25,132 research outputs found
An Evasion Attack against ML-based Phishing URL Detectors
Background: Over the year, Machine Learning Phishing URL classification
(MLPU) systems have gained tremendous popularity to detect phishing URLs
proactively. Despite this vogue, the security vulnerabilities of MLPUs remain
mostly unknown. Aim: To address this concern, we conduct a study to understand
the test time security vulnerabilities of the state-of-the-art MLPU systems,
aiming at providing guidelines for the future development of these systems.
Method: In this paper, we propose an evasion attack framework against MLPU
systems. To achieve this, we first develop an algorithm to generate adversarial
phishing URLs. We then reproduce 41 MLPU systems and record their baseline
performance. Finally, we simulate an evasion attack to evaluate these MLPU
systems against our generated adversarial URLs. Results: In comparison to
previous works, our attack is: (i) effective as it evades all the models with
an average success rate of 66% and 85% for famous (such as Netflix, Google) and
less popular phishing targets (e.g., Wish, JBHIFI, Officeworks) respectively;
(ii) realistic as it requires only 23ms to produce a new adversarial URL
variant that is available for registration with a median cost of only
$11.99/year. We also found that popular online services such as Google
SafeBrowsing and VirusTotal are unable to detect these URLs. (iii) We find that
Adversarial training (successful defence against evasion attack) does not
significantly improve the robustness of these systems as it decreases the
success rate of our attack by only 6% on average for all the models. (iv)
Further, we identify the security vulnerabilities of the considered MLPU
systems. Our findings lead to promising directions for future research.
Conclusion: Our study not only illustrate vulnerabilities in MLPU systems but
also highlights implications for future study towards assessing and improving
these systems.Comment: Draft for ACM TOP
Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh
Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations
Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities
With the rapid development of economy in China over the past decade, air
pollution has become an increasingly serious problem in major cities and caused
grave public health concerns in China. Recently, a number of studies have dealt
with air quality and air pollution. Among them, some attempt to predict and
monitor the air quality from different sources of information, ranging from
deployed physical sensors to social media. These methods are either too
expensive or unreliable, prompting us to search for a novel and effective way
to sense the air quality. In this study, we propose to employ the state of the
art in computer vision techniques to analyze photos that can be easily acquired
from online social media. Next, we establish the correlation between the haze
level computed directly from photos with the official PM 2.5 record of the
taken city at the taken time. Our experiments based on both synthetic and real
photos have shown the promise of this image-based approach to estimating and
monitoring air pollution.Comment: ICIMCS '1
Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance
Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities.
In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment.
Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities.
Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
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