Ashish Kamra

Efficient k-anonymization using clustering techniques

k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n2). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data.

Detecting anomalous access patterns in relational databases

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance and security. An important component of any strong security solution is represented by Intrusion Detection (ID) techniques, able to detect anomalous behavior of applications and users. To date, however, there have been few ID mechanisms proposed which are specifically tailored to function within the DBMS. In this paper, we propose such a mechanism. Our approach is based on mining SQL queries stored in database audit log files. The result of the mining process is used to form profiles that can model normal database access behavior and identify intruders. We consider two different scenarios while addressing the problem. In the first case, we assume that the database has a Role Based Access Control (RBAC) model in place. Under a RBAC system permissions are associated with roles, grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals while holding a specific role, behave differently than expected. An important advantage of providing an ID technique specifically tailored to RBAC databases is that it can help in protecting against insider threats. Furthermore, the existence of roles makes our approach usable even for databases with large user population. In the second scenario, we assume that there are no roles associated with users of the database. In this case, we look directly at the behavior of the users. We employ clustering algorithms to form concise profiles representing normal user behavior. For detection, we either use these clustered profiles as the roles or employ outlier detection techniques to identify behavior that deviates from the profiles. Our preliminary experimental evaluation on both real and synthetic database traces shows that our methods work well in practical situations.

Intrusion detection in RBAC-administered databases

A considerable effort has been recently devoted to the development of database management systems (DBMS) which guarantee high assurance security and privacy. An important component of any strong security solution is represented by intrusion detection (ID) systems, able to detect anomalous behavior by applications and users. To date, however, there have been very few ID mechanisms specifically tailored to database systems. In this paper, we propose such a mechanism. The approach we propose to ID is based on mining database traces stored in log files. The result of the mining process is used to form user profiles that can model normal behavior and identify intruders. An additional feature of our approach is that we couple our mechanism with role based access control (RBAC). Under a RBAC system permissions are associated with roles, usually grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals that while holding a specific role, have a behavior different from the normal behavior of the role. An important advantage of providing an ID mechanism specifically tailored to databases is that it can also be used to protect against insider threats. Furthermore, the use of roles makes our approach usable even for databases with large user population. Our preliminary experimental evaluation on both real and synthetic database traces show that our methods work well in practical situations

Access Control for Databases: Concepts and Systems

As organizations depend on, possibly distributed, information systems for operational, decisional and strategic activities, they are vulnerable to security breaches leading to data theft and unauthorized disclosures even as they gain productivity and efficiency advantages. Though several techniques, such as encryption and digital signatures, are available to protect data when transmitted across sites, a truly comprehensive approach for data protection must include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. To address such requirements, over the years the database security research community has developed a number of access control techniques and mechanisms that are specific to database systems. In this monograph, we present a comprehensive state of the art about models, systems and approaches proposed for specifying and enforcing access control policies in database management systems. In addition to surveying the foundational work in the area of access control for database systems, we present extensive case studies covering advanced features of current database management systems, such as the support for fine-grained and context-based access control, the support for mandatory access control, and approaches for protecting the data from insider threats. The monograph also covers novel approaches, based on cryptographic techniques, to enforce access control and surveys access control models for object-databases and XML data. For the reader not familiar with basic notions concerning access control and cryptography, we include a tutorial presentation on these notions. Finally, the monograph concludes with a discussion on current challenges for database access control and security, and preliminary approaches addressing some of these challenges.

Design and Implementation of an Intrusion Response System for Relational Databases

The intrusion response component of an overall intrusion detection system is responsible for issuing a suitable response to an anomalous request. We propose the notion of database response policies to support our intrusion response system tailored for a DBMS. Our interactive response policy language makes it very easy for the database administrators to specify appropriate response actions for different circumstances depending upon the nature of the anomalous request. The two main issues that we address in context of such response policies are that of policy matching, and policy administration. For the policy matching problem, we propose two algorithms that efficiently search the policy database for policies that match an anomalous request. We also extend the PostgreSQL DBMS with our policy matching mechanism, and report experimental results. The experimental evaluation shows that our techniques are very efficient. The other issue that we address is that of administration of response policies to prevent malicious modifications to policy objects from legitimate users. We propose a novel Joint Threshold Administration Model (JTAM) that is based on the principle of separation of duty. The key idea in JTAM is that a policy object is jointly administered by at least k database administrator (DBAs), that is, any modification made to a policy object will be invalid unless it has been authorized by at least k DBAs. We present design details of JTAM which is based on a cryptographic threshold signature scheme, and show how JTAM prevents malicious modifications to policy objects from authorized users. We also implement JTAM in the PostgreSQL DBMS, and report experimental results on the efficiency of our techniques.

Profiling Database Application to Detect SQL Injection Attacks

Countering threats to an organizations internal databases from database applications is an important area of research. In this paper, we propose a novel framework based on anomaly detection techniques, to detect malicious behaviour of database application programs. Specifically, we create a fingerprint of an application program based on SQL queries submitted by it to a database. We then use association rule mining techniques on this fingerprint to extract useful rules. These rules succinctly represent the normal behaviour of the database application. We then apply an anomaly detection algorithm to detect queries that do not conform to these rules. We further demonstrate how this model can be used to detect SQL Injection attacks on databases. We show the validity and usefulness of our approach on synthetically generated datasets and SQL Injected queries. Experimental results show that our techniques are effective in addressing various types of SQL Injection threat scenarios.

Mechanisms for database intrusion detection and response

Data represent today a valuable asset for companies and organizations and must be protected. Most of an organizations sensitive and proprietary data resides in a Database Management System (DBMS). The focus of this thesis is to develop advanced security solutions for protecting the data residing in a DBMS. Our strategy is to develop an Intrusion Detection (ID) mechanism, implemented within the database server, that is capable of detecting anomalous user requests to a DBMS. The key idea is to learn profiles of users and applications interacting with a database. A database request that deviates from these profiles is then termed as anomalous. A major component of this work involves prototype implementation of this ID mechanism in the Post-greSQL database server. We also propose to augment the ID mechanism with an Intrusion Response engine that is capable of issuing an appropriate response to an anomalous database request.

ViWiD: visible watermarking based defense against phishing

In this paper, we present a watermarking based approach, and its implementation, for mitigating phishing attacks – a form of web based identity theft. ViWiD is an integrity check mechanism based on visible watermarking of logo images. ViWiD performs all of the computation on the company’s web server and it does not require installation of any tool or storage of any data, such as keys or history logs, on the user’s machine. The watermark message is designed to be unique for every user and carries a shared secret between the company and the user in order to thwart the “one size fits all” attacks. The main challenge in visible watermarking of logo images is to maintain the aesthetics of the watermarked logo to avoid damage to its marketing purpose yet be able to insert a robust and readable watermark into it. Logo images have large uniform areas and very few objects in them, which is a challenge for robust visible watermarking. We tested our scheme with two different visible watermarking techniques on various randomly selected logo images.

Responding to Anomalous Database Requests

Organizations have recently shown increased interest in database activity monitoring and anomaly detection techniques to safeguard their internal databases. Once an anomaly is detected, a response from the database is needed to contain the effects of the anomaly. However, the problem of issuing an appropriate response to a detected database anomaly has received little attention so far. In this paper, we propose a framework and policy language for issuing a response to a database anomaly based on the characteristics of the anomaly. We also propose a novel approach to dynamically change the state of the access control system in order to contain the damage that may be caused by the anomalous request. We have implemented our mechanisms in PostgreSQL and in the paper we discuss relevant implementation issues. We have also carried out an experimental evaluation to assess the performance overhead introduced by our response mechanism. The experimental results show that the techniques are very efficient.

Privilege states based access control for fine-grained intrusion response

We propose an access control model specifically developed to support fine-grained response actions, such as request suspension and request tainting, in the context of an anomaly detection system for databases. To achieve such response semantics, the model introduces the concept of privilege states and orientation modes in the context of a role-based access control system. The central idea in our model is that privileges, assigned to a user or role, have a state attached to them, thereby resulting in a privilege states based access control (PSAC) system. In this paper, we present the design details and a formal model of PSAC tailored to database management systems (DBMSs). PSAC has been designed to also take into account role hierarchies that are often present in the access control models of current DBMSs. We have implemented PSAC in the PostgreSQL DBMS and in the paper, we discuss relevant implementation issues. We also report experimental results concerning the overhead of the access control enforcement in PSAC. Such results confirm that our design and algorithms are very efficient.