MLP) for detection tasks. To address the imbalanced classification in MLP, we use a reinforcement learning (RL) strategy. The RL approach strategically adjusts incentives, enhancing the performance of the model in identifying less frequent but critical malicious instances, thereby supporting a balanced security assessment. Our evaluations using the CSIC 2010 (Spanish National Research Council 2010), FWAF (web application firewall), and HttpParams datasets show that our method outperforms existing techniques, achieving (Accuracy, F-measure, geometric mean (G-means), and area under the curve (AUC)) reaching (90.937%, 89.755%, 88.446%, 0.838), (89.055, 90.663%, 88.334%, 0.847) and (92.242%, 93.774%, 91.356%, 0.897), respectively. Moreover, our model achieves efficient runtime and memory usage across the datasets, providing a practical solution for real-time web attack detection. These results confirm the effectiveness of the model in security contexts, representing a substantial advancement in web attack detection and the improvement of investigative strategies. |
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| AbstractList | Web applications store sensitive data, making them prime targets for cybercriminals and posing national security risks. This study introduces a new approach to distinguishing legitimate and malicious hypertext transfer protocol (HTTP) requests using an autoencoder (AE). The integration of AE allows for efficient feature distillation, enhancing the sensitivity of the model to anomalies in HTTP traffic. The AE framework is combined with a transductive long short-term memory (TLSTM) network, which is trained with an advanced generative adversarial network (GAN). Using GAN promotes an adaptive learning environment, significantly boosting the robustness and generalizability of our method against evolving web attack vectors. TLSTM uses transductive learning to focus on data points near the test set, improving the adaptability of the model to outperform traditional LSTM models. In our GAN, the generator purposely excludes gradients from the most influential batch elements, improving the ability of the model to generate diverse and generalized outputs. After training the AE, its latent representations are passed to a multilayer perceptron (MLP) for detection tasks. To address the imbalanced classification in MLP, we use a reinforcement learning (RL) strategy. The RL approach strategically adjusts incentives, enhancing the performance of the model in identifying less frequent but critical malicious instances, thereby supporting a balanced security assessment. Our evaluations using the CSIC 2010 (Spanish National Research Council 2010), FWAF (web application firewall), and HttpParams datasets show that our method outperforms existing techniques, achieving (Accuracy, F-measure, geometric mean (G-means), and area under the curve (AUC)) reaching (90.937%, 89.755%, 88.446%, 0.838), (89.055, 90.663%, 88.334%, 0.847) and (92.242%, 93.774%, 91.356%, 0.897), respectively. Moreover, our model achieves efficient runtime and memory usage across the datasets, providing a practical solution for real-time web attack detection. These results confirm the effectiveness of the model in security contexts, representing a substantial advancement in web attack detection and the improvement of investigative strategies. |
| ArticleNumber | 126982 |
| Author | Prajapat, Sunil Bourouis, Sami Xue, Haozhong Yuan, Yuping Yang, Jing Wu, Yuangui Abdel-Salam, Mahmoud Por, Lip Yee |
| Author_xml | – sequence: 1 givenname: Jing surname: Yang fullname: Yang, Jing email: s2147529@siswa.um.edu.my organization: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 2 givenname: Yuangui surname: Wu fullname: Wu, Yuangui email: wuyuangui@meedog.com organization: School of Software, Jilin University, Jilin Province 130012, China – sequence: 3 givenname: Yuping surname: Yuan fullname: Yuan, Yuping email: Xiao-yin-Shawn.wang@dis.tech organization: Information and Network Institute, Radio, Film and Television Design and Research Institute Co., Ltd, Beijing 100045, China – sequence: 4 givenname: Haozhong surname: Xue fullname: Xue, Haozhong email: hx2341@nyu.edu organization: Tandon School of Engineering, New York University, New York 11101, USA – sequence: 5 givenname: Sami surname: Bourouis fullname: Bourouis, Sami email: s.bourouis@tu.edu.sa organization: Department of Information Technology , College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia – sequence: 6 givenname: Mahmoud surname: Abdel-Salam fullname: Abdel-Salam, Mahmoud email: mahmoud20@mans.edu.eg organization: Faculty of Computer and Information Science, Mansoura University, Mansoura 35516, Egypt – sequence: 7 givenname: Sunil surname: Prajapat fullname: Prajapat, Sunil email: cuhp21rdmath13@hpcu.ac.in organization: Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamshala 176215, India – sequence: 8 givenname: Lip Yee surname: Por fullname: Por, Lip Yee email: porlip@um.edu.my organization: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia |
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| Keywords | Long short-term memory Anomalous request Web attack Autoencoder Generative adversarial network Reinforcement learning |
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