Unauthorized Queries Research Articles

Fraud Detection in NoSQL Database Systems using Advanced Machine Learning

NoSQL databases such as MongoDB and Cassandra have been rapidly adopted in recent years because of their high performance, flexibility, and scalability. These databases present new security issues compared to SQL databases. NoSQL databases are vulnerable to fraud, intrusions and data breaches due to their dynamic schemas, lack of control over access and the focus on availability. This paper examines how advanced machine-learning techniques can be used to enhance fraud and intrusion detection in NoSQL databases. We examine different machine-learning algorithms, including neural networks and support vector machines. Random forests, clustering, and random forests can be used to analyze large databases activity logs in order to identify anomalous patterns of access indicative of malicious behavior. We examine how these models are trained online to detect emerging threats, and we validate the techniques using proof-of concept experiments on a prototype NoSQL based database. Our results show high accuracy for detecting injection attacks, unauthorized query, and abnormal database traffic, with low false-positive rates.

Privacy-aware document retrieval with two-level inverted indexing

Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverted index structure. In this scheme, posting records are grouped with bucket tags and runtime query processing produces query-specific tags in order to gather encoded features of matched documents with a privacy protection during index traversal. To thwart leakage-abuse attacks, our design minimizes the chance that a server processes unauthorized queries or identifies document sharing across posting lists through index inspection or across-query association. This paper presents the evaluation and analytic results of the proposed scheme to demonstrate the tradeoffs in its design considerations for privacy, efficiency, and relevance.

Multi-Key Similar Data Search on Encrypted Storage With Secure Pay-Per-Query

Many commercial cloud service providers (CSPs) adopt pay-per-query pricing models, in which data owners are charged based on the amount of data scanned by each query. In such a data sharing model, not only the privacy preservation for the data and queries but also the trustworthiness of the underlying billing system is of the utmost importance. In this paper, we revisit multi-key searchable encryption (MKSE), an efficient and secure data search algorithm allowing a data owner to grant users the ability to retrieve data of interest over the outsourced, encrypted datasets. We first investigate which factor in existing MKSE schemes renders authorized users over-privileged such that, without risking their credits (e.g., leaking the private keys and/or the passwords for their accounts associated with a project where the shared data resides), they can allow unauthorized users to make valid queries. Unfortunately, this concern may be devastating because the queries made by unauthorized users would incur unexpected financial damage to the owner in practical pay-per-query models. We then propose a novel multi-key data search scheme that is resilient to unauthorized queries. The proposed scheme features a novel user authorization mechanism that carefully limits user privilege such that even an authorized user cannot illegally invite unauthorized users to query unless he entirely leaks his credit. We demonstrate the proposed scheme is comparable to prior work in terms of performance while achieving a higher level of security.

Delegatable Order-Revealing Encryption for Reliable Cross-Database Query

Cloud service providers adopt pay-per-query pricing models to charge data owners based on the amount of data scanned by each query. In such models, the trustworthiness of the underlying billing system is as important as the privacy preservation for the data and queries. In this paper, we revisit delegatable order-revealing encryption (DORE), a range query algorithm allowing authorized users to retrieve data of specific ranges across multiple databases encrypted under different secret keys. We first investigate which factor in the authorization mechanism of DORE can lead to overprivileged users and let them allow any unauthorized user to query over the database of the victim without risking their credits, such as leaking the secret keys. Unfortunately, such unauthorized queries would incur unexpected financial damage to the victim in practical pay-per-query models. We then propose SEDORE, a secure order-revealing encryption scheme with resilience to unauthorized queries across databases. SEDORE features a novel user authorization mechanism limiting user privileges carefully. Consequently, the authorized users cannot illegally invite any unauthorized user to query unless they entirely leak their credits. We demonstrate that the performance of SEDORE is comparable to that of DORE while achieving a higher security level.

Deciding Schema <i>k</i>-Secrecy for XML Databases

We study a static analysis problem on k-secrecy, which is a metric for the security against inference attacks on XML databases. Intuitively, k-secrecy means that the number of candidates of sensitive data of a given database instance or the result of unauthorized query cannot be narrowed down to k-1 by using available information such as authorized queries and their results. In this paper, we investigate the decidability of the schema k-secrecy problem defined as follows: for a given XML database schema, an authorized query and an unauthorized query, decide whether every database instance conforming to the given schema is k-secret. We first show that the schema k-secrecy problem is undecidable for any finite k>1 even when queries are represented by a simple subclass of linear deterministic top-down tree transducers (LDTT). We next show that the schema ∞-secrecy problem is decidable for queries represented by LDTT. We give an algorithm for deciding the schema ∞-secrecy problem and analyze its time complexity. We show the schema ∞-secrecy problem is EXPTIME-complete for LDTT. Moreover, we show similar results LDTT with regular look-ahead.

Model Based Approach to Prevent SQL Injection Attacks on .NET Applications

Web applications support static and dynamic queries to access the database. Dynamic queries take input from the user and use that input to form the query. A user can give malicious input to the application which results in an incorrect query or an unauthorized query and performs vulnerable action on the database. In this paper, we presented an approach to prevent SQL injection attack (SQLIA) on .Net applications using static and dynamic analysis of the queries. The paper explains comparison of Dynamic query model and static query model in order to validate the query before sending it to the database. The result obtained proves that our designed tool has achieved prevention from SQL injection at greater extend.

Security against Inference Attacks on Negative Information in Object-Oriented Databases

Inference attacks mean that a user derives information on the execution results of unauthorized queries from the execution results of authorized queries. Most of the studies on inference attacks so far have focused on only inference of positive information (i.e., what value is the execution result of a given unauthorized query). However, negative information (i.e., what value is never the execution result of a given unauthorized query) is also sensitive in many cases. This paper presents the following results on the security against inference attacks on negative information in object-oriented databases. First, inference of negative information is formalized under a model of object-oriented databases called method schemas. Then, the following two types of security problems are defined: (1) Is a given database instance secure against inference attacks on given negative information? (2) Are all of the database instances of a given database schema secure against inference attacks on given negative information? It is shown that the first problem is decidable in polynomial time in the description size of the database instance while the second one is undecidable. A decidable sufficient condition for any database instance of a given database schema to be secure is also proposed. Finally, it is shown that for a monadic schema (i.e., every method has exactly one parameter), this sufficient condition is also a necessary one.

A General Additive Data Perturbation Method for Database Security

The security of organizational databases has received considerable attention in the literature in recent years. This can be attributed to a simultaneous increase in the amount of data being stored in databases, the analysis of such data, and the desire to protect confidential data. Data perturbation methods are often used to protect confidential, numerical data from unauthorized queries while providing maximum access and accurate information to legitimate queries. To provide accurate information, it is desirable that perturbation does not result in a change in relationships between attributes. In the presence of nonconfidential attributes, existing methods will result in such a change. This study describes a new method (General Additive Data Perturbation) that does not change relationships between attributes. All existing methods of additive data perturbation are shown to be special cases of this method. When the database has a multivariate normal distribution, the new method provides maximum security and minimum bias. For nonnormal databases, the new method provides better security and bias performance than the multiplicative data perturbation method.