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Voice delivery over IP networks including VoIP (Voice over IP) and VoLTE (Voice over LTE) are emerging as the alternatives to the conventional public telephony networks. With the growing number of subscribers and the global integration of 4/5G by operations, VoIP/VoLTE as the only option for voice delivery becomes an attractive target to be abused and exploited by malicious attackers. This dissertation aims to address some of the security challenges in VoIP/VoLTE. When we examine the past events to identify trends and changes in attacking strategies, we find that spam calls, caller-ID spoofing, and DoS attacks are the most imminent threats to VoIP deployments. Compared to email spam, voice spam will be much more obnoxious and time consuming nuisance for human subscribers to filter out. Since the threat of voice spam could become as serious as email spam, we first focus on spam detection and propose a content-based approach to protect telephone subscribers' voice mailboxes from voice spam. Caller-ID has long been used to enable the callee parties know who is calling, verify his identity for authentication and his physical location for emergency services. VoIP and other packet switched networks such as all-IP Long Term Evolution (LTE) network provide flexibility that helps subscribers to use arbitrary caller-ID. Moreover, interconnecting between IP telephony and other Circuit-Switched (CS) legacy telephone networks has also weakened the security of caller-ID systems. We observe that the determination of true identity of a calling device helps us in preventing many VoIP attacks, such as caller-ID spoofing, spamming and call flooding attacks. This motivates us to take a very different approach to the VoIP problems and attempt to answer a fundamental question: is it possible to know the type of a device a subscriber uses to originate a call? By exploiting the impreciseness of the codec sampling rate in the caller's RTP streams, we propose a fuzzy rule-based system to remotely identify calling devices. Finally, we propose a caller-ID based public key infrastructure for VoIP and VoLTE that provides signature generation at the calling party side as well as signature verification at the callee party side. The proposed signature can be used as caller-ID trust to prevent caller-ID spoofing and unsolicited calls. Our approach is based on the identity-based cryptography, and it also leverages the Domain Name System (DNS) and proxy servers in the VoIP architecture, as well as the Home Subscriber Server (HSS) and Call Session Control Function (CSCF) in the IP Multimedia Subsystem (IMS) architecture. Using OPNET, we then develop a comprehensive simulation testbed for the evaluation of our proposed infrastructure. Our simulation results show that the average call setup delays induced by our infrastructure are hardly noticeable by telephony subscribers and the extra signaling overhead is negligible. Therefore, our proposed infrastructure can be adopted to widely verify caller-ID in telephony networks.

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Background subtraction or scene modeling techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically "learned" as part of the background using a large training set of video data. Traditional techniques exhibit a number of limitations including inability to model partial background or subtract partial foreground, inflexibility of the model being used, need for large training data and computational inefficiency. In this thesis, we present our work to address each of these limitations and propose algorithms in two major areas of research within background subtraction namely single-view and multi-view based techniques. We first propose the use of both spatial and temporal properties to model a dynamic scene and show how Mapping Convergence framework within Support Vector Mapping Convergence (SVMC) can be used to minimize training data. We also introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a "selective subtraction" method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Our novel use of projective depth as a decision boundary allows us to extend the traditional definition of background subtraction and propose a much more powerful framework. Furthermore, we show that the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one; (iii) the technique can be used for a variety of situations including when images are captured using stationary cameras or hand-held cameras and for both indoor and outdoor scenes. We provide extensive results to show the effectiveness of the proposed framework in a variety of very challenging environments.

Multi-core processors increasingly appear as an enabling platform for embedded systems, e.g., mobile phones, tablets, computerized numerical controls, etc. The parallel task model, where a task can execute on multiple cores simultaneously, can efficiently exploit the multi-core platform's computational ability. Many computation-intensive systems (e.g., self-driving cars) that demand stringent timing requirements often evolve in the form of parallel tasks. Several real-time embedded system applications demand predictable timing behavior and satisfy other system constraints, such as energy consumption. Motivated by the facts mentioned above, this thesis studies the approach to integrating the dynamic voltage and frequency scaling (DVFS) policy with real-time embedded system application's internal parallelism to reduce the worst-case energy consumption (WCEC), an essential requirement for energy-constrained systems. First, we propose an energy-sub-optimal scheduler, assuming the per-core speed tuning feature for each processor. Then we extend our solution to adapt the clustered multi-core platform, where at any given time, all the processors in the same cluster run at the same speed. We also present an analysis to exploit a task's probabilistic information to improve the average-case energy consumption (ACEC), a common non-functional requirement of embedded systems. Due to the strict requirement of temporal correctness, the majority of the real-time system analysis considered the worst-case scenario, leading to resource over-provisioning and cost. The mixed-criticality (MC) framework was proposed to minimize energy consumption and resource over-provisioning. MC scheduling has received considerable attention from the real-time system research community, as it is crucial to designing safety-critical real-time systems. This thesis further addresses energy-aware scheduling of real-time tasks in an MC platform, where tasks with varying criticality levels (i.e., importance) are integrated into a common platform. We propose an algorithm GEDF-VD for scheduling MC tasks with internal parallelism in a multiprocessor platform. We also prove the correctness of GEDF-VD, provide a detailed quantitative evaluation, and reported extensive experimental results. Finally, we present an analysis to exploit a task's probabilistic information at their respective criticality levels. Our proposed approach reduces the average-case energy consumption while satisfying the worst-case timing requirement.

The purpose of this research is to analyze the security of next-generation big data processing (BDP) and examine the feasibility of applying advanced security features to meet the needs of modern multi-tenant, multi-level data analysis. The research methodology was to survey of the status of security mechanisms in BDP systems and identify areas that require further improvement. Access control (AC) security services were identified as priority area, specifically Attribute Based Access Control (ABAC). The exemplar BDP system analyzed is the Apache Hadoop ecosystem. We created data generation software, analysis programs, and posted the detailed the experiment configuration on GitHub. Overall, our research indicates that before a BDP system, such as Hadoop, can be used in operational environment significant security configurations are required. We believe that the tools are available to achieve a secure system, with ABAC, using Apache Ranger and Apache Atlas. However, these systems are immature and require verification by an independent third party. We identified the following specific actions for overall improvement: consistent provisioning of security services through a data analyst workstation, a common backplane of security services, and a management console. These areas are partially satisfied in the current Hadoop ecosystem, continued AC improvements through the open source community, and rigorous independent testing should further address remaining security challenges. Robust security will enable further use of distributed, cluster BDP, such as Apache Hadoop and Hadoop-like systems, to meet future government and business requirements.