Biblio

This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.

Computer and network accountability is to make every action in computers and networks accountable. In order to achieve accountability, we need to answer the following questions: what did it happen? When did it happen? Who did it? In order to achieve accountability, the first step is to record what exactly happened. Therefore, an accountable logging is needed and implemented in computers and networks. Our previous work proposed a novel accountable logging methodology called Flow-Net. However, how to storage the huge amount of Flow-net logs into databases is not clear. In this paper, we try to answer this question.

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.

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.

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.

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.

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.

Nowadays the massive amount of data is produced from different sources and lots of applications are processing these data to discover insights. Sometimes we may get unexpected results from these applications and it is not feasible to trace back to the data origin manually to find the source of errors. To avoid this problem, data must be accompanied by the context of how they are processed and analyzed. Especially, data-intensive applications like e-Science always require transparency and therefore, we need to understand how data has been processed and transformed. In this paper, we propose mathematical formulation and implementation of query inversion techniques to trace the provenance of data in a relational database management system (RDBMS). We build mathematical formulations of inverse queries for most of the relational algebra operations and show the formula for join operations in this paper. We, then, implement these formulas of inversion techniques and the experiment shows that our proposed inverse queries can successfully trace back to original data i.e. finding data provenance.

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.

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.

The new generation ship integrated power system (IPS) realizes high level informatization for various physical equipments, and gradually develops to a cyber-physical system (CPS). The future trend is collecting ship big data to achieve data-driven intelligence for IPS. However, traditional relational data management framework becomes inefficient to handle the real-time data processing in ship integrated power cyber-physics system. In order to process the large-scale real-time data that collected from numerous sensors by field bus of IPS devices within acceptable latency, especially for handling the semi-structured and non-structured data. This paper proposes a novel key-value data model based real-time data management framework, which enables batch processing and distributed deployment to acquire time-efficiency as well as system scalable. We implement a real-time data management prototype system based on an open source in-memory key-value store. Finally, the evaluation results from the prototype verify the advantages of novel framework compared with traditional solution.

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.

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.

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.

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.

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.

Cloud computing, cloud security and cloud storage have been gradually introduced into people's life and become hot topicsof research, for which relevant technologies have permeated through the computer industry and relevant industries. With the coming of the modern information society, secure storage of data has been becoming increasingly important. Proceeding from traditional policy storage, this paper includes comparison and improvement of policy storage for the purpose of meeting requirements of storage of cloud security policy. Policy storage technology refers to a technology used to realize storage of policies created by users and relevant policy information. Policy repository can conduct centralized management and processing of multiple policies and their relevant information. At present, popular policy repositories generally include policy storage for relational database or policy storage for directory server or a file in a fixed format, such as XML file format.

This paper proposes a framework for predicting and mitigating insider collusion threat in relational database systems. The proposed model provides a robust technique for database architect and administrators to predict insider collusion threat when designing database schema or when granting privileges. Moreover, it proposes a real time monitoring technique that monitors the growing knowledgebases of insiders while executing transactions and the possible collusion insider attacks that may be launched based on insiders accesses and inferences. Furthermore, the paper proposes a mitigating technique based on the segregation of duties principle and the discovered collusion insider threat to mitigate the problem. The proposed model was tested to show its usefulness and applicability.

Parfait [1] is a static analysis tool originally developed to find implementation defects in C/C++ systems code. Parfait's focus is on proving both high precision (low false positives) as well as scaling to systems with millions of lines of code (typically requiring 10 minutes of analysis time per million lines). Parfait has since been extended to detect security vulnerabilities in applications code, supporting the Java EE and PL/SQL server stack. In this abstract we describe some of the challenges we encountered in this process including some of the differences seen between the applications code being analysed, our solutions that enable us to analyse a variety of applications, and a summary of the challenges that remain.

This paper addresses the problem of querying Knowledge bases (KBs) that store semantic big data. For efficiently querying data the most important factor is cache replacement policy, which determines the overall query response. As cache is limited in size, less frequently accessed data should be removed to provide more space to hot triples (frequently accessed). So, to achieve a similar performance to RDBMS, we proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Moreover, performance bottleneck of triplestore, makes realworld application difficult. To achieve a closer performance similar to RDBMS, we have proposed an Adaptive Cache Replacement (ACR) policy that predict the hot triples from query log. Our proposed algorithm effectively replaces cache with high accuracy. To implement cache replacement policy, we have applied exponential smoothing, a forecast method, to collect most frequently accessed triples. The evaluation result shows that the proposed scheme outperforms the existing cache replacement policies, such as LRU (least recently used) and LFU (least frequently used), in terms of higher hit rates and less time overhead.

It is a well-known fact that nowadays access to sensitive information is being performed through the use of a three-tier-architecture. Web applications have become a handy interface between users and data. As database-driven web applications are being used more and more every day, web applications are being seen as a good target for attackers with the aim of accessing sensitive data. If an organization fails to deploy effective data protection systems, they might be open to various attacks. Governmental organizations, in particular, should think beyond traditional security policies in order to achieve proper data protection. It is, therefore, imperative to perform security testing and make sure that there are no holes in the system, before an attack happens. One of the most commonly used web application attacks is by insertion of an SQL query from the client side of the application. This attack is called SQL Injection. Since an SQL Injection vulnerability could possibly affect any website or web application that makes use of an SQL-based database, the vulnerability is one of the oldest, most prevalent and most dangerous of web application vulnerabilities. To overcome the SQL injection problems, there is a need to use different security systems. In this paper, we will use 3 different scenarios for testing security systems. Using Penetration testing technique, we will try to find out which is the best solution for protecting sensitive data within the government network of Kosovo.

The mitigation of insider threats against databases is a challenging problem as insiders often have legitimate access privileges to sensitive data. Therefore, conventional security mechanisms, such as authentication and access control, may be insufficient for the protection of databases against insider threats and need to be complemented with techniques that support real-time detection of access anomalies. The existing real-time anomaly detection techniques consider anomalies in references to the database entities and the amounts of accessed data. However, they are unable to track the access frequencies. According to recent security reports, an increase in the access frequency by an insider is an indicator of a potential data misuse and may be the result of malicious intents for stealing or corrupting the data. In this paper, we propose techniques for tracking users' access frequencies and detecting anomalous related activities in real-time. We present detailed algorithms for constructing accurate profiles that describe the access patterns of the database users and for matching subsequent accesses by these users to the profiles. Our methods report and log mismatches as anomalies that may need further investigation. We evaluated our techniques on the OLTP-Benchmark. The results of the evaluation indicate that our techniques are very effective in the detection of anomalies.

Volumetric DDoS attacks continue to inflict serious damage. Many proposed defenses for mitigating such attacks assume that a monitoring system has already detected the attack. However, many proposed DDoS monitoring systems do not focus on efficiently analyzing high volume network traffic to provide important characterizations of the attack in real-time to downstream traffic filtering systems. We propose a scalable real-time framework for an effective volumetric DDoS monitoring system that leverages modern big data technologies for streaming analytics of high volume network traffic to accurately detect and characterize attacks.

In cyberspace, unknown zero-day attacks can bring safety hazards. Traditional defense methods based on signatures are ineffective. Based on the Cyberspace Mimic Defense (CMD) architecture, the paper proposes a framework to detect the attacks and respond to them. Inputs are assigned to all online redundant heterogeneous functionally equivalent modules. Their independent outputs are compared and the outputs in the majority will be the final response. The abnormal outputs can be detected and so can the attack. The damaged executive modules with abnormal outputs will be replaced with new ones from the diverse executive module pool. By analyzing the abnormal outputs, the correspondence between inputs and abnormal outputs can be built and inputs leading to recurrent abnormal outputs will be written into the zero-day attack related database and their reuses cannot work any longer, as the suspicious malicious inputs can be detected and processed. Further responses include IP blacklisting and patching, etc. The framework also uses honeypot like executive module to confuse the attacker. The proposed method can prevent the recurrent attack based on the same exploit.

Being an era of fast internet-based application environment, large volumes of relational data are being outsourced for business purposes. Therefore, ownership and digital rights protection has become one of the greatest challenges and among the most critical issues. This paper presents a novel fingerprinting technique to protect ownership rights of non-numeric digital data on basis of pattern generation and row association schemes. Firstly, fingerprint sequence is formulated by using secret key and buyer's Unique ID. With the chunks of these sequences and by applying the Fibonacci series, we select some rows. The selected rows are candidates of fingerprinting. The primary key of selected row is protected using RSA encryption; after which a pattern is designed by randomly choosing the values of different attributes of datasets. The encryption of primary key leads to develop an association between original and fake pattern; creating an ease in fingerprint detection. Fingerprint detection algorithm first finds the fake rows and then extracts the fingerprint sequence from the fake attributes, hence identifying the traitor. Some most important features of the proposed approach is to overcome major weaknesses such as error tolerance, integrity and accuracy in previously proposed fingerprinting techniques. The results show that technique is efficient and robust against several malicious attacks.