Biblio

For a long time, SQL injection has been considered one of the most serious security threats. NoSQL databases are becoming increasingly popular as big data and cloud computing technologies progress. NoSQL injection attacks are designed to take advantage of applications that employ NoSQL databases. NoSQL injections can be particularly harmful because they allow unrestricted code execution. In this paper we use supervised learning and natural language processing to construct a model to detect NoSQL injections. Our model is designed to work with MongoDB, CouchDB, CassandraDB, and Couchbase queries. Our model has achieved an F1 score of 0.95 as established by 10-fold cross validation.

Privacy and data security are critical aspects in databases, mainly when the latter are publically accessed such in social networks. Furthermore, for advanced databases, such as NoSQL ones, security models and security meta-data must be integrated to the business specification and data. In the literature, the proposed models for NoSQL databases can be considered as static, in the sense where the privileges for a given user are predefined and remain unchanged during job sessions. In this paper, we propose a novel model for NoSQL database access control that we aim that it will be dynamic. To be able to design such model, we have considered the Trust concept to compute the reputation degree for a given user that plays a given role.

The advancement of technology in the creation of new tools to solve problems such as information storage generates proportionally developing methods that search for security flaws or breaches that compromise said information. The need to periodically generate security reports on database managers is given by the complexity and number of attacks that can be carried out today. This project seeks to carry out an evaluation of the security of NoSQL database managers. The work methodology is developed according to the order of the objectives, it begins by synthesizing the types of vulnerabilities, attacks and protection schemes limited to MongoDB, Redis and Apache Cassandra. Once established, a prototype of a web system that stores information with a non-relational database will be designed on which a series of attacks defined by a test plan will be applied seeking to add, consult, modify or eliminate information. Finally, a report will be presented that sets out the attacks carried out, the way in which they were applied, the results, possible countermeasures, security advantages and disadvantages for each manager and the conclusions obtained. Thus, it is possible to select which tool is more convenient to use for a person or organization in a particular case. The results showed that MongoDB is more vulnerable to NoSQL injection attacks, Redis is more vulnerable to attacks registered in the CVE and that Cassandra is more complex to use but is less vulnerable.

The heterogeneous massive logs incoming from multiple sources pose major challenges to professionals responsible for IT security and system administrator. One of the challenges is to develop a scalable heterogeneous logs database for storage and further analysis. In fact, it is difficult to decide which database is suitable for the needs, the best of a use case, execution time and storage performances. In this paper, we explore, study, and compare the performance of SQL and NoSQL databases on large heterogeneous event logs. We implement the relational database using MySQL, the column-oriented database using Impala on the top of Hadoop, and the graph database using Neo4j. We experiment the databases on a large heterogeneous logs and provide advice, the pros and cons of each SQL and NoSQL database. Our findings that Impala outperforms MySQL and Neo4j databases in terms of loading logs, execution time of simple queries, and storage of logs. However, Neo4j outperforms Impala and MySQL in the execution time of complex queries.

Cloud computing, supported by advancements in virtualisation and distributed computing, became the default options for implementing the IT infrastructure of organisations. Medical data and in particular medical images have increasing storage space and remote access requirements. Cloud computing satisfies these requirements but unclear safeguards on data security can expose sensitive data to possible attacks. Furthermore, recent changes in legislation imposed additional security constraints in technology to ensure the privacy of individuals and the integrity of data when stored in the cloud. In contrast with this trend, current data security methods, based on encryption, create an additional overhead to the performance, and often they are not allowed in public cloud servers. Hence, this paper proposes a mechanism that combines data fragmentation to protect medical images on the public cloud servers, and a NoSQL database to secure an efficient organisation of such data. Results of this paper indicate that the latency of the proposed method is significantly lower if compared with AES, one of the most adopted data encryption mechanisms. Therefore, the proposed method is an optimal trade-off in environments with low latency requirements or limited resources.

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.

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.

Security within the IoT is currently below par. Common security issues include IoT device vendors not following security best practices and/or omitting crucial security controls and features within their devices, lack of defined and mandated IoT security standards, default IoT device configurations, missing secure update mechanisms to rectify security flaws discovered in IoT devices and the overall unintended consequence of complexity - the attack surface of networks comprising IoT devices can increase exponentially with the addition of each new device. In this paper we set out an approach using graphs and graph databases to understand IoT network complexity and the impact that different devices and their profiles have on the overall security of the underlying network and its associated data.

User Generated Videos (UGV) are the dominating content stored in scattered caches to meet end-user Content Delivery Networks (CDN) requests with quality of service. End-User behaviour leads to a highly variable UGV popularity. This aspect can be exploited to efficiently utilize the limited storage of the caches, and improve the hit ratio of UGVs. In this paper, we propose a new architecture for Data Analytics in Cloud-based CDN to derive UGVs popularity online. This architecture uses RESTful web services to gather CDN logs, store them through generic collections in a NoSQL database, and calculate related popular UGVs in a real time fashion. It uses a dynamic model training and prediction services to provide each CDN with related popular videos to be cached based on the latest trained model. The proposed architecture is implemented with k-means clustering prediction model and the obtained results are 99.8% accurate.

NoSQL databases have gained a lot of popularity over the last few years. They are now used in many new system implementations that work with vast amounts of data. This data will typically also include sensitive information that needs to be secured. NoSQL databases are also underlying a number of cloud implementations which are increasingly being used to store sensitive information by various organisations. This has made NoSQL databases a new target for hackers and other state sponsored actors. Forensic examinations of compromised systems will need to be conducted to determine what exactly transpired and who was responsible. This paper examines specifically if NoSQL databases have security features that leave relevant traces so that accurate forensic attribution can be conducted. The seeming lack of default security measures such as access control and logging has prompted this examination. A survey into the top ranked NoSQL databases was conducted to establish what authentication and authorisation features are available. Additionally the provided logging mechanisms were also examined since access control without any auditing would not aid forensic attribution tremendously. Some of the surveyed NoSQL databases do not provide adequate access control mechanisms and logging features that leave relevant traces to allow forensic attribution to be done using those. The other surveyed NoSQL databases did provide adequate mechanisms and logging traces for forensic attribution, but they are not enabled or configured by default. This means that in many cases they might not be available, leading to insufficient information to perform accurate forensic attribution even on those databases.

NoSQL databases have become popular with enterprises due to their scalable and flexible storage management of big data. Nevertheless, their popularity also brings up security concerns. Most NoSQL databases lacked secure data encryption, relying on developers to implement cryptographic methods at application level or middleware layer as a wrapper around the database. While this approach protects the integrity of data, it increases the difficulty of executing queries. We were motivated to design a system that not only provides NoSQL databases with the necessary data security, but also supports the execution of query over encrypted data. Furthermore, how to exploit the distributed fashion of NoSQL databases to deliver high performance and scalability with massive client accesses is another important challenge. In this research, we introduce Crypt-NoSQL, the first prototype to support execution of query over encrypted data on NoSQL databases with high performance. Three different models of Crypt-NoSQL were proposed and performance was evaluated with Yahoo! Cloud Service Benchmark (YCSB) considering an enormous number of clients. Our experimental results show that Crypt-NoSQL can process queries over encrypted data with high performance and scalability. A guidance of establishing service level agreement (SLA) for Crypt-NoSQL as a cloud service is also proposed.