Using Packet Timing Information in Website Fingerprinting

Website Fingerprinting (WF) enables an eavesdropper to discover what sites the user is visiting despite the use of a VPN or even the Tor anonymity system. Recent WF attacks on Tor have reached high enough accuracy (up to 98%) to prompt Tor to consider adopting defenses based on packet padding. Defenses such as Walkie-Talkie mainly remove features related to bursts of traffic without affecting packet timing. This was reasonable given that previous research on WF attacks ignored or deemphasized the use of packet timing information. In this thesis, we examine the extent to which packet timing can be used to facilitate WF attacks. In our experiment, we gained up to 61% accuracy on our unprotected dataset, 54% on our WTF-PAD dataset, and 43% on our Walkie-Talkie dataset using only timing-based features in an SVM classifier. Using a convolutional neural network (CNN), we got 88% accuracy on our unprotected dataset, and 76% and 47% accuracy on ourWTF-PAD and Walkie-Talkie dataset respectively. We intend to investigate further to develop an effective and robust WF attack using packet timing

Application of Mixed Iron Oxides in Subsurface Remediation Technologies

Heavy metal contamination of soil and groundwater has been a concern in water supply and public health in many countries where the water supply system draws primarily from groundwater. In the present study, mixed maghemite-magnetite nanoparticles have been used as adsorbents for Cr(VI), As and Cd(II) removal. From the study, it is apparent that the removal of Cr(VI ), Cd(II) and As(V) by mixed iron oxide nanoparticles depends on pH, temperature, contact time, solid/liquid ratio and initial concentration of heavy metals. The results showed that Cr(VI) adsorption on mixed maghemite-magnetite was dependent on solution pH between 3 and 6. Theoretical multiplet analyses in X-ray photoelectron spectroscopy (XPS) study showed that during Cr adsorption, the amount of maghemite increased from 70 to 89%. Fe(II) was transformed into Fe(III) by the redox reaction and Cr(VI) species were reduced to Cr(III) species. In arsenic removal study, it was found that the percent of maghemite also increased for As(V) and As(III) adsorption. At the same time, the percentage of magnetite was reduced for both cases. Thus, a redox reaction occurred on the mixed magnetite-magheamite surface when arsenic was introduced. In cadmium removal study, adsorption capacity of mixed maghemite-magnetite for Cd(II) ions increased with an increase in the pH of the adsorbate solution. The results showed that 0.8 g/L of 20-60 nm maghemite-magnetite particles removed up to 1.5 mg/L Cd. The XPS surveys confirmed that As, Cr(VI) and Cd(II) ions may undergo oxidation-reduction reactions upon exposure to mixed maghemite-magnetite, or may be fixed by complexation to the oxygen atoms in the oxyhydroxy groups.The investigation of transport and chemical states analysis during arsenic removal by monolith slag from nickel smelting revealed that slag was efficient in arsenic removal, attaining equilibrium sorption capacities in the range of 1000-1054 µg/g for an initial arsenic concentration of C0= 10 mg/L. Column studies showed the sorption of arsenic by smelter slag (a waste material) was complex and involved both chemisorption and physical sorption. Sorption capacities for As(V) were significantly higher for Ni smelter slag. Raman spectroscopy and XPS results demonstrate that the As reacted with a large proportion of the slag in the experiment. Thus, further investigation would be necessary to evaluate the applicability of mixed iron oxide loaded particles for subsurface remediation at field scale

Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces

Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design and train his attack classifier based on the defense, we first demonstrate that at a straightforward technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower bandwidth overheads.Comment: 18 pages, 13 figures and 8 Tables. Accepted in IEEE Transactions on Information Forensics and Security (TIFS

Change Impact Analysis of Code Clones

Copying a code fragment and reusing it with or without modifications is known to be a frequent activity in software development. This results in exact or closely similar copies of code fragments, known as code clones, to exist in the software systems. Developers leverage the code reuse opportunity by code cloning for increased productivity. However, different studies on code clones report important concerns regarding the impacts of clones on software maintenance. One of the key concerns is to maintain consistent evolution of the clone fragments as inconsistent changes to clones may introduce bugs. Challenges to the consistent evolution of clones involve the identification of all related clone fragments for change propagation when a cloned fragment is changed. The task of identifying the ripple effects (i.e., all the related components to change) is known as Change Impact Analysis (CIA). In this thesis, we evaluate the impacts of clones on software systems from new perspectives and then we propose an evolutionary coupling based technique for change impact analysis of clones. First, we empirically evaluate the comparative stability of cloned and non-cloned code using fine-grained syntactic change types. Second, we assess the impacts of clones from the perspective of coupling at the domain level. Third, we carry out a comprehensive analysis of the comparative stability of cloned and non-cloned code within a uniform framework. We compare stability metrics with the results from the original experimental settings with respect to the clone detection tools and the subject systems. Fourth, we investigate the relationships between stability and bug-proneness of clones to assess whether and how stability contribute to the bug-proneness of different types of clones. Next, in the fifth study, we analyzed the impacts of co-change coupling on the bug-proneness of different types of clones. After a comprehensive evaluation of the impacts of clones on software systems, we propose an evolutionary coupling based CIA approach to support the consistent evolution of clones. In the sixth study, we propose a solution to minimize the effects of atypical commits (extra large commits) on the accuracy of the detection of evolutionary coupling. We propose a clustering-based technique to split atypical commits into pseudo-commits of related entities. This considerably reduces the number of incorrect couplings introduced by the atypical commits. Finally, in the seventh study, we propose an evolutionary coupling based change impact analysis approach for clones. In addition to handling the atypical commits, we use the history of fine-grained syntactic changes extracted from the software repositories to detect typed evolutionary coupling of clones. Conventional approaches consider only the frequency of co-change of the entities to detect evolutionary coupling. We consider both change frequencies and the fine-grained change types in the detection of evolutionary coupling. Findings from our studies give important insights regarding the impacts of clones and our proposed typed evolutionary coupling based CIA approach has the potential to support the consistent evolution of clones for better clone management

Thermophysical Properties of Metal Oxides Nanofluids

Thermophysical properties of TiO2, Al2O3 and SiO2 nanofluids are experimentally investigated and compared with published data. Density has been measured over a range of 25–40°C for nanoparticle volumetric concentration of 0.05–4%. Viscosity experiments were carried out over a wide temperature range, from 25 to 80°C, to determine their applicability in such ranges. Nanofluids with particle volume fraction ranging from 0.02 to 0.03% and 1–4 kg/min were examined for the convective heat transfer and pumping power. The heat transfer coefficient of the nanofluid rises with rising mass flow rate, as well as rising volume concentration of metal oxide nanofluids; however, increasing the volume fraction results in increasing the density and viscosity of nanofluid, leading to a slight increase in friction factor which can be neglected. Addition of surfactants results in part of the increment in viscosity as well. An empirical formula for density is proposed, which also contributes to the novelty of this paper

Home Quarantine Challenges and Psychological Status of Bangladeshi University Students during COVID-19

The study determined the home quarantine challenges and psychological status of Bangladeshi University students in the COVID-19 pandemic break-out. The study also looked into the impact of home quarantine challenges on the psychological status of the student. A well-structured questionnaire was created and circulated to respondents through various social media platforms and utilized the snowball sampling technique. A total of 250 graduate and undergraduate students were taken as respondents of this study. Statistical Packages for Social Science (SPSS) software was used to complete the data analysis procedure. The findings of the study show that the most noteworthy home quarantine challenges for Bangladeshi University students are the lack of awareness of home quarantine advantages (82%), insufficient financial support from their families (78%), and unsatisfactory medical support (77%). Also, home quarantine challenges are positively connected to psychological status (r=.364**) and it had a significant impact (β=.287, p<0.00) on psychological status in the pandemic period. The study also aided the policymakers in the better understanding of home quarantine challenges and the psychological status of Bangladeshi students. However, out of seven divisions in Bangladesh only students of Dhaka and Sylhet were selected to be the respondents. This caused difficulty in generalizing the findings of the study