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BackdoorBench: A Comprehensive Benchmark of Backdoor Learning 36

BackdoorBench: A Comprehensive Benchmark of Backdoor Learnin...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Wu, Baoyuan Chen, Hongrui Zhang, Mingda Zhu, Zihao Wei, Shaokui Yuan, Danni Shen, Chao Chinese Univ Hong Kong Shenzhen Res Inst Big Data Sch Data Sci Shenzhen Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China

ISBN: (纸本)9781713871088

Backdoor learning is an emerging and vital topic for studying deep neural networks' vulnerability (DNNs). Many pioneering backdoor attack and defense methods are being proposed, successively or concurrently, in the status of a rapid arms race. However, we find that the evaluations of new methods are often unthorough to verify their claims and accurate performance, mainly due to the rapid development, diverse settings, and the difficulties of implementation and reproducibility. Without thorough evaluations and comparisons, it is not easy to track the current progress and design the future development roadmap of the literature. To alleviate this dilemma, we build a comprehensive benchmark of backdoor learning called BackdoorBench. It consists of an extensible modular-based codebase (currently including implementations of 8 state-of-the-art (SOTA) attacks and 9 SOTA defense algorithms) and a standardized protocol of complete backdoor learning. We also provide comprehensive evaluations of every pair of 8 attacks against 9 defenses, with 5 poisoning ratios, based on 5 models and 4 datasets, thus 8,000 pairs of evaluations in total. We present abundant analysis from different perspectives about these 8,000 evaluations, studying the effects of different factors in backdoor learning. All codes and evaluations of BackdoorBench are publicly available at https://***.

关键词： Deep neural networks

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Detection of anomalous trajectories for vehicle traffic data

Detection of anomalous trajectories for vehicle traffic data

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2023 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2023

作者： Moavinis, Angelos Gounaris, Anastasios Constantinou, Ioannis Istognosis Ltd. Nicosia Cyprus Aristotle University of Thessaloniki Greece

Modern GPS recording devices and big data infrastructures enable us to study traffic patterns in a multitude of ways, including trajectory outlier detection. A trajectory is a sequence of consecutive geographical points that can also have timestamps and an outlier is defined as a record, which in our case is a trajectory, that significantly differs from the norm. In this work, we are motivated by the need (i) to encapsulate trajectory outlier detection in real-life Fleet Management Systems (FMSs) and (ii) to improve the performance of existing outlier detection methods. To this end, two trajectory outlier detection methods are proposed, the first one relying on the DBSCAN clustering algorithm and the Hausdorff distance and the second one relying on a Support Vector Machine (SVM) classifier and the Generalized Sequence Pattern algorithm. These two algorithms are evaluated against baselines on two automatically labeled traffic datasets, the former from the Beijing metropolitan area and the latter from a real FMS in Cyprus. Automated labelling process is adopted to both allow for reproducibility and lift the burden of manual annotations from domain experts. The results show that our proposals exhibit better performance than the baselines in terms of accuracy and the F1 score, while, in general, the SVM model performs better than the path clustering one. Finally, traffic clusters and specific outliers are discussed to prove the validity of the models. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Trajectories

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IoT Network Security Framework to Classify RPL Attacks Using Deep Learning algorithms

IoT Network Security Framework to Classify RPL Attacks Using...

引用

Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Saja Ghanem Alanazi Amjad Alsirhani Nada Sultan Alanazi College of Computer and Information Sciences Jouf University Jouf Saudi Arabia Faculty of Computer Science Dalhousie University Halifax NS Canada

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The Internet of Things (IoT) is an innovative concept that has become widely popular due to its numerous benefits. However, the Internet of Things (IoT) encounters numerous obstacles to ensuring security. RPL attacks pose a significant threat to the security of IoT networks, potentially compromising the integrity and functionality of interconnected devices and systems. Nevertheless, this growing dependence requires strong cybersecurity measures. We propose utilizing two deep learning models, namely GRU and LSTM-CNN. These models have been trained on the Contiki Cooja IoT simulator dataset. Furthermore, the SMOTE-ENN approach is utilized to balance the distribution of the attack classes in the dataset. The efficacy of the two proposed models is evaluated by taking into account their precision and F1-score. The findings show that the GRU and LSTM-CNN models exhibit 90% and 95% classification accuracies, respectively, in identifying RPL attacks.

关键词： Deep learning Training Accuracy Computational modeling Standardization data models Real-time systems Internet of Things Computer security Systematic literature review

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Positioning in DECT-2020 NR with Proactive Measurement Selection

Positioning in DECT-2020 NR with Proactive Measurement Selec...

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international conference on Localization and GNSS (ICL-GNSS)

作者： Antti Saikko Jukka Talvitie Mikko Valkama Department of Electrical Engineering Tampere University Tampere Finland

ISBN: (数字)9798350380781

ISBN: (纸本)9798350380798

This paper addresses efficient positioning and user device tracking in Internet-of- Things (IoT) networks with par-ticular focus on the new DECT-2020 New Radio standard - the first non-cellular 5G technology standard in the world. Stemming from fundamental performance requirements of IoT networks, for example, related to energy consumption, latency, and reliability, it is important to utilize available radio resources efficiently, while avoiding redundant transmissions and signaling. In this paper, we propose a tracking-based positioning solution with a proactive measurement selection, in which positioning measurements are selected based on Fisher information. The proposed method, accompanied with computationally attractive processing solution, is able to sort and select potential positioning measurements based on achievable minimum positioning error. The provided numerical results, considering both range and angle measurements, show that the proposed method outperforms the traditional signal-to-noise ratio based measurement selection approach, and enables positioning with a smaller number of measurements.

关键词： Performance evaluation Energy consumption Mobility models Energy measurement Position measurement Prediction algorithms New Radio

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data Trimming Methods to Improve Gesture Classification 24

Data Trimming Methods to Improve Gesture Classification

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24th international conference on Electrical Machines and Systems (ICEMS)

作者： Roh, Hye Sung Kim, DaeEun Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9788986510218

This paper introduces a data processing method to enhance the performance of a gesture classification model. When tested on the UTD-MHAD dataset, the HMM model initially rendered a poor performance due to seemingly resembling gestures. To tackle this problem, data has been altered via normalization and selection of significant joints that determine the gesture. Refining data prior to classifying generates a better performance in both HMM and LSTM models, highlighting the significance of data processing across different types of classification models.

关键词： Hidden Markov Model (HMM) data Preprocessing Gesture Recognition Human-Computer Interaction

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Multiple Dynamic Outlier-Detection from a data Stream by Exploiting Duality of data and Queries 21

Multiple Dynamic Outlier-Detection from a Data Stream by Exp...

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ACM SIGMOD international conference on Management of data (SIGMOD)

作者： Yoon, Susik Shin, Yooju Lee, Jae-Gil Lee, Byung Suk Korea Adv Inst Sci & Technol Daejeon South Korea Univ Vermont Burlington VT 05405 USA

ISBN: (纸本)9781450383431

Real-time outlier detection from a data stream has become increasingly important in the current hyperconnected world. This paper focuses on an important yet unaddressed challenge in continuous outlier detection-the multiplicity and dynamicity of queries. This challenge arises from various contexts of outliers evolving over time, but the state-of-the-art algorithms cannot handle the challenge effectively, as they can only process a fixed set of outlier detection queries for each data point separately. In this paper, we propose a novel algorithm, abbreviated as MDUAL, based on a new idea called duality-based unified processing. The underlying rationale is to exploit the duality of data and queries so that a group of similar data points are processed together by a group of similar queries incrementally. Two main techniques embodying the idea, data-query grouping and prioritized group processing, are employed. Comprehensive experiments showed that MDUAL runs 216 to 221 times faster while consuming 11 to 13 times less memory than the state-of-the-art algorithms through its efficient and effective handling of the multiplicity-dynamicity challenge.

关键词： Outlier detection anomaly detection data stream

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Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning 1

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18th international conference on Soft Computing models in Industrial and Environmental Applications, SOCO 2023

作者： Chacón-Maldonado, A.M. Troncoso-García, A.R. Martínez-Álvarez, F. Asencio-Cortés, G. Troncoso, A. Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

This paper proposes an application of the Automated Deep Learning model to predict the presence of olive flies in crops. Compared to baseline algorithms such as Random Forest or K-Nearest Neighbor, our Automated Deep Learning model demonstrates superior performance. Explainable Artificial Intelligence techniques such as Local Interpretable Model-Agnostic Explanations and Shapley Additive explanations are applied to interpret the results, revealing solar radiation as a key predictor for the presence of olive fly. This study enhances deep learning for agriculture, showcasing Automated Deep Learning superiority, and providing interpretable insights for effective pest management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Olive oil

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SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage processing Architectures 22

SmartSAGE: Training Large-scale Graph Neural Networks using ...

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49th IEEE/ACM Annual international Symposium on Computer Architecture (ISCA)

作者： Lee, Yunjae Chung, Jinha Rhu, Minsoo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9781450386104

Graph neural networks (GNNs) can extract features by learning both the representation of each objects (i.e., graph nodes) and the relationship across different objects (i.e., the edges that connect nodes), achieving state-of-the-art performance in various graph-based tasks. Despite its strengths, utilizing these algorithms in a production environment faces several challenges as the number of graph nodes and edges amount to several billions to hundreds of billions scale, requiring substantial storage space for training. Unfortunately, state-of-the-art ML frameworks employ an in-memory processing model which significantly hampers the productivity of ML practitioners as it mandates the overall working set to fit within DRAM capacity. In this work, we first conduct a detailed characterization on a state-of-the-art, large-scale GNN training algorithm, GraphSAGE. Based on the characterization, we then explore the feasibility of utilizing capacity-optimized NVMe SSDs for storing memory-hungry GNN data, which enables large-scale GNN training beyond the limits of main memory size. Given the large performance gap between DRAM and SSD, however, blindly utilizing SSDs as a direct substitute for DRAM leads to significant performance loss. We therefore develop SmartSAGE, our software/hardware co-design based on an in-storage processing (ISP) architecture. Our work demonstrates that an ISP based large-scale GNN training system can achieve both high capacity storage and high performance, opening up opportunities for ML practitioners to train large GNN datasets without being hampered by the physical limitations of main memory size.

关键词： Graph neural network computational storage device near data processing solid state drives (SSD)

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DF-SegDiff: A Diffusion Segmentation Model using a New Distributed Parallel Computing Algorithm

DF-SegDiff: A Diffusion Segmentation Model using a New Distr...

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Cognitive Computing and Complex data (ICCD), international conference on the

作者： Hancang Mi Hong-Seng Gan Xiaoyi Wang Akinobu Shimizu Muhammad Hanif Ramlee Mehmet Zubeyir Unlu School of AI and Advanced Computing Xi’an Jiaotong – Liverpool University Suzhou China School of Mathematics and Physics Xi’an Jiaotong – Liverpool University Suzhou China Institute of Engineering Tokyo University of Agriculture and Technology Tokyo Japan Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Electric and Electronics Engineering Izmir Institute of Technology Turkey

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallel computing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.

关键词： Training Measurement Image segmentation Computational modeling Graphics processing units Parallel processing Diffusion models data models Tumors Biomedical imaging

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An NLP Approach to Efficient Duplicate Question Detection using Neural Networks and TF-IDF

An NLP Approach to Efficient Duplicate Question Detection us...

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Computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Afshan Hashmi Department of Computer Science and Engineering Jamia Hamdard University New Delhi India

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Duplicate question pair detection (DQPD) is one of the significant factors influencing the user satisfaction in the NLP based platforms like Quora. With the increase in user generated content, identifying duplicate questions is also necessary. In this work, a new approach for DQPI is introduced and based on neural networks and TF-IDF features. For this experiment, we use Quora Question Pairs dataset obtained from Kaggle and our model achieves an impressive accuracy of 96.71% To handle the extensive datasets required for training, we utilize specialized data generation techniques. This work advances the field of Natural Language processing by: Establishing the efficacy of introducing neural networks along with TF-IDF for DQPI. Making the training more memory efficient by using custom data generators. Increasing the Accuracy to detect Duplicate Question Pairs on a Realistic dataset. It is envisioned that this work will be useful in following up on text classification with the neural network techniques in the NLP.

关键词： Training Accuracy Computational modeling Neural networks Text categorization Memory management User-generated content Feature extraction Natural language processing data models

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