The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and per- haps more severe, possibilities for attacks. In this project, we explore a novel generation of mobile malware called the Manchurian Malware. It exploits the rich variety of sensors available on current mobile devices.
Two properties distinguish the proposed malware from the existing state-of-the-art. First, in addition to the misuse of the various traditional services available on modern mobile devices, this malware can be used for the purpose of targeted context-aware attacks.Second such a malware can be commanded and controlled over context-aware, out-of-band channels as opposed to a centralized infrastructure. These communication channels can be used to reach out to a large number of infected devices, while remaining covert. To demonstrate the feasibility of the Manchurian Malware, we have designed different flavors of command and control channels based on acoustic, visual and magnetic signalling. We further built a proof-of- concept Android application implementing many such channels.
This is a joint project led by Dr. Ragib Hasan of UAB SECRETLab and Dr. Nitesh Saxena of SPIES Lab.
While the cloud computing paradigm gains more popularity, there are many unresolved issues related to confidentiality, integrity, and availability of data and computations involving a cloud. In this project, we examine cloud computing models, look into the threat model and security issues related to data and computation outsourcing, and explore practical applications of secure cloud computing.
Secure Location Provenance
With the advent of mobile computing, location-based services have recently gained popularity. Many applications use the location provenance of users, i.e., the chronological history of the users’ location for purposes ranging from access control, authentication, information sharing, and evaluation of policies. However, location provenance is subject to tampering and collusion attacks by malicious users. In this paper, we examine the secure location provenance problem. We introduce a witness-endorsed scheme for generating collusion-resistant location proofs. We also describe two efficient and privacy-preserving schemes for protecting the integrity of the chronological order of location proofs. These schemes, based on hash chains and Bloom filters respectively, allow users to prove the order of any arbitrary subsequence of their location history to auditors. Finally, we present experimental results from our proof-of-concept implementation on the Android platform and show that our schemes are practical in today’s mobile devices.
Secure Data Provenance
Provenance is the documented history of an object, in other words, how the object was created, modified, propagated, and disseminated to its current location/status. By looking into the provenance of an object, we can infer the trustworthiness of the object.
As increasing amounts of valuable information are produced and persist digitally, the ability to determine the origin of data becomes important. In science, medicine, commerce, and government, data provenance tracking is essential for rights protection, regulatory compliance, management of intelligence and medical data, and authentication of information as it flows through workplace tasks. While significant research has been conducted in this area, the associated security and privacy issues have not been explored, leaving provenance information vulnerable to illicit alteration as it passes through untrusted environments.
In this project, we show how to provide strong integrity and confidentiality assurances for data provenance information at the kernel, file system, or application layer. We have created a provenance-aware system prototype that implements provenance tracking of data writes at the application layer, which makes it extremely easy to deploy. Experimental results that show that, for real-life workloads, the runtime overhead of our approach to recording provenance with confidentiality and integrity guarantees are low, often less than 1%- 12% depending on optimizations.
Data Waste Management
Our everyday data processing activities create massive amounts of data. Like physical waste and trash, unwanted and unused data also pollutes the digital environment by degrading the performance and capacity of storage systems and requiring costly disposal. In this project, we propose using the lessons from real life waste management in handling waste data. We show the impact of waste data on the performance and operational costs of our computing systems. To allow better waste data management, we define a waste hierarchy for digital objects and provide insights into how to identify and categorize waste data. Finally, we introduce novel ways of reusing, reducing, and recycling data and software to minimize the impact of data wastage.