ArticlePDF Available

Learning SQL for Database Intrusion Detection using Context-Sensitive Modelling (Re-submission)

Authors:
  • University of Bonn and Fraunhofer FKIE

Abstract and Figures

Modern multi-tier application systems are gen- erally based on high performance database sys- tems in order to process and store business in- formation. Containing valuable business infor- mation, these systems are highly interesting to attackers and special care needs to be taken to prevent any malicious access to this database layer. In this work we propose a novel approach for modelling SQL statements to apply machine learning techniques, such as clustering or out- lier detection, in order to detect malicious be- haviour at the database transaction level. The approach incorporates the parse tree structure of SQL queries as characteristic e.g. for cor- relating SQL queries with applications and dis- tinguishing benign and malicious queries. We demonstrate the usefulness of our approach on real-world data.
Content may be subject to copyright.
A preview of the PDF is not available
... The research paper on web application intrusion detection proposes a tool that employs DNN and auto-encoders for semi-supervised and unsupervised learning for web attack detection, including SQLIA [11]. With the aim of modeling SQL commands and parsing the tree structure of SQL queries as features, a clustering technique is presented for mapping SQL queries to applications and recognizing harmful malicious and non-malicious queries [12]. Another earlier research reported that the detection accuracy for SQL and PHP code injection attacks using the current RCE vulnerability detection system, which includes a payload-based anomaly detection approach by combining structural information from a protocol analyzer [14], was 49%. ...
Preprint
Full-text available
Since they are able to meet both commercial and consumer objectives, web apps are becoming more and more popular. Web apps may now deliver business services to its stakeholders in the most efficient and effective manner possible. Today, a variety of services are offered via web apps, and the efficiency of such services is gauged by the speed at which they handle requests and the usefulness of their informational features. But concurrently, a vulnerability to such services might arise through faulty authorization. At this time, cyber-attacks are a serious concern for any global digital transition. Various kinds of application level vulnerabilities still exist in the web system as a result of incautious coding practices throughout implementation and an inadequate level of security awareness. Among the security vulnerabilities in the modern world the major is the remote code execution (RCE). RCE has been identified as the second-most vital online application vulnerability. An exhaustive review on RCE vulnerability is presented in this article.
... Bockerman et al., [15] dealed with kernel based tree classification method to explore the reality of SQL syntax. They ended with the advantage with exhibiting the machine learning thechniques. ...
Article
Full-text available
Detecting SQL injection attacks (SQLIAs) is ending up progressively significant in database-driven sites. A large portion of the investigations on SQLIA detection have concentrated on the structured query language (SQL) structure at the application level. Yet, those methodologies unavoidably neglects to identify those attacks that utilization previously put away methodology and information inside the database framework. While most existing techniques tended to towards diminishing the quantity of support vectors, the proposed philosophy concentrated on decreasing the quantity of test datapoints that need SVMs assistance in getting grouped. The focal thought is to inexact the choice limit of SVM utilizing paired trees. The subsequent tree is a half and half tree as in it has both univariate and multivariate (SVM) nodes. The cross breed tree takes SVMs assistance just in ordering significant information focuses lying close choice limit staying less urgent datapoints are grouped by quick univariate nodes.
Article
Full-text available
In this paper the SQL injection attacks (SQLIAs) continue to pose significant threats to web applications, exploiting vulnerabilities in poorly secured databases. Traditional detection and prevention mechanisms often struggle to adapt to the evolving techniques employed by attackers. This paper presents an advanced framework for detecting and preventing SQL injection attacks using machine learning techniques to enhance web security. The proposed system employs a combination of supervised and unsupervised learning models to analyze query patterns, identify anomalies, and classify malicious inputs in real time. Our methodology involves preprocessing web application traffic, feature extraction from SQL queries, and model training using labeled datasets. Various algorithms, including decision trees, support vector machines, and neural networks, were evaluated to determine their effectiveness in detecting SQLIAs. The results demonstrate that machine learning-based approaches can significantly improve the detection and prevention of SQL injection attacks compared to traditional rule-based methods. This study also highlights the importance of continuous learning and adaptation in cyber security frameworks. The proposed solution provides a robust and scalable tool for enhancing web application security, paving the way for further research into the integration of artificial intelligence in cyber security.
Research Proposal
Full-text available
Detect the SQL Injection Attacks is to identify whether Hacker entered in users Data is Fraudulent or not. This project is totally built at user level and user at any place in this world
Article
Data Warehouse (DW) security has always been a critical challenge for DW designers because of its global availability and accessibility. Over time, different researchers have suggested different DW security solutions, such as Role Based Access Controls (RBAC), Extended RBAC, Temporal RBAC (TRBAC), Risk-based access control, etc. Intrusion Detection System (IDS) and some other customized security solutions for DWs have also been proposed. Here, Risk-based access control provides additional security by utilizing risk value for each access decision. In RBAC systems, if an attacker obtains access to the system using some compromised credentials, the RBACs has no mechanism to secure DW elements which are accessible to the compromised user's role. The Intrusion Detection System (IDS) aims to solve this limitation; it monitors the user activities and alerts the system administrator whenever a user deviates from routine behavior. However, in the IDS solution for DWs, most of the real intrusions go undetected. In this work, we propose a second level authentication within the IDS, where a minute deviation from the user’s past behavior is detected. It brings more robustness to the user's historical profile and makes the system less susceptible to false negatives. The proposed solution has been implemented on standard TPC-H databases, and results indicate a significant decrease in undetected real intrusions, which is one of the main achievements of the proposed mechanism.
Chapter
In today's world, many of our interactions are with Web applications, HTML5 based hybrid mobile applications and IoT devices. These applications and devices have a database at the back‐end which can contain sensitive information. The information might contain personal details as well as information which are not meant to be altered illicitly. Hackers are always looking to penetrate into the databases to gather information which can later be used for illegal purposes. Such breaches can cause harm to the individual as well as the organization responsible for managing that data. One such method is to use SQL injection attacks (SQLIAs). It enables the hacker to create SQL queries which can be used to alter or retrieve the state of the database. The paper is based on a review of various SQL injection approaches with the methods available to counter these attacks.
Chapter
Land use/Land cover classification is essential from the point of Earth explorations and scientific investigation. Earlier, the land‐use and land cover changes are monitored with the assistance of the pixel‐based approach, and now objects based approaches have taken their place. In the pixel‐based approach, image pixels are examined to monitor the changes developed in land use and land cover. The newly developed method in the field of remote sensing is object‐based change detection (OBCD) techniques. These approaches have entirely changed the study of remotely sensing and satellite image processing. The pixel‐based approach made a comparison based on changing pixels between two or a series of images. In contrast, the object‐based approach constitutes the formation of objects (classes), e.g., water, urban, agriculture, soil, etc., between two images and making a comparison between them. One of the important accept of these techniques is how accurately they provide us information about the changes in the nearby surrounding. This paper begins with the development of traditional pixel‐based methods and ends with the evolution of the latest object‐based change detection techniques. LANDSAT and PALSAR images are used to represent the changes developed in the land use/ land cover using pixel‐based and object‐based approaches.
Article
Relational database management system (RDBMS) is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The model consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation.
Conference Paper
Full-text available
An SQL injection attack targets interactive web applica- tions that employ database services. Such applications ac- cept user input, such as form elds, and then include this input in database requests, typically SQL statements. In SQL injection, the attacker provides user input that results in a dieren t database request than was intended by the application programmer. That is, the interpretation of the user input as part of a larger SQL statement, results in an SQL statement of a dieren t form than originally intended. We describe a technique to prevent this kind of manipula- tion and hence eliminate SQL injection vulnerabilities. The technique is based on comparing, at run time, the parse tree of the SQL statement before inclusion of user input with that resulting after inclusion of input. Our solution is ecien t, adding about 3 ms overhead to database query costs. In addition, it is easily adopted by application pro- grammers, having the same syntactic structure as current popular record set retrieval methods. For empirical anal- ysis, we provide a case study of our solution in J2EE. We implement our solution in a simple static Java class, and show its eectiv eness and scalability.
Conference Paper
Full-text available
The syntax of application layer protocols carries valuable in- formation for network intrusion detection. Hence, the majority of modern IDS perform some form of protocol analysis to refine their signatures with application layer context. Protocol analysis, however, has been mainly used for misuse detection, which limits its application for the detection of unknown and novel attacks. In this contribution we address the issue of incorporating application layer context into anomaly-based intrusion de- tection. We extend a payload-based anomaly detection method by incor- porating structural information obtained from a protocol analyzer. The basis for our extension is computation of similarity between attributed tokens derived from a protocol grammar. The enhanced anomaly detec- tion method is evaluated in experiments on detection of web attacks, yielding an improvement of detection accuracy of 49%. While byte-level anomaly detection is sufficient for detection of buffer overflow attacks, identification of recent attacks such as SQL and PHP code injection strongly depends on the availability of application layer context.
Article
Web-based vulnerabilities represent a substantial portion of the security exposures of computer networks. In order to detect known web-based attacks, misuse detection systems are equipped with a large number of signatures. Unfortunately, it is difficult to keep up with the daily disclosure of web-related vulnerabilities, and, in addition, vulnerabilities may be introduced by installation-specific web-based applications. Therefore, misuse detection systems should be complemented with anomaly detection systems.This paper presents an intrusion detection system that uses a number of different anomaly detection techniques to detect attacks against web servers and web-based applications. The system analyzes client queries that reference server-side programs and creates models for a wide-range of different features of these queries. Examples of such features are access patterns of server-side programs or values of individual parameters in their invocation. In particular, the use of application-specific characterization of the invocation parameters allows the system to perform focused analysis and produce a reduced number of false positives.The system derives automatically the parameter profiles associated with web applications (e.g., length and structure of parameters) and relationships between queries (e.g., access times and sequences) from the analyzed data. Therefore, it can be deployed in very different application environments without having to perform time-consuming tuning and configuration.
Conference Paper
There is a growing security concern on the increasing number of databases that are accessible through the Internet. Such databases may contain sensitive information like credit card numbers and personal medical histories. Many e-service providers are reported to be leaking customers’ information through their websites. The hackers exploited poorly coded programs that interface with backend databases using SQL injection techniques. We developed an architectural framework, DIDAFIT (Detecting Intrusions in DAtabases through FIngerprinting Transactions) [1], that can efficiently detect illegitimate database accesses. The system works by matching SQL statements against a known set of legitimate database transaction fingerprints. In this paper, we explore the various issues that arise in the collation, representation and summarization of this potentially huge set of legitimate transaction fingerprints. We describe an algorithm that summarizes the raw transactional SQL queries into compact regular expressions. This representation can be used to match against incoming database transactions efficiently. A set of heuristics is used during the summarization process to ensure that the level of false negatives remains low. This algorithm also takes into consideration incomplete logs and heuristically identifies “high risk” transactions.
Conference Paper
There are many Intrusion Detection Systems (IDS) for networks and operating systems and there are few for Databases- despite the fact that the most valuable resources of every organization are in its databases. The number of database attacks has grown, especially since most databases are accessible from the web and satisfactory solutions to these kinds of attacks are still lacking. We present DIWeDa - a practical solution for detecting intrusions to web databases. Contrary to any existing database intrusion detection method, our method works at the session level and not at the SQL statement or transaction level. We use a novel SQL Session Content Anomaly intrusion classifier and this enables us to detect not only most known attacks such as SQL Injections, but also more complex kinds of attacks such as Business Logic Violations. Our experiments implemented the proposed intrusion detection system prototype and showed its feasibility and effectiveness.
Conference Paper
Careless development of web-based applications results in vulnerable code being deployed and made available to the whole Internet, creating easily-exploitable entry points for the compromise of entire networks. To ameliorate this situ- ation, we propose an approach that composes a web-based anomaly detection system with a reverse HTTP proxy. The approach is based on the assumption that a web site's con- tent can be split into security sensitive and non-sensitive parts, which are distributed to dieren t servers. The anomaly score of a web request is then used to route suspicious re- quests to copies of the web site that do not hold sensitive content. By doing this, it is possible to serve anomalous but benign requests that do not require access to sensitive information, sensibly reducing the impact of false positives. We developed a prototype of our approach and evaluated its applicability with respect to several existing web-based applications, showing that our approach is both feasible and eectiv e.