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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 2
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Blog Post number 1
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portfolio
ML Inferencing for DDoS Attack Detection in Heterogeneous & Constrained Environments
Exploring machine learning techniques to detect advanced DDoS attacks from IoT devices that mimic benign traffic, introducing correlation-aware architectures and comparing centralized versus distributed detection methods. 
The Study on Essential Technology of Food Security Data
This project created a user-friendly web platform to highlight food security risks using a graph database and interactive visual tools. It offers dynamic maps and a searchable 3D word cloud to make information easily accessible and engaging for the public. 
Introduction to Neural Network Online Project-Based Learning Program
In the following project, we will find an efficient convolution neural network to identify whether a house was damaged using the satellite imagery as well as the best way to improve it’s appearance. 
publications
ST-Seq2Seq: A Spatio-Temporal Feature-Optimized Seq2Seq Model for Short-Term Vessel Trajectory Prediction
Published in IEEE ACCESS, 2020
Abstract: Deep learning provides appropriate mechanisms to predict vessel trajectories for safer and efficient shipping, but still existing models are mainly oriented to longer-term prediction trends and do not fully support real time navigation needs. While most recent works have been largely exploiting Automatic Identification System (AIS), the complete semantics of these data haven’t so far fully exploited. The research presented in this paper introduced an extended sequence-to-sequence model using AIS data. A Gated Recurrent Unit (GRU) network encodes historical spatio-temporal sequences as a context vector, which not only preserves the sequential relationships among trajectory locations, but also alleviates the gradient descent problem. The GRU network acts as a decoder, outputting target trajectory location sequences. Real AIS data from the Chongqing and Wuhan sections of the Yangzi River were selected as typical experimental areas for evaluation purposes. The proposed ST-Seq2Seq model has been tested against the LSTM-RNN and GRU-RNN baseline models for short term trajectory prediction experiments. A 10-minute historical trajectory sequence was used to predict the trajectory sequence for the next five minutes. Overall, the findings show that LSTM and GRU networks, while applying a recursive method to predict a sequence of continuous trajectory points, when the number of predicted trajectory points increases accuracy decreases. Conversely, the extended sequence-to-sequence model shows satisfactory stability on different ship channels.
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Recommended citation: You, L., Xiao, S., Peng, Q., Claramunt, C., Han, X., Guan, Z., & Zhang, J. (2020). St-seq2seq: A spatio-temporal feature-optimized seq2seq model for short-term vessel trajectory prediction. IEEE Access, 8, 218565-218574.
An Ensemble Learning Approach of Multi-Model for Classifying House Damage
Published in 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering, 2021
Abstract: In the past decades, natural disasters have caused an immeasurable impact on the development of human society. The original way to measure the damage is to detect and quantify the number of damaged buildings, usually by driving around the affected area and noting down manually, which takes a lot of resources. Therefore, damage assessment is vital to the relief helpers and first responders so that resources and help can be planned and allocated appropriately. To tackle this issue, an ensemble learning model for classifying house damage is proposed in this paper. Firstly, to facilitate the processing of the model, a set of satellite images before and after the destruction of houses is preprocessed using data augmentation. Secondly, the basic models are first trained and used to predict test sets. Their running parameters were recorded to demonstrate their performances. Thirdly, using Bootstrapping to generate multiple sub-datasets and Bagging is adopted to ensemble the basic classifiers. Finally, the ensemble learning model is used to predict the test set, and the final result is determined by voting strategy to achieve the optimization effect. The experimental results on the public dataset show that the proposed model achieves an ideal effect on classification. Furthermore, by comparing the results of the basic classifiers with that of the model using the Bagging strategy, we find that Bagging is significantly helpful to improve the model effect.
Authors in the paper contribute equally.
Recommended citation: J. Fan, C. Xu and J. Zhang, "An Ensemble Learning Approach of Multi-Model for Classifying House Damage," 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), Zhuhai, China, 2021, pp. 145-152, doi: 10.1109/ICBASE53849.2021.00035.
A Spatio-temporal Schedule-fused Neural Network for Urban Taxi Waiting Time Prediction
Published in International Journal of Geo- Information, ISPRS (IF: 3.4, SCI Q3), 2021
Abstract: Taxi waiting times is an important criterion for taxi passengers to choose appropriate pick-up locations in urban environments. How to predict the taxi waiting time accurately at a certain time and location is the key solution for the imbalance between the taxis’ supplies and demands. Considering the life schedule of urban residents and the different functions of geogrid regions, the research developed in this paper introduces a spatio-temporal schedule-based neural network for urban taxi waiting time prediction. The approach integrates a series of multi-source data from taxi trajectories to city points of interest, different time frames and human behaviors in the city. We apply a grid-based and functional structuration of an urban space that provides a lower-level data representation. Overall, the neural network model can dynamically predict the waiting time of taxi passengers in real time under some given spatio-temporal constraints. The experimental results show that the granular-based grids and spatio-temporal neural network can effectively predict and optimize the accuracy of taxi waiting times. This work provides a decision support for intelligent travel predictions of taxi waiting time in a smart city.
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Recommended citation: You, Lan, et al. "A spatio-temporal schedule-based neural network for urban taxi waiting time prediction." ISPRS International Journal of Geo-Information 10.10 (2021): 703.
Demo Abstract- CUDDoS: Correlation-aware Ubiquitous Detection of DDoS in IoT Systems
Published in The 21th ACM Conference on Embedded Networked Sensor Systems (SenSys 2023), 2023
Abstract: In recent years, there has been a significant surge in the deployment of Internet of Things (IoT) devices, which has consequently escalated security threats, notably Distributed Denial of Service (DDoS) attacks. Our prior research developed an LSTM-based framework for detecting futuristic DDoS attacks but largely relied on simulated datasets [1]. To bridge this gap, we designed a Raspberry Pi (RPi) testbed that mimics the complexities of large-scale IoT networks. This setup allows us to simulate realistic DDoS attacks originating from IoT devices and evaluate the effectiveness of various DDoS detection techniques. Specifically, using this RPi testbed, we validated the effectiveness of our LSTM-based framework in identifying futuristic DDoS attacks, observing an F1 score ranging between 0.8 and 0.86 depending on the aggressiveness of the DDoS attack.
This paper will be published in the proceedings of the conference, and the source code associated with this paper is available here.
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Note: This work corresponds to the project “ML Inferencing for DDoS Attack Detection in Heterogeneous & Constrained Environments”, here.
Recommended citation: Jiahe Zhang, Tamoghna Sarkar, Arvin Hekmati, Bhaskar Krishnamachari. 2023. Demo Abstract: CUDDoS - Correlation-aware Ubiquitous Detection of DDoS in IoT Systems. In The 21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23), November 12–17, 2023, Istanbul,Turkiye. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3625687.3628392
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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