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With the advent of technology in recent years, people depend more on online reviews to purchase a product. It is hard to determine whether the product is good or bad from hundreds of mixed reviews. Also, it is very time-consuming to read many reviews. So, opinion mining of reviews is necessary. The main aim of this project is to convert the reviews of a product into a rating and to evaluate the ratings using machine learning algorithms such as Naïve Bayes and Support Vector Machine. In the process of converting the reviews to a rating, score words are created using SentiWordNet and transformed into seven categories from highly positive to highly negative.

Graphic Processing Units (GPUs) are originally mainly designed to accelerate graphic applications. Now the capability of GPUs to accelerate applications that can be parallelized into a massive number of threads makes GPUs the ideal accelerator for boosting the performance of such kind of general-purpose applications. Meanwhile it is also very promising to apply GPUs to embedded and real-time applications as well, where high throughput and intensive computation are also needed. However, due to the different architecture and programming model of GPUs, how to fully utilize the advanced architectural features of GPUs to boost the performance and how to analyze the worst-case execution time (WCET) of GPU applications are the problems that need to be addressed before exploiting GPUs further in embedded and real-time applications. We propose to apply both architectural modification and static analysis methods to address these problems. First, we propose to study the GPU cache behavior and use bypassing to reduce unnecessary memory traffic and to improve the performance. The results show that the proposed bypassing method can reduce the global memory traffic by about 22% and improve the performance by about 13% on average. Second, we propose a cache access reordering framework based on both architectural extension and static analysis to improve the predictability of GPU L1 data caches. The evaluation results show that the proposed method can provide good predictability in GPU L1 data caches, while allowing the dynamic warp scheduling for good performance. Third, based on the analysis of the architecture and dynamic behavior of GPUs, we propose a WCET timing model based on a predictable warp scheduling policy to enable the WCET estimation on GPUs. The experimental results show that the proposed WCET analyzer can effectively provide WCET estimations for both soft and hard real-time application purposes. Last, we propose to analyze the shared Last Level Cache (LLC) in integrated CPU-GPU architectures and to integrate the analysis of the shared LLC into the WCET analysis of the GPU kernels in such systems. The results show that the proposed shared data LLC analysis method can improve the accuracy of the shared LLC miss rate estimations, which can further improve the WCET estimations of the GPU kernels.

Energy-efficiency and performance have been the driving forces of system architectures and designers in the last century. Given the diversity of workloads and the significant performance and power improvements when running workloads on customized processing elements, system vendors are drifting towards new system architectures (e.g., FAM or HMM). Such architectures are being developed with the purpose of improving the system's performance, allow easier data sharing, and reduce the overall power consumption. Additionally, current computing systems suffer from a very wide attack surface, mainly due to the fact that such systems comprise of tens to hundreds of sub-systems that could be manufactured by different vendors. Vulnerabilities, backdoors, and potentially hardware trojans injected anywhere in the system form a serious risk for confidentiality and integrity of data in computing systems. Thus, adding security features is becoming an essential requirement in modern systems. In the purpose of achieving these performance improvements and power consumption reduction, the emerging NVMs stand as a very appealing option to be the main memory building block or a part of it. However, integrating the NVMs in the memory system can lead to several challenges. First, if the NVM is used as the sole memory, incorporating security measures can exacerbate the NVM's write endurance and reduce its lifetime. Second, integrating the NVM as a part of the main memory as in DRAM-NVM hybrid memory systems can lead to higher performance overheads of persistent applications. Third, Integrating the NVM as a memory extension as in fabric-attached memory architecture can cause a high contention over the security metadata cache. Additionally, in FAM architectures, the memory sharing can lead to security metadata coherence problems. In this dissertation, we study these problems and propose novel solutions to enable secure and efficient integration of NVMs in the emerging architectures.

The Internet of Things (IoT) integrates a wide range of devices into a network to provide intelligent services. The lack of security mechanisms in such systems can cause an exposure of sensitive private data. Moreover, a networks of compromised IoT devices can allow adversaries the ability to bring down crucial systems. Indeed, adversaries have exploited software vulnerabilities in these devices for their benefit, and to execute various malicious intents. Therefore, understanding the software of these emerging systems is of the utmost importance. Building towards this goal, in this dissertation, we undertake a comprehensive analysis of the IoT software by employing different analysis techniques. To analyze the emerging IoT software systems, we first perform an in-depth and thorough analysis of the IoT binaries through static analysis. Through efficient and scalable static analysis, we extract artifacts that highlight the dynamics of the malware. In particular, by analyzing the strings, functions, and Control Flow Graphs (CFGs) of the IoT malware, we uncover their execution strategy, unique textual characteristics, and network dependencies. Additionally, through analysis of CFGs, we show the ability to approximate the main function. Using the extracted static artifacts, we design an effective malware detector. Noting that IoT malware have increased their sophistication and impact, the static approaches are prone to obfuscation that aims to evade analysis attempts. Acknowledging these attempts and to mitigate such threats, it is essential to profile the shared and exclusive behavior of these threats, such that they are easily achievable and aware of the capabilities of the widely-used IoT devices. To that end, we introduce MALInformer, an integrated dynamic and static analysis framework to analyze Linux-based IoT software and identify behavioral patterns for effective threat profiling. Leveraging an iterative signatures selection method, by taking into account the normalized frequency, cardinality, and programs covered by the signatures, MALInformer identifies distinctive and interpretable behaviors for every threat category. The static and dynamic analyses show the exploitability of the emerging systems. These weaknesses are typically reported to vulnerability databases along with the information that enable their reproduction and subsequent patching in other and related software. These weaknesses are assigned a Common Vulnerabilities and Exposures (CVE) number. We explore the quality of the reports in the National Vulnerability Database (NVD), unveiling their inconsistencies which we eventually fix. We then conduct case studies, including a large-scale evaluation of the cost of software vulnerabilities, revealing that the consumer product, software, and the finance industry are more likely to be negatively impacted by vulnerabilities. Overall, our work builds tools to analyze and detect the IoT malware and extract behavior unique to malware families. Additionally, our consistent NVD streamlines vulnerability management in emerging internet-connected systems, highlighting the economics aspects of vulnerabilities.

Systems like Google Home, Alexa, and Siri that use voice-based authentication to verify their users’ identities are vulnerable to voice replay attacks. These attacks gain unauthorized access to voice-controlled devices or systems by replaying recordings of passphrases and voice commands. This shows the necessity to develop more resilient voice-based authentication systems that can detect voice replay attacks. This thesis implements a system that detects voice-based replay attacks by using deep learning and image classification of voice spectrograms to differentiate between live and recorded speech. Tests of this system indicate that the approach represents a promising direction for detecting voice-based replay attacks.

Agent-based (AB) and system dynamics (SD) modeling and simulation techniques have been studied and used by various research fields. After the new hybrid modeling field emerged, the combination of these techniques started getting attention in the late 1990's. Applications of using agent-based (AB) and system dynamics (SD) hybrid models for simulating systems have been demonstrated in the literature. However, majority of the work on the domain includes system specific approaches where the models from two techniques are integrated after being independently developed. Existing work on creating an implicit and universal approach is limited to conceptual modeling and structure design. This dissertation proposes an approach for generating AB-SD hybrid models of systems by using Systems Modeling Language (SysML) which can be simulated without exporting to another software platform. Although the approach is demonstrated using IBM's Rational Rhapsody it is applicable to all other SysML platforms. Furthermore, it does not require prior knowledge on agent-based or system dynamics modeling and simulation techniques and limits the use of any programming languages through the use of SysML diagram tools. The iterative modeling approach allows two-step validations, allows establishing a two-way dynamic communication between AB and SD variables and develops independent behavior models that can be reused in representing different systems. The proposed approach is demonstrated using a hypothetical population, movie theater and a real–world training management scenarios. In this setting, the work provides methods for independent behavior and system structure modeling. Finally, provides behavior models for probabilistic behavior modeling and time synchronization.

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU.