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sql injection detection algorithm based on Bi-LSTM and integrated feature selection

JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-32页

作者： Qin, Qiurong Li, Yueqin Mi, Yajie Shen, Jinhui Wu, Kexin Wang, Zhenzhao Beijing Union Univ Smart City Coll Beijing 100101 Peoples R China

sql injection attacks represent a significant web security threat. However, due to their diversity and variability, existing detection methods often suffer from high false alarm rates and low accuracy. To address these challenges, this paper proposes an efficient and lightweight sql injection detection model, sqlLS, based on a bidirectional long short-term memory network. Initially, the term frequency-inverse document frequency algorithm is employed to convert sql statements into numerical feature vectors, enabling the extraction of key information and enhancing the model's ability to characterize the input data. Subsequently, an integrated feature selection method, GFC, is presented, which combines multiple techniques to improve both the accuracy and robustness of feature selection. Specifically, gradient boosting regression trees are used to evaluate the importance of each feature, identifying those most significant for classification;Fisher score filters out features that can effectively distinguish between sql injections and non-injections based on statistical significance;and the chi-square test further evaluates the relevance of the features with respect to the target label, ensuring that the selected features are highly correlated with sql injection detection. After feature selection, a mixed precision training technique is utilized to reduce memory consumption and enhance training efficiency. To reduce the complexity of the bidirectional long short-term memory model and improve its computational efficiency, this paper introduces a pruning technique that minimizes computational overhead by removing unimportant weight connections, thereby improving the model's operational efficiency. Experimental results demonstrate that the sqlLS model achieves an accuracy of 100%, a low false alarm rate of 0.154%, and significantly shorter running times compared to existing models.

关键词： sql injection TF-IDF algorithm Integrated feature selection Bi-LSTM network

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PROGESI: A PROxy Grammar to Enhance Web Application Firewall for sql injection Prevention

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IEEE ACCESS 2024年 12卷 107689-107703页

作者： Coscia, Antonio Dentamaro, Vincenzo Galantucci, Stefano Maci, Antonio Pirlo, Giuseppe BV TECH SpA Cybersecur Lab I-20123 Milan Italy Univ Bari Aldo Moro Dept Comp Sci I-70125 Bari Italy

A web application is prone to security threats due to its open nature. The security of these platforms is imperative for organizations of all sizes because they store sensitive information. Consequently, exploiting web application vulnerabilities could result in large-scale data breaches and significant brand and financial damages. sql injection (sqli) represents a popular attack vector that malicious actors use to compromise website security. Web application firewalls (WAFs) play a primary role in preventing such malicious attack typologies. In the recent literature, several advances have been proposed in the field of WAF enhancement to prevent sqli exploitation. However, many of them test the effectiveness of a WAF without releasing a patch to fix security flaws if a WAF is bypassed. In other cases, the patch is distributed exclusively according to the syntax specified by the WAF tested. This paper introduces a framework that leverages PROxy Grammar to Enhance web application firewalls for sql injection prevention (PROGESI). The proposed solution can act as an intermediary layer between the targeted web server and the incoming application level requests. Specifically, PROGESI can be used individually or in combination with a WAF and includes a series of rules that patch sqli vulnerabilities exposed by a specific web server. Furthermore, it can identify and mitigate sqli attempts, also when attackers use mutation techniques, since the rules used encompass generalization mechanisms. The experiments performed revealed two strengths of PROGESI: (i) the ability to identify sqli even in the presence of server-side defense mechanisms, which increases as the generalization rate implemented by the rule generation algorithm increases;(ii) impressive detection performance even for low generalization rate values, which is higher than that achieved by competitors using a state-of-the-art sqli dataset.

关键词： Security Firewalls (computing) Structured Query Language Grammar sql injection Prevention and mitigation Symbols Context-free grammar proxy web application firewall

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TPsqli: Test Prioritization for sql injection Vulnerability Detection in Web Applications

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8365页

作者： Yang, Guan-Yan Wang, Farn Gu, You-Zong Teng, Ya-Wen Yeh, Kuo-Hui Ho, Ping-Hsueh Wen, Wei-Ling Natl Taiwan Univ Dept Elect Engn Taipei 106319 Taiwan CyberLink Corp New Taipei 231023 Taiwan Natl Yang Ming Chiao Tung Univ Inst Artificial Intelligence Innovat Hsinchu 300093 Taiwan Natl Dong Hwa Univ Dept Informat Management Hualien 974301 Taiwan

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for sql injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.

关键词： software testing penetration testing automatic testing information security sql injection testing priority

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sql injection attack sample generation based on IE-GAN 22

SQL injection attack sample generation based on IE-GAN

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IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) / BigDataSE Conference / CSE Conference / EUC Conference / ISCI Conference

作者： Xu, Mingdi Xie, Bo Cui, Feng Jin, Chaoyang Wang, Yu Wuhan Inst Digital Engn Dept Comp Environm Wuhan Peoples R China

ISBN: (纸本)9798350381993;9798350382006

The relentless advancement of Generative Adversarial Network (GAN) technology has stimulated research interest in exploiting its unique properties within the realm of network security. In conjunction with the maturity and growth of artificial intelligence, employing AI technology for sql injection detection has evolved into an unalterable trend. This study introduces the application of an Improved Evolutionary Generative Adversarial Network (IE-GAN) for the synthesis of sql injection attacks, thereby facilitating the generation of realistic sql injection attack examples. Given the considerable threat posed by sql injection attacks to the security of web applications, the demand for robust and efficient detection mechanisms has escalated significantly. Earlier research has made considerable progress in incorporating GANs in the field of cybersecurity. Our study builds upon these achievements, utilizing a comprehensive dataset that includes both benign and malicious sql queries. To ensure the efficiency of model training, these data are subjected to intricate preprocessing and vectorization processes, which are discussed in the paper in detail.

关键词： sql injection GAN network intrusion

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SIDNet: A sql injection Detection Network for Enhancing Cybersecurity

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IEEE ACCESS 2024年 12卷 176511-176526页

作者： Muduli, Debendra Shookdeb, Shantanu Zamani, Abu Taha Saxena, Surabhi Kanade, Anuradha Shantanu Parveen, Nikhat Shameem, Mohammad CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 Odisha India Northern Border Univ Dept Comp Sci Ar Ar 91431 Saudi Arabia CHRIST Univ Dept Comp Sci Bengaluru India Dr Vishwanath Karad MIT World Peace Univ Dept Comp Sci & Applicat Pune 411038 India Koneru Lakshmaiah Educ Fdn Deemed Be Univ Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent & Secure Sys Dhahran 31261 Saudi Arabia

sql (Structured Query Language) injection is one of the most prevalent and dangerous forms of cyber-attacks, posing significant threats to database management systems and the overall security of web applications. By exploiting vulnerabilities in web applications, attackers can execute malicious sql statements, potentially compromising the integrity and confidentiality of critical data. To combat these threats, in this study, we introduce two novel CNN models, SIDNet-1 (sql injection-attack Detection Network-1) and SIDNet-2 (sql injection-attack Detection Network-2), specifically designed for the classification of sql injection attacks to bolster web application security. Our comprehensive evaluation includes a comparison of the performance of these customized CNN models against traditional machine learning approaches, highlighting improvements in classification accuracy and reductions in false alarm rates. The proposed models have been experimented with two publicly available dataset sqlI (sql-injection) and sqlV2 (sql-injection version2). Specifically, SIDNet-1 achieves an impressive accuracy of 98.02% on the sqlI dataset, while SIDNet-2 closely follows with 97.54%. Furthermore, on the sqlIV2 dataset, SIDNet-1 attains 97.77%, and SIDNet-2 achieves 97.83% accuracy respectively.

关键词： Structured Query Language sql injection Databases Analytical models Accuracy Feature extraction Security Data models Deep learning Computer science SIDNet sqlI cyber security CNN

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Simulating all archetypes of sql injection vulnerability exploitation using reinforcement learning agents

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2024年第1期23卷 225-246页

作者： Sommervoll, Avald Aslaugson Erdodi, Laszlo Zennaro, Fabio Massimo Univ Oslo Dept Informat Gaustadalleen 23B N-0373 Oslo Norway Norwegian Univ Sci & Technol Dept Informat Secur & Commun Technol Hogskoleringen 1 N-7034 Trondheim Trondelag Norway Univ Warwick Dept Comp Sci 6 Lord Bhattacharyya Way Coventry CV4 7EZ West Midlands England

Vulnerabilities such as sql injection represent a serious challenge to security. While tools with a pre-defined logic are commonly used in the field of penetration testing, the continually evolving nature of the security challenge calls for models able to learn autonomously from experience. In this paper we build on previous results on the development of reinforcement learning models devised to exploit specific forms of sql injection, and we design agents that are able to tackle a varied range of sql injection vulnerabilities, virtually comprising all the archetypes normally considered by experts. We show that our agents, trained on a synthetic environment, perform a transfer of learning among the different sql injections challenges;in particular, they learn to use their queries to efficiently gain knowledge about multiple vulnerabilities at once. We also introduce a novel and more versatile way to interpret server messages that reduces reliance on expert inputs. Our simulations show the feasibility of our approach which easily deals with a number of homogeneous challenges, as well as some of its limitations when presented with problems having higher degrees of uncertainty.

关键词： sql injection Capture the flag Vulnerability detection Autonomous agents Reinforcement learning Q-learning

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A deep learning approach based on multi-view consensus for sql injection detection

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2024年第2期23卷 1541-1556页

作者： Kakisim, Arzu Gorgulu Istanbul Medeniyet Univ Comp Engn Istanbul Turkiye

sql injection (sqli) attacks are one of the oldest and most serious security threats, consistently ranking among the top ten critical web security risks. Traditional defense mechanisms against sql injection predominantly use blacklists to disallow common injection characters or terms. However, the major challenge for these systems is to create a comprehensive list of potential sqli characters, terms, and multi-terms that encompass various types of sqli attacks (time-based, error-based, etc.), taking into account various sql datasets (such as Mysql, Oracle, and Nosql). Recently, some research studies have concentrated on feature learning from sql queries by applying some well-known deep architectures to detect sqli attacks. Motivated by a similar objective, this research introduces a novel deep learning-based sqli detection system named "Bidirectional LSTM-CNN based on Multi-View Consensus" (MVC-BiCNN). The proposed method implements a pre-processing step that generates multiple views from sql data by semantically encoding sql statements into their corresponding sql tags. By utilizing two different main layers, which are bidirectional long short-term memory (LSTM) and convolutional neural network (CNN), the proposed method learns a joint latent space from multi-view representations. In the detection phase, the proposed method yields separate predictions for each representation and assesses whether the query constitutes an sqli attack based on a consensus function's output. Moreover, Interpretable Model-Agnostic Annotations (LIME), one of the methods of Explainable Artificial Intelligence (XAI), is employed for the purpose of interpreting the model's results and analyzing the sql injection (sqli) inputs. The experimental results demonstrate that MVC-BiCNN outperforms the baseline methods, yielding 99.96% detection rate.

关键词： sql injection Deep learning Code injection Information security XAI

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Towards lightweight model using non-local-based graph convolution neural network for sql injection detection

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EGYPTIAN INFORMATICS JOURNAL 2025年 30卷

作者： Nguyen, Duc-Chinh Ha, Manh-Hung Do, Manh-Tuan Chen, Oscal Tzyh-Chiang Int Sch Hanoi 100000 Vietnam Natl Chung Cheng Univ Chiayi 62102 Taiwan Univ Paris Saclay F-91000 Evry Ile De France France

sql injection poses serious threats to web applications and databases by enabling unauthorized access and data leakage. To address this issue, we propose a unique graph network, an innovative topology not explored previously for sql injection detection. sql statements are nodes, and their connections form edges in the graph. We introduce three graph CNN models, including a graph classification model with a two-layer Graph Convolutional Network (GCN), a graph classification model leveraging a non-local graph convolution derived from a 1x1 convolution, supplanting the original 1x1 convolution, and a modified non-local-block module by substituting the 1x1 convolution layers in the non-local architecture with GCN. The proposed models exhibit accuracy above 99% and inference times under 1 ms on two datasets. In comparison with traditional 22 models, our models using GCN demonstrate superior computation efficiency, parameter reduction, accuracy enhancement, and the advantage of handling input sequences of any length, underlining their potential in real-world cybersecurity systems, especially in effective sql injection detection and mitigation strategies.

关键词： sql injection Graph convolution neural network Non-local network Lightweight model Detection LSTM BERT CNN

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sql injection Detection Using Optimized Recurrent Neural Network Integrated with N-Gram: A Comparison

SQL Injection Detection Using Optimized Recurrent Neural Net...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Vinodh, Jerlin Mary Kanaga, E. Grace Computer Science and Engineeringm Karunya University of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331540685

sql injection is a serious security issue that can lead to data breaches and identity theft. Detecting sql injection attempts in real time is important to prevent attacks and identify vulnerabilities. The performance of Particle Swarm Optimizer., Ant Colony Optimizer., and Artificial Bee Colony optimizer in training Recurrent Neural Networks for sql detection using N-gram analysis is compared. The inclusion of N-gram analysis significantly improves the RNN-based model's performance., making it a promising solution for detecting sql injection attacks in real-world web applications. © 2024 IEEE.

关键词： N-gram analysis Recurrent Neural Networks sql injection vulnerabilities

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Analysis of sql injection Based on Petri Net in Wireless Network

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2023年第1期39卷 167-181页

作者： Wang, Yi-Chuan Zhang, Gui-Ling Zhang, Ya-Ling Xian Univ Technol Sch Comp Sci & Engn Xian 710000 Peoples R China Xian Univ Technol Shaanxi Key Lab Network Comp & Secur Xian 710000 Peoples R China

sql injection is becoming more frequent and is urgent to be studied the defense mechanism. In this paper, a fine-grained Petri Net (PN) model is proposed to describe the process of sql Boolean injection and sql time blind injection. The results show that the speed of PN model simulating sql injection can be scaled, such as 10 times the speed. It is conducive to faster-than-real-time simulation and prediction of unknown vulnerabilities when the injection speed is fast. It is good for fine-grained analysis of the attack and es-tablish a patch model when the injection speed is slow. The modeling and simulation in this paper provide a theoretical guarantee for the occurrence mechanism of vulnerabilities and the discovery of unknown vulnerabilities.

关键词： sql injection wireless network network security petri net fine-grained modeling

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