A Cloud-based Framework for Security Analysis of Browser Extensions

In today's internet world, web browsers are an integral part of our day-to-day activities. Therefore, web browser security is a serious concern for all of us. Browsers can be breached in different ways. Because of the over privileged access, extensions are responsible for many security issues. Browser vendors try to keep safe extensions in their official extension galleries. However, their security control measures are not always effective and adequate. The distribution of unsafe extensions through different social engineering techniques is also a very common practice. Therefore, before installation, users should thoroughly analyze the security of browser extensions. Extensions are not only available for desktop browsers, but many mobile browsers, for example, Firefox for Android and UC browser for Android, are also furnished with extension features. Mobile devices have various resource constraints in terms of computational capabilities, power, network bandwidth, etc. Hence, conventional extension security analysis techniques cannot be efficiently used by end users to examine mobile browser extension security issues. To overcome the inadequacies of the existing approaches, we propose CLOUBEX, a CLOUd-based security analysis framework for both desktop and mobile Browser EXtensions. This framework uses a client-server architecture model. In this framework, compute-intensive security analysis tasks are generally executed in a high-speed computing server hosted in a cloud environment. CLOUBEX is also enriched with a number of essential features, such as client-side analysis, requirements-driven analysis, high performance, and dynamic decision making. At present, the Firefox extension ecosystem is most susceptible to different security attacks. Hence, the framework is implemented for the security analysis of the Firefox desktop and Firefox for Android mobile browser extensions. A static taint analysis is used to identify malicious information flows in the Firefox extensions. In CLOUBEX, there are three analysis modes. A dynamic decision making algorithm assists us to select the best option based on some important parameters, such as the processing speed of a client device and network connection speed. Using the best analysis mode, performance and power consumption are improved significantly. In the future, this framework can be leveraged for the security analysis of other desktop and mobile browser extensions, too.

Investigation of Performance Enhancements for a Liquid-Desiccant Solar Air-Conditioner

Thermally driven liquid-desiccant air-conditioners (LDAC) are a proven but still developing technology. LDACs can use a solar thermal system to reduce the operational cost and environmental impact of the system by reducing the amount of fuel (e.g. natural gas, propane, etc.) used to drive the system. LDACs also have a key benefit of being able to store energy in the form of concentrated desiccant storage. TRNSYS simulations were used to evaluate several different methods of improving the thermal and electrical coefficients of performance (COPt and COPe) and the solar fraction (SF) of a LDAC. The study analyzed a typical June to August cooling season in Toronto, Ontario. Utilizing properly sized, high-efficiency pumps increased the COPe to 3.67, an improvement of 55%. A new design, featuring a heat recovery ventilator on the scavenging-airstream and an energy recovery ventilator on the process-airstream, increased the COPt to 0.58, an improvement of 32%. This also improved the SF slightly to 54%, an increase of 8%. A new TRNSYS TYPE was created to model a stratified desiccant storage tank. Different volumes of desiccant were tested with a range of solar array system sizes. The largest storage tank coupled with the largest solar thermal array showed improvements of 64% in SF, increasing the value to 82%. The COPe was also improved by 17% and the COPt by 9%. When combining the heat recovery systems and the desiccant storage systems, the simulation results showed a 78% increase in COPe and 30% increase in COPt. A 77% improvement in SF and a 17% increase in total cooling rate were also predicted by the simulation. The total thermal energy consumed was 10% lower and the electrical consumption was 34% lower. The amount of non-renewable energy needed from the natural gas boiler was 77% lower. Comparisons were also made between LDACs and vapour-compression (VC) systems. Dependent on set-up, LDACs provided higher latent cooling rates and reduced electrical power consumption. Negatively, a thermal input was required for the LDAC systems but not for the VC systems.

THE DESIGN OF A POWER SYSTEM FOR A WIRELESS SENSOR NETWORK FOR LONG-TERM OPERATION

Wireless sensor networks (WSNs) have shown wide applicability to many fields including monitoring of environmental, civil, and industrial settings. WSNs however are resource constrained by many competing factors that span their hardware, software, and networking. One of the central resource constrains is the charge consumption of WSN nodes. With finite energy supplies, low charge consumption is needed to ensure long lifetimes and success of WSNs. This thesis details the design of a power system to support long-term operation of WSNs. The power system’s development occurs in parallel with a custom WSN from the Queen’s MEMS Lab (QML-WSN), with the goal of supporting a 1+ year lifetime without sacrificing functionality. The final power system design utilizes a TPS62740 DC-DC converter with AA alkaline batteries to efficiently supply the nodes while providing battery monitoring functionality and an expansion slot for future development. Testing tools for measuring current draw and charge consumption were created along with analysis and processing software. Through their use charge consumption of the power system was drastically lowered and issues in QML-WSN were identified and resolved including the proper shutdown of accelerometers, and incorrect microcontroller unit (MCU) power pin connection. Controlled current profiling revealed unexpected behaviour of nodes and detailed current-voltage relationships. These relationships were utilized with a lifetime projection model to estimate a lifetime between 521-551 days, depending on the mode of operation. The power system and QML-WSN were tested over a long term trial lasting 272+ days in an industrial testbed to monitor an air compressor pump. Environmental factors were found to influence the behaviour of nodes leading to increased charge consumption, while a node in an office setting was still operating at the conclusion of the trail. This agrees with the lifetime projection and gives a strong indication that a 1+ year lifetime is achievable. Additionally, a light-weight charge consumption model was developed which allows charge consumption information of nodes in a distributed WSN to be monitored. This model was tested in a laboratory setting demonstrating +95% accuracy for high packet reception rate WSNs across varying data rates, battery supply capacities, and runtimes up to full battery depletion.