Visible to the public Biblio

Filters: Keyword is relational database security  [Clear All Filters]
2021-08-31
Ge, Chonghui, Sun, Jian, Sun, Yuxin, Di, Yunlong, Zhu, Yongjin, Xie, Linfeng, Zhang, Yingzhou.  2020.  Reversible Database Watermarking Based on Random Forest and Genetic Algorithm. 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). :239—247.
The advancing information technology is playing more and more important role in data mining of relational database.1 The transfer and sharing of databases cause the copyright-related security threats. Database watermarking technology can effectively solve the problem with copyright protection and traceability, which has been attracting researchers' attention. In this paper, we proposed a novel, robust and reversible database watermarking technique, named histogram shifting watermarking based on random forest and genetic algorithm (RF-GAHCSW). It greatly improves the watermark capacity by means of histogram width reduction and eliminates the impact of the prediction error attack. Meanwhile, random forest algorithm is used to select important attributes for watermark embedding, and genetic algorithm is employed to find the optimal secret key for the database grouping and determine the position of watermark embedding to improve the watermark capacity and reduce data distortion. The experimental results show that the robustness of RF-GAHCSW is greatly improved, compared with the original HSW, and the distortion has little effect on the usability of database.
El-Banna, Mohamed Metwally, Khafagy, Mohamed Helmy, El Kadi, Hatem Mohamed.  2020.  Smurf Detector: a Detection technique of criminal entities involved in Money Laundering. 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). :64—71.
Criminals do money laundry to hide the illegitimate sources of money to show as if their money is of a legitimate source. Money laundry has many stages that money flow has to go through to finally look as if it is of a legitimate source, rule-based systems are implemented across different banks to detect structuring which is one technique of the layering stage which sophisticated criminals can evade by unsatisfying the check rules. In this work, graph database and graph data mining are to be used to overcome this limitation, the proposed technique does this by plotting the whole transactional monetary flow of entities doing money transfers between each other as one large graph database and then detecting clusters of entities interacting with each other, afterwards detection of the most influential node (intended destination) which we consider the destination to which huge amounts of money is intended to flow to (criminal`s account) using PageRank algorithm and eventually detecting all members (Smurfs) of participated in the paths leading to that destination, a technique that would be hard to implement using traditional RDBMS in contrary to Graph DB, our results have proven correct detection of clusters as well as the final destination of the monetary flow (criminal`s account).
Natarajan, K, Shaik, Vaheedbasha.  2020.  Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases. 2020 Fifth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). :137—142.
This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions.
Kim, Hwajung, Yeom, Heon Young, Son, Yongseok.  2020.  An Efficient Database Backup and Recovery Scheme using Write-Ahead Logging. 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). :405—413.
Many cloud services perform periodic database backup to keep the data safe from failures such as sudden system crashes. In the database system, two techniques are widely used for data backup and recovery: a physical backup and a logical backup. The physical backup uses raw data by copying the files in the database, whereas the logical backup extracts data from the database and dumps it into separated files as a sequence of query statements. Both techniques support a full backup strategy that contains data of the entire database and incremental backup strategy that contains changed data since a previous backup. However, both strategies require additional I/O operations to perform the backup and need a long time to restore a backup. In this paper, we propose an efficient backup and recovery scheme by exploiting write-ahead logging (WAL) in database systems. In the proposed scheme, for backup, we devise a backup system to use log data generated by the existing WAL to eliminate the additional I/O operations. To restore a backup, we utilize and optimize the existing crash recovery procedure of WAL to reduce recovery time. For example, we divide the recovery range and applying the backup data for each range independently via multiple threads. We implement our scheme in MySQL, a popular database management system. The experimental result demonstrates that the proposed scheme provides instant backup while reducing recovery time compared with the existing schemes.
Feng, Na, Yin, Qiangguo.  2020.  Research on Computer Software Engineering Database Programming Technology Based on Virtualization Cloud Platform. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). :696—699.
The most important advantage of database is that it can form an intensive management system and serve a large number of information users, which shows the importance of information security in network development. However, there are many problems in the current computer software engineering industry, which seriously hinder the development of computer software engineering, among which the most remarkable and prominent one is that the database programming technology is difficult to be effectively utilized. In this paper, virtualization technology is used to manage the underlying resources of data center with the application background of big data technology, and realize the virtualization of network resources, storage resources and computing resources. It can play a constructive role in the construction of data center, integrate traditional and old resources, realize the computing data center system through virtualization, distributed storage and resource scheduling, and realize the clustering and load balancing of non-relational databases.
Zhang, Zehao, Yu, Zhen, Weng, Wei, Guan, Cheng.  2020.  Study on the Digitalization Method of Intelligent Emergency Plan of Power System. 2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE). :179—182.
This paper puts forward a formalized method of emergency plan based on ontology, sums up the main concepts such as system, event, rule, measure, constraint and resource, and analyzes the logical relationship among concepts. A digital intelligent emergency plan storage scheme based on relational database model is proposed. In this paper, full-text search, data search and knowledge search are comprehensively used to adapt to the information needs and characteristics of different users' query plans. Finally, an example of emergency plan made by a power supply company is given to illustrate the effectiveness of the method.
Bartol, Janez, Souvent, Andrej, Suljanović, Nermin, Zajc, Matej.  2020.  Secure data exchange between IoT endpoints for energy balancing using distributed ledger. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). :56—60.
This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.
Fadolalkarim, Daren, Bertino, Elisa, Sallam, Asmaa.  2020.  An Anomaly Detection System for the Protection of Relational Database Systems against Data Leakage by Application Programs. 2020 IEEE 36th International Conference on Data Engineering (ICDE). :265—276.
Application programs are a possible source of attacks to databases as attackers might exploit vulnerabilities in a privileged database application. They can perform code injection or code-reuse attack in order to steal sensitive data. However, as such attacks very often result in changes in the program's behavior, program monitoring techniques represent an effective defense to detect on-going attacks. One such technique is monitoring the library/system calls that the application program issues while running. In this paper, we propose AD-PROM, an Anomaly Detection system that aims at protecting relational database systems against malicious/compromised applications PROgraMs aiming at stealing data. AD-PROM tracks calls executed by application programs on data extracted from a database. The system operates in two phases. The first phase statically and dynamically analyzes the behavior of the application in order to build profiles representing the application's normal behavior. AD-PROM analyzes the control and data flow of the application program (i.e., static analysis), and builds a hidden Markov model trained by the program traces (i.e., dynamic analysis). During the second phase, the program execution is monitored in order to detect anomalies that may represent data leakage attempts. We have implemented AD-PROM and carried experimental activities to assess its performance. The results showed that our system is highly accurate in detecting changes in the application programs' behaviors and has very low false positive rates.
Vonitsanos, Gerasimos, Dritsas, Elias, Kanavos, Andreas, Mylonas, Phivos, Sioutas, Spyros.  2020.  Security and Privacy Solutions associated with NoSQL Data Stores. 2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA). :1—5.
Technologies such as cloud computing and big data management, have lately made significant progress creating an urgent need for specific databases that can safely store extensive data along with high availability. Specifically, a growing number of companies have adopted various types of non-relational databases, commonly referred to as NoSQL databases. These databases provide a robust mechanism for the storage and retrieval of large amounts of data without using a predefined schema. NoSQL platforms are superior to RDBMS, especially in cases when we are dealing with big data and parallel processing, and in particular, when there is no need to use relational modeling. Sensitive data is stored daily in NoSQL Databases, making the privacy problem more serious while raising essential security issues. In our paper, security and privacy issues when dealing with NoSQL databases are introduced and in following, security mechanisms and privacy solutions are thoroughly examined.
Churi, Akshata A., Shinde, Vinayak D..  2020.  Alphanumeric Database Security through Digital Watermarking. 2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW). :1—4.
As the demand of online data availability increases for sharing data, business analytics, security of available data becomes important issue, data needs to be protected from unauthorized access as well as it needs to provide authority that the data is received from a trusted owner. To provide owners identity digital watermarking technique is used since long time for multimedia data. This paper proposed a technique which supports watermarking on database as most of the data available today is in database format. The characters to be entered as watermark are converted into binary values; these binary values are hidden in the database using space character. Each bit is hidden in each tuple randomly. Ant colony optimization algorithm is proposed to select tuples where watermark bits are inserted. The proposed system is enhanced in terms of security due to use of ant colony optimization and resilient because even if some bits are modified the hidden text remains almost same.
Siledar, Seema, Tamane, Sharvari.  2020.  A distortion-free watermarking approach for verifying integrity of relational databases. 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). :192—195.
Due to high availability and easy accessibility of information, it has become quite difficult to assure security of data. Even though watermarking seems to be an effective solution to protect data, it is still challenging to be used with relational databases. Moreover, inserting a watermark in database may lead to distortion. As a result, the contents of database can no longer remain useful. Our proposed distortion-free watermarking approach ensures that integrity of database can be preserved by generating an image watermark from its contents. This image is registered with Certification Authority (CA) before the database is distributed for use. In case, the owner suspects any kind of tampering in the database, an image watermark is generated and compared with the registered image watermark. If both do not match, it can be concluded that the integrity of database has been compromised. Experiments are conducted on Forest Cover Type data set to localize tampering to the finest granularity. Results show that our approach can detect all types of attack with 100% accuracy.
2020-04-20
Huang, Zhen, Lie, David, Tan, Gang, Jaeger, Trent.  2019.  Using Safety Properties to Generate Vulnerability Patches. 2019 IEEE Symposium on Security and Privacy (SP). :539–554.
Security vulnerabilities are among the most critical software defects in existence. When identified, programmers aim to produce patches that prevent the vulnerability as quickly as possible, motivating the need for automatic program repair (APR) methods to generate patches automatically. Unfortunately, most current APR methods fall short because they approximate the properties necessary to prevent the vulnerability using examples. Approximations result in patches that either do not fix the vulnerability comprehensively, or may even introduce new bugs. Instead, we propose property-based APR, which uses human-specified, program-independent and vulnerability-specific safety properties to derive source code patches for security vulnerabilities. Unlike properties that are approximated by observing the execution of test cases, such safety properties are precise and complete. The primary challenge lies in mapping such safety properties into source code patches that can be instantiated into an existing program. To address these challenges, we propose Senx, which, given a set of safety properties and a single input that triggers the vulnerability, detects the safety property violated by the vulnerability input and generates a corresponding patch that enforces the safety property and thus, removes the vulnerability. Senx solves several challenges with property-based APR: it identifies the program expressions and variables that must be evaluated to check safety properties and identifies the program scopes where they can be evaluated, it generates new code to selectively compute the values it needs if calling existing program code would cause unwanted side effects, and it uses a novel access range analysis technique to avoid placing patches inside loops where it could incur performance overhead. Our evaluation shows that the patches generated by Senx successfully fix 32 of 42 real-world vulnerabilities from 11 applications including various tools or libraries for manipulating graphics/media files, a programming language interpreter, a relational database engine, a collection of programming tools for creating and managing binary programs, and a collection of basic file, shell, and text manipulation tools.
Zaw, Than Myo, Thant, Min, Bezzateev, S. V..  2019.  Database Security with AES Encryption, Elliptic Curve Encryption and Signature. 2019 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF). :1–6.

A database is an organized collection of data. Though a number of techniques, such as encryption and electronic signatures, are currently available for the protection of data when transmitted across sites. Database security refers to the collective measures used to protect and secure a database or database management software from illegitimate use and malicious threats and attacks. In this paper, we create 6 types of method for more secure ways to store and retrieve database information that is both convenient and efficient. Confidentiality, integrity, and availability, also known as the CIA triad, is a model designed to guide policies for information security within the database. There are many cryptography techniques available among them, ECC is one of the most powerful techniques. A user wants to the data stores or request, the user needs to authenticate. When a user who is authenticated, he will get key from a key generator and then he must be data encrypt or decrypt within the database. Every keys store in a key generator and retrieve from the key generator. We use 256 bits of AES encryption for rows level encryption, columns level encryption, and elements level encryption for the database. Next two method is encrypted AES 256 bits random key by using 521 bits of ECC encryption and signature for rows level encryption and column level encryption. Last method is most secure method in this paper, which method is element level encryption with AES and ECC encryption for confidentiality and ECC signature use for every element within the database for integrity. As well as encrypting data at rest, it's also important to ensure confidential data are encrypted in motion over our network to protect against database signature security. The advantages of elements level are difficult for attack because the attacker gets a key that is lose only one element. The disadvantages need to thousands or millions of keys to manage.

Gupta, Himanshu, Mondal, Subhash, Ray, Srayan, Giri, Biswajit, Majumdar, Rana, Mishra, Ved P.  2019.  Impact of SQL Injection in Database Security. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). :296–299.
In today's world web applications have become an instant means for information broadcasting. At present, man has become so dependent on web applications that everything done through electronic means like e-banking, e-shopping, online payment of bills etc. Due to an unauthorized admittance might threat customer's or user's confidentiality, integrity and authority. SQL injection considered as most Spartan dangerous coercions to the databases of web applications. current scenario databases are highly susceptible to SQL Injection[4] . SQL Injection is one of the most popular and dangerous hacking or cracking technique . In this work authors projected a novel approach to mitigate SQL Injection Attacks in a database. We have illustrated a technique or method prevent SQLIA by incorporating a hybrid encryption in form of Advanced Encryption Standard (AES) and Elliptical Curve Cryptography (ECC) [5]. In this research paper integrated approach of encryption method is followed to prevent the databases of the web applications against SQL Injection Attack. Incidentally if an invader gains access to the database, then it can cause severe damage and ends up with retrieves data or information. So to prevent these type of attacks a combined approach is projected , Advanced Encryption Standard (AES) at login phase to prevent the unauthorized access to databases and on the other hand Elliptical Curve Cryptography (ECC) to encode the database so that without the key no one can access the database information [3]. This research paper illustrates the technique to prevent SQL Injection Attack.
Mahmoud, Ahmed Y., Alqumboz, Mohammed Naji Abu.  2019.  Encryption Based On Multilevel Security for Relational Database EBMSR. 2019 International Conference on Promising Electronic Technologies (ICPET). :130–135.
Cryptography is one of the most important sciences today because of the importance of data and the possibility of sharing data via the Internet. Therefore, data must be preserved when stored or transmitted over the Internet. Encryption is used as a solution to protect information during the transmission via an open channel. If the information is obtained illegally, the opponent/ enemy will not be able to understand the information due to encryption. In this paper we have developed a cryptosystem for testing the concepts of multi security level. The information is encrypted using more than one encryption algorithm based on the security level. The proposed cryptosystem concerns of Encryption Based on Multilevel Security (MLS) Model for DBMS. The cryptosystem is designed for both encryption and decryption.
2020-04-03
Al-Haj, Ali, Aziz, Benjamin.  2019.  Enforcing Multilevel Security Policies in Database-Defined Networks using Row-Level Security. 2019 International Conference on Networked Systems (NetSys). :1-6.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

Kantarcioglu, Murat, Shaon, Fahad.  2019.  Securing Big Data in the Age of AI. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA). :218—220.

Increasingly organizations are collecting ever larger amounts of data to build complex data analytics, machine learning and AI models. Furthermore, the data needed for building such models may be unstructured (e.g., text, image, and video). Hence such data may be stored in different data management systems ranging from relational databases to newer NoSQL databases tailored for storing unstructured data. Furthermore, data scientists are increasingly using programming languages such as Python, R etc. to process data using many existing libraries. In some cases, the developed code will be automatically executed by the NoSQL system on the stored data. These developments indicate the need for a data security and privacy solution that can uniformly protect data stored in many different data management systems and enforce security policies even if sensitive data is processed using a data scientist submitted complex program. In this paper, we introduce our vision for building such a solution for protecting big data. Specifically, our proposed system system allows organizations to 1) enforce policies that control access to sensitive data, 2) keep necessary audit logs automatically for data governance and regulatory compliance, 3) sanitize and redact sensitive data on-the-fly based on the data sensitivity and AI model needs, 4) detect potentially unauthorized or anomalous access to sensitive data, 5) automatically create attribute-based access control policies based on data sensitivity and data type.

2020-03-18
Pouliot, David, Griffy, Scott, Wright, Charles V..  2019.  The Strength of Weak Randomization: Easily Deployable, Efficiently Searchable Encryption with Minimal Leakage. 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :517–529.

Efficiently searchable and easily deployable encryption schemes enable an untrusted, legacy service such as a relational database engine to perform searches over encrypted data. The ease with which such schemes can be deployed on top of existing services makes them especially appealing in operational environments where encryption is needed but it is not feasible to replace large infrastructure components like databases or document management systems. Unfortunately all previously known approaches for efficiently searchable and easily deployable encryption are vulnerable to inference attacks where an adversary can use knowledge of the distribution of the data to recover the plaintext with high probability. We present a new efficiently searchable, easily deployable database encryption scheme that is provably secure against inference attacks even when used with real, low-entropy data. We implemented our constructions in Haskell and tested databases up to 10 million records showing our construction properly balances security, deployability and performance.

2020-02-10
Nomura, Komei, Rikitake, Kenji, Matsumoto, Ryosuke.  2019.  Automatic Whitelist Generation for SQL Queries Using Web Application Tests. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:465–470.

Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods for automated generation are unable to detect attacks immediately because of the long time required for collecting legitimate queries. Moreover, they require application-specific implementations that reduce the versatility of the methods. As described herein, we propose a method to generate a whitelist automatically using queries issued during web application tests. Our proposed method uses the queries generated during application tests. It is independent of specific applications, which yields improved timeliness against attacks and versatility for multiple applications.

2019-11-04
Beigi, Ghazaleh, Shu, Kai, Zhang, Yanchao, Liu, Huan.  2018.  Securing Social Media User Data: An Adversarial Approach. Proceedings of the 29th on Hypertext and Social Media. :165–173.
Social media users generate tremendous amounts of data. To better serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such data would raise more concerns on user privacy. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of social media users. In this work, we propose a new adversarial attack specialized for social media data.We further provide a principled way to assess effectiveness of anonymizing different aspects of social media data. Our work sheds light on new privacy risks in social media data due to innate heterogeneity of user-generated data which require striking balance between sharing user data and protecting user privacy.
Alomari, Mohammad Ahmed, Hafiz Yusoff, M., Samsudin, Khairulmizam, Ahmad, R. Badlishah.  2019.  Light Database Encryption Design Utilizing Multicore Processors for Mobile Devices. 2019 IEEE 15th International Colloquium on Signal Processing Its Applications (CSPA). :254–259.

The confidentiality of data stored in embedded and handheld devices has become an urgent necessity more than ever before. Encryption of sensitive data is a well-known technique to preserve their confidentiality, however it comes with certain costs that can heavily impact the device processing resources. Utilizing multicore processors, which are equipped with current embedded devices, has brought a new era to enhance data confidentiality while maintaining suitable device performance. Encrypting the complete storage area, also known as Full Disk Encryption (FDE) can still be challenging, especially with newly emerging massive storage systems. Alternatively, since the most user sensitive data are residing inside persisting databases, it will be more efficient to focus on securing SQLite databases, through encryption, where SQLite is the most common RDBMS in handheld and embedded systems. This paper addresses the problem of ensuring data protection in embedded and mobile devices while maintaining suitable device performance by mitigating the impact of encryption. We presented here a proposed design for a parallel database encryption system, called SQLite-XTS. The proposed system encrypts data stored in databases transparently on-the-fly without the need for any user intervention. To maintain a proper device performance, the system takes advantage of the commodity multicore processors available with most embedded and mobile devices.

Khan, Muhammad Imran, O’Sullivan, Barry, Foley, Simon N..  2018.  Towards Modelling Insiders Behaviour as Rare Behaviour to Detect Malicious RDBMS Access. 2018 IEEE International Conference on Big Data (Big Data). :3094–3099.
The heart of any enterprise is its databases where the application data is stored. Organizations frequently place certain access control mechanisms to prevent access by unauthorized employees. However, there is persistent concern about malicious insiders. Anomaly-based intrusion detection systems are known to have the potential to detect insider attacks. Accurate modelling of insiders behaviour within the framework of Relational Database Management Systems (RDBMS) requires attention. The majority of past research considers SQL queries in isolation when modelling insiders behaviour. However, a query in isolation can be safe, while a sequence of queries might result in malicious access. In this work, we consider sequences of SQL queries when modelling behaviours to detect malicious RDBMS accesses using frequent and rare item-sets mining. Preliminary results demonstrate that the proposed approach has the potential to detect malicious RDBMS accesses by insiders.
Sallam, Asmaa, Bertino, Elisa.  2018.  Detection of Temporal Data Ex-Filtration Threats to Relational Databases. 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). :146–155.
According to recent reports, the most common insider threats to systems are unauthorized access to or use of corporate information and exposure of sensitive data. While anomaly detection techniques have proved to be effective in the detection of early signs of data theft, these techniques are not able to detect sophisticated data misuse scenarios in which malicious insiders seek to aggregate knowledge by executing and combining the results of several queries. We thus need techniques that are able to track users' actions across time to detect correlated ones that collectively flag anomalies. In this paper, we propose such techniques for the detection of anomalous accesses to relational databases. Our approach is to monitor users' queries, sequences of queries and sessions of database connection to detect queries that retrieve amounts of data larger than the normal. Our evaluation of the proposed techniques indicates that they are very effective in the detection of anomalies.
Ramachandran, Raji, Nidhin, R, Shogil, P P.  2018.  Anomaly Detection in Role Administered Relational Databases — A Novel Method. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). :1017–1021.
A significant amount of attempt has been lately committed for the progress of Database Management Systems (DBMS) that ensures high assertion and high security. Common security measures for database like access control measures, validation, encryption technologies, etc are not sufficient enough to secure the data from all the threats. By using an anomaly detection system, we are able to enhance the security feature of the Database management system. We are taking an assumption that the database access control is role based. In this paper, a mechanism is proposed for finding the anomaly in database by using machine learning technique such as classification. The importance of providing anomaly detection technique to a Role-Based Access Control database is that it will help for the protection against the insider attacks. The experimentation results shows that the system is able to detect intrusion effectively with high accuracy and high F1-score.
Tufail, Hina, Zafar, Kashif, Baig, Rauf.  2018.  Digital Watermarking for Relational Database Security Using mRMR Based Binary Bat Algorithm. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1948–1954.
Publically available relational data without security protection may cause data protection issues. Watermarking facilitates solution for remote sharing of relational database by ensuring data integrity and security. In this research, a reversible watermarking for numerical relational database by using evolutionary technique has been proposed that ensure the integrity of underlying data and robustness of watermark. Moreover, mRMR based feature subset selection technique has been used to select attributes for implementation of watermark instead of watermarking whole database. Binary Bat algorithm has been used as constraints optimization technique for watermark creation. Experimental results have shown the effectiveness of the proposed technique against data tempering attacks. In case of alteration attacks, almost 70% data has been recovered, 50% in deletion attacks and 100% data is retrieved after insertion attacks. The watermarking based on evolutionary technique (WET) i.e., mRMR based Binary Bat Algorithm ensures the data accuracy and it is resilient against malicious attacks.