Search Results for author:
Found 30 papers, 4 papers with code
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity
Our results show that while federated learning enhances data privacy and distributed learning, it remains vulnerable to poisoning attacks, which must be mitigated to ensure its reliability in real-world applications.
A Comprehensive Review of Recommender Systems: Transitioning from Theory to Practice
Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions.
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation.
BEADs: Bias Evaluation Across Domains
We provide the BEADs dataset for detecting biases in various domains, and this dataset is readily usable for responsible AI development and application.
Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives
Beyond these numerical ratings, textual reviews provide insights into users fine-grained preferences and item features.
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda.
Experimental Validation of Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
To collect data, a vehicle equipped with a GNSS receiver, along with Inertial Measurement Unit (IMU) is used.
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time
Despite increasing awareness and research around fake news, there is still a significant need for datasets that specifically target racial slurs and biases within North American political speeches.
FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance.
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently.
Ranked #8 on
Natural Language Inference
on ANLI test
CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia
Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years.
Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems
Specifically, an RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s).
Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification
Although visual sensors of an AV, such as camera, lidar, and radar, help to see its surrounding environment, an AV cannot see beyond those sensors line of sight.
Audio Analytics-based Human Trafficking Detection Framework for Autonomous Vehicles
The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles.
Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon
The novelty of the asymmetric LBCM is that using this model all the follower vehicles in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time gap to improve platoon operational efficiency while maintaining local and string stability.
A Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
Data from multiple low-cost in-vehicle sensors (i. e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i. e., the distance that an AV travels between two consecutive timestamps.
A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles
We find that the accuracy of the RL model ranges from 99. 99% to 100%, and the recall value is 100%.
An Innovative Attack Modelling and Attack Detection Approach for a Waiting Time-based Adaptive Traffic Signal Controller
Thus, the objectives of this paper are to: (i) develop a "slow poisoning" attack generation strategy for an ATSC, and (ii) develop a prediction-based "slow poisoning" attack detection strategy using a recurrent neural network -- i. e., long short-term memory model.
Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles
In this study, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state).
Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles
A spoofed attack is difficult to detect as a spoofer (attacker who performs spoofing attack) can mimic the GNSS signal and transmit inaccurate location coordinates to an AV.
Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers.
Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications
The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions in order to maintain a high pedestrian detection accuracy.
Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control
Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1, 1)); (ii) GM(1, 1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections.
Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections
The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds).
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
This attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.
Connected Vehicle Application Development Platform (CVDeP) for Edge-centric Cyber-Physical Systems
Connected vehicle (CV) application developers need a development platform to build, test and debug CV applications, such as safety, mobility, and environmental applications, in an edge-centric Cyber-Physical Systems.
Networking and Internet Architecture
Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment
Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers.
Cryptography and Security
Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles
This estimated traffic flow parameters from low penetration of connected vehicles become noisy compared to 100 percent penetration of CVs, and such noise reduces the real time prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in terms of predicting traffic flow parameters.
Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy
We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined object detection accuracy.
A Longitudinal Study of Google Play
Our results show that a high number of these apps have not been updated over the monitoring interval.
Social and Information Networks Computers and Society
