The black-box nature of clouds introduces a lack of trusts in clouds. Since provenance can provide a complete history of an entity, trustworthy provenance management for data, application, or workflow can make the cloud more accountable. Current research on cloud provenance mainly focuses on collecting provenance records and trusting the cloud providers in managing the provenance records. However, a dishonest cloud provider can alter the provenance records, as the records are stored within the control of the cloud provider. To solve this problem, we first propose CloProv - a provenance model to capture the complete provenance of any type of entities in the cloud. We analyze the threats on the CloProv model considering collusion among malicious users and dishonest cloud providers. Based on the threat model, we propose a secure data provenance scheme - SECProv for cloud-based, multi-user, shared data storage systems. We integrate SECProv with the object storage module of an open source cloud framework - OpenStack Swift and analyze the efficiency of the proposed scheme.

Provenance for an application can provide important insights about the behavior and life cycle of the application. However, currently, there are no provenance management systems which collect and preserve provenance records securely for the applications running in a virtual machine hosted on the cloud. Moreover, the black-box nature of clouds and the possibility of cloud providers being malicious can make the secure application provenance management challenging. In this paper, we analyze the threats on trustworthy application provenance in the context of clouds while considering the collusion between users and cloud providers. Based on this threat model, we present the SECure Application Provenance (SECAP) scheme, which ensures the required integrity and confidentiality properties for application provenance efficiently. We evaluate the performance of the SECAP scheme on an OpenStack-based cloud. The experimental results suggest that SECAP performs better than several other state-of-the-art secure provenance schemes in terms of time and space requirements.

The availability of wireless interfaces with the new generation medical devices has spawned numerous opportunities in providing better healthcare support to patients. However, the weaknesses of available wireless communication channels introduce various novel attacks on the medical devices. Since the smart mobile devices, such as smartphones, tablets, laptops are also equipped with the same communication channels (WiFi/Bluetooth), attacks on medical devices can be initiated from a compromised or malware infected mobile device. Attackers can steal confidential medical records from a wireless-enabled medical device. Medical devices or communication channels can also be compromised to feed incorrect medical records to doctors or send life threatening commands to the devices. Moreover, since the compromised mobile devices are already inside the security perimeter of a healthcare network, it is very challenging to block attacks from such compromised mobile devices. In this paper, we systematically analyze the novel threats on healthcare devices and networks, which can be initiated from compromised mobile devices. We provide a detail audit guideline to evaluate the security strength of a healthcare network. Based on our proposed guideline, we evaluate the current security state of a large university healthcare facility. We also propose several mitigation strategies to mitigate some of the possible attacks.

In digital forensics investigations, the system time of computing resources can provide critical information to implicate or exonerate a suspect. In clouds, alteration of the system time of a virtual machine (VM) or a cloud host machine can provide unreliable time information, which in turn can mislead an investigation in the wrong direction. In this paper, we propose Chronos to secure the system time of cloud hosts and VMs in an untrusted cloud environment. Since it is not possible to prevent a malicious user or a dishonest insider of a cloud provider from altering the system time of a VM or a host machine, we propose a tamper-evident scheme to detect this malicious behavior at the time of investigation. We integrate Chronos with an open-source cloud platform - OpenStack and evaluate the feasibility of Chronos while running 20 VMs on a single host machine. Our test results suggest that Chronos can be easily deployed in the existing cloud with very low overheads, while achieving a high degree of trustworthiness of the system time of the cloud hosts and VMs.

Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users' privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree, which performs better than the other two accumulators in terms of time and space requirement.

The rise of cloud computing has changed the way computing services and resources are used. However, existing digital forensics science cannot cope with the black-box nature of clouds nor with multi-tenant cloud models. Because of the fundamental characteristics of clouds, many assumptions of digital forensics are invalidated in clouds. In the digital forensics process involving clouds, the role of cloud service providers (CSP) is utterly important, a role which needs to be considered in the science of cloud forensics. In this paper, we define cloud forensics considering the role of the CSP and propose the Open Cloud Forensics (OCF) model. Based on this OCF model, we propose a cloud computing architecture and validate our proposed model using a case study, which is inspired from an actual civil lawsuit.

URL blacklisting is a widely used technique for blocking phishing websites. To prepare an effective blacklist, it is necessary to analyze possible threats and include the identified malicious sites in the blacklist. Spam emails are good source for acquiring suspected phishing websites. However, the number of URLs gathered from spam emails is quite large. Fetching and analyzing the content of this large number of websites are very expensive tasks given limited computing and storage resources. Moreover, a high percentage of URLs extracted from spam emails refer to the same website. Hence, preserving the contents of all the websites causes significant storage waste. To solve the problem of massive computing and storage resource requirements, we propose and develop CURLA - a Cloud-based spam URL Analyzer, built on top of Amazon Elastic Computer Cloud (EC2) and Amazon Simple Queue Service (SQS). CURLA allows processing large number of spam-based URLs in parallel, which reduces the cost of establishing equally capable local infrastructure. Our system builds a database of unique spam-based URLs and accumulates the content of these unique websites in a central repository, which can be later used for phishing or other counterfeit websites detection. We show the effectiveness of our proposed architecture using real-life spam-based URL data.

Personal mobile devices and location based services are gaining popularity every day. Since the location based services are often customized based on the location information, it is important to securely generate, preserve, and validate the claim of presence at a given location at a given time as well as location provenance - the history of locations for a mobile device user over a given time period. Location provenance needs to imply secure and chronological ordering of location proofs, which can be successfully verified at a later time. Otherwise, the location based services can be easily spoofed by falsified location history. In this paper, we present OTIT - a model for designing secure location provenance. We formalized the features and characteristics for the domain of secure location provenance schemes, using formal propositional logic and logical proofs. We also present several schemes, which can be used in various modes to provide secure location provenance services. Based on the characteristics defined in OTIT, we have analyzed different schemes to show their adherence to the desired features of secure location provenance. Furthermore, we present experimental results on the performance of the various schemes, in terms of time and storage, to show a comparative applicability analysis. We posit that OTIT will serve as a comprehensive benchmark framework to evaluate the models for secure location provenance.

The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and perhaps more severe, possibilities for attacks. In this paper, we explore a novel generation of mobile malware that exploits the rich variety of sensors available on current mobile devices.

Cloud computing has emerged as a popular computing paradigm in recent years. However, today's cloud computing architectures often lack support for computer forensic investigations. Analyzing various logs (e.g., process logs, network logs) plays a vital role in computer forensics. Unfortunately, collecting logs from a cloud is very hard given the black-box nature of clouds and the multi-tenant cloud models, where many users share the same processing and network resources. Researchers have proposed using log API or cloud management console to mitigate the challenges of collecting logs from cloud infrastructure. However, there has been no concrete work, which shows how to provide cloud logs to investigator while preserving users' privacy and integrity of the logs. In this paper, we introduce Secure-Logging-as-a-Service (SecLaaS), which stores virtual machines' logs and provides access to forensic investigators ensuring the confidentiality of the cloud users. Additionally, SeclaaS preserves proofs of past log and thus protects the integrity of the logs from dishonest investigators or cloud providers. Finally, we evaluate the feasibility of the scheme by implementing SecLaaS for network access logs in OpenStack -- a popular open source cloud platform.