Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multidimensional time series and achieved high performance.

Protecting sensitive datasets from insider and outsider attacks has been a major concern over the years. Relational Database Management System (RDBMS) has been the de facto standard to store, retrieve and manage large datasets efficiently in the last few years. However, as surprising as it seems, not a lot of works can be found in the literature which protect databases from anomalous accesses. In this paper, we present a novel Intrusion Detection System (IDS) for relational databases. Our primary objective is to protect databases from both insider and outsider threats by detecting anomalous access patterns using Hidden Markov Model (HMM). While most of the previous notable works in this area focus on query syntax to detect anomalous access, our approach takes into account the amount of sensitive information a query result contains to detect a potential intrusion. Finally, our empirical evaluation on the publicly available TPC-H dataset shows that our IDS can detect anomalous query access with a high degree of accuracy.

Nowadays, huge amount of documents are increasingly transferred to the remote servers due to the appealing features of cloud computing. On the other hand, privacy and security of the sensitive information in untrusted cloud environment is a big concern. To alleviate such concerns, encryption of sensitive data before its transfer to the cloud has become an important risk mitigation option. Encrypted storage provides protection at the expense of a significant increase in the data management complexity. For effective management, it is critical to provide efficient selective document retrieval capability on the encrypted collection. In fact, considerable amount of searchable symmetric encryption schemes have been designed in the literature to achieve this task. However, with the emergence of big data everywhere, available approaches are insufficient to address some crucial real-world problems such as scalability.

Wireless Sensor Networks (WSNs) have a wide range of applications including smart home and environmental monitoring systems. Sensors in such systems gather and propagate critical information to sinks (data collectors). Reliability is a crucial issue in these networks but the wireless medium is subject to interference and noise that can disrupt transmissions. Thus we design a reliable routing topology construction framework for WSNs and evaluate the proposed reliable topologies' performance. We observe that there are tradeoffs of reliability, energy consumption and path properties that applications can take advantage of.

To mitigate security concerns of outsourced databases, quite a few protocols have been proposed that outsource data in encrypted format and allow encrypted query execution on the server side. Among the more practical protocols, the "bucketization" approach facilitates query execution at the cost of reduced efficiency by allowing some false positives in the query results. Precise Query Protocols (PQPs), on the other hand, enable the server to execute queries without incurring any false positives. Even though these protocols do not reveal the underlying data, they reveal query access pattern to an adversary. In this paper, we introduce a general attack on PQPs based on access pattern disclosure in the context of secure range queries. Our empirical analysis on several real world datasets shows that the proposed attack is able to disclose significant amount of sensitive data with high accuracy provided that the attacker has reasonable amount of background knowledge. We further demonstrate that a slight variation of such an attack can also be used on imprecise protocols (e.g., bucketization) to disclose significant amount of sensitive information.

In recent years, database as a service (DAS) model where data management is outsourced to cloud service providers has become more prevalent. Although DAS model offers lower cost and flexibility, it necessitates the transfer of potentially sensitive data to untrusted cloud servers. To ensure the confidentiality, encryption of sensitive data before its transfer to the cloud emerges as an important option. Encrypted storage provides protection but it complicates data processing including crucial selective record retrieval. To achieve selective retrieval over encrypted collection, considerable amount of searchable encryption schemes have been proposed in the literature with distinct privacy guarantees. Among the available approaches, oblivious RAM based ones offer optimal privacy. However, they are computationally intensive and do not scale well to very large databases. On the other hand, almost all efficient schemes leak some information, especially data access pattern to the remote servers. Unfortunately, recent evidence on access pattern leakage indicates that adversary's background knowledge could be used to infer the contents of the encrypted data and may potentially endanger individual privacy.

In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset.

IEEE 802.11 Medium Access Control (MAC) protocol employs two techniques for packet transmission; the basic access scheme and the RTS/CTS-based reservation scheme. A parameter called RTS Threshold determines which scheme to use. If packet size is smaller than the RTS Threshold then the basic scheme is used otherwise the reservation scheme is used. With current standard, the RTS Threshold is fixed. In this paper, we, first point out the advantages and disadvantages of RTS/CTS based scheme. Then we state the problems of having a fixed RTS Threshold. Next, we present a numerical way to fix the RTS Threshold adaptively based on network traffic. The proposed adaptive scheme creates a balance between the basic scheme and the RTS/CTS based scheme and optimizes the network throughput. Considering multi-hop networks with hidden node problems we have validated our proposal through simulation.

Handheld devices in a pervasive computing environment are prone to security as well as privacy violations, while discovering, sharing and accessing services and contents. Trust models are devised to fight against such violations and breaches. Although initial trust assignment is an important issue in evolving overall trust, a little amount of work has been done in this field so far. In pervasive smart space, similar type of contexts exhibits significant correlations to each other. However, this fact is not taken into consideration while computing the initial trust values. In this paper, we describe a new mechanism to assign initial trust: CCTB (Context Correlation for Trust Bootstrapping), which takes advantage of the presence of correlations among different contexts in a context-ontology. We evaluate the effectiveness of CCTB by simulating in two different scenarios. We show that CCTB offers better initial trust values than the other models considered here. We also implement a prototype for performance measurement using .NET Compact Framework.