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Analysis and Detection of DDoS Backscatter Using NetFlow data, Hyperband-Optimised Deep learning and Explainability Techniques 21st

Analysis and Detection of DDoS Backscatter Using NetFlow Dat...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Pawlicki, Marek Zadnik, Martin Kozik, Rafal Choras, Michal ITTI Sp Zoo Poznan Poland Bydgoszcz Univ Sci & Technol Bydgoszcz Poland CESNET Prague Czech Republic

ISBN: (纸本)9783031234910;9783031234927

The Denial of Service attacks are one of the most common attacks used to disrupt the services of public institutions. The criminal act of exhausting a network resource with the intent to obstruct the utility of a service is associated with hacktivism, blackmailing and extortion attempts. Intrusion Prevention Systems are an essential line of defence against this problem, strengthening public institutions, industrial and critical infrastructure alike. In the following work, an analysis of the detection of DDoS Backscatter with the use of neural networks is performed. To this end, a novel dataset is collected and described, on which a hyperband-optimized neural network is trained, and the decision process of the classifier is explained using LIME and SHAP.

关键词： Cyber security Machine learning Explainable AI (xAI)

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Cooperative learning strategy for Human Behavior Prediction Using Multi-Modal data 14

Cooperative Learning Strategy for Human Behavior Prediction ...

引用

14th international conference on Information and Communication Technology Convergence, ICTC 2023

作者： Lee, Doyun Lee, Hoon Pukyong National University Department of Intelligent Robot Engineering Busan Korea Republic of Unist Department of Electrical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350313277

Understanding human behaviors leads to fully-automated systems in the near future. This paper investigates a deep learning solution that forecasts human activity patterns based on sensing signals measured by internet-of-things devices. Practical limitations on these small-form-factor sensors request remote deep learning services at a distant edge computing server. Therefore, we need to involve impairments in sensor-server communication phases, such as random packet loss, resource constraint, and propagation noise, in the design of the remote learning architecture. To address these challenges, we propose a collaborative learning strategy among the sensors and server. Each sensor is equipped with its own encoding neural network that compresses high-dimensional sensing signals to communication messages. These are forwarded to the server through imperfect backhaul channels. Then, a classifier at the server infers desired labels. A joint training mechanism of the encoders and classifier is developed along with the channel impairment. By doing so, we can obtain a robust prediction model for arbitrary communication noises. Numerical results demonstrate the viability of the proposed methods. © 2023 IEEE.

关键词： human behavior prediction Lifelog dataset multi-modal features

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MTLearn: Extracting Temporal Rules Using datalog Rule Learners 21

MTLearn: Extracting Temporal Rules Using Datalog Rule Learne...

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21st international conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Wang, Dingmin Walęga, Przemyslaw A. Grau, Bernardo Cuenca Department of Computer Science University of Oxford United Kingdom

ISBN: (纸本)9781956792058

We propose a framework for temporal rule learning from datasets, which capitalises on the availability of increasingly mature datalog rule learners. Our approach is based on the idea of splitting a temporal dataset into windows, extracting static rules from each window with an off-the-shelf datalog rule learner, and then combining the obtained static rules into temporal rules corresponding to the whole dataset. Temporal rules generated by our approach are expressed in datalogMTL and are assigned time-sensitive confidence scores. We have implemented our approach in a system MTLearn compatible with any datalog rule learner, as well as with a range of strategies for scoring the output temporal rules. Results on the task of temporal link prediction show that our proposed approach is highly competitive, achieve performance comparable to that of state-of-the-art machine learning models for both the extrapolation and the interpolation settings, while at the same time providing interpretable results. © 2024 Proceedings of the international conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Spatio-temporal data

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Toward Robust DDOS Detection in SDN: Leveraging Feature engineering and Ensemble learning 21

Toward Robust DDOS Detection in SDN: Leveraging Feature Engi...

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21st international Computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Madoune, Sid Ali Senouci, Sarra Setitra, Mohamed Ali Dingde, Jiang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Mechanical and Electronic Engineering University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

This paper introduces a novel approach for detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN) environments, addressing the critical need for accurate and efficient threat detection in modern network infrastructures. The proposed method integrates dimensionality reduction, feature engineering, and machine learning (ML) into a cohesive pipeline. Specifically, it combines Principal Component Analysis (PCA) and t-Distributed stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, followed by K-Means clustering to enhance feature representation. Cluster assignments (Benign/Attack) derived from the t-SNE-reduced data are incorporated as a new feature, enabling classifiers to detect latent attack patterns more effectively than traditional methods. When evaluated on a real-world SDN dataset, the method significantly outperformed existing approaches, achieving over 99.9% accuracy with Random Forest (RF) and XGBoost classifiers. Notably, XGBoost processed data in just 0.2 seconds, demonstrating exceptional suitability for real-time applications. Furthermore, the ensemble model, combining RF and XGBoost, achieved the highest overall accuracy of 99.93%, an AUC of 1.00, and an execution time of 1.61 seconds. These results underscore the proposed approach's substantial improvements in accuracy and computational efficiency making it a robust solution for addressing critical network security challenges in SDN environments. © 2024 IEEE.

关键词： Dimensionality reduction

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Resource-Limited Localized Adaptive Adversarial Training for Machine learning Model 21

Resource-Limited Localized Adaptive Adversarial Training for...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Rajhi, Mohammed Pissinou, Niki Florida International University School of Computing and Information Sciences MiamiFL United States Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia

ISBN: (纸本)9798350329223

Balancing robustness and computational efficiency in machine learning models is challenging, especially in settings with limited resources like mobile and IoT devices. This study introduces Adaptive and Localized Adversarial Training (ALAT), an optimization approach that balances these competing needs. ALAT combines generalized models with localized adversarial perturbations and adaptive data augmentation. As a result, the model strengthens its weak points without needing to explore all possible adversarial threats, saving computational effort Our data shows that ALAT-trained models perform robustly with less computational cost compared to traditional adversarial training methods. This adaptability makes ALAT suitable for various machine learning architectures and particularly valuable in resource-constrained settings requiring resilience to adversarial threats. © 2023 IEEE.

关键词： Computational efficiency

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Solar power plant data analysis and prediction using different techniques of machine learning 21

Solar power plant data analysis and prediction using differe...

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21st IEEE international conference on Sciences and Techniques of Automatic Control and Computer engineering, stA 2022

作者： Arab, Marwa Ben Rekik, Mouna Krichen, Lotfi Electrical Engineering Department Sfax Tunisia

ISBN: (纸本)9781665482615

Machine learning is a subassembly of artificial intelligence and has been an important part of digital solutions, attracting a lot of attention in the digital field. The field of machine learning, which enables computers to build accurate predictions based on huge past data, has grown significantly in recent years due to the rapid growth of computers' data storage and processing capabilities. Solar energy data analysing and predicting is a key factor in improving the competitiveness, and performance of solar power plants (SPPs) in the energy market and reducing the dependence on fossil fuels. This paper presents a solar power plant data analysing and forecasting based on machine learning techniques. The relevance of the studied paper is analysing data as irradiation, temperature, and generated Alternative Current (AC) power generated from inverters, and in order to enhance security, risk, and fraud detection in the SPP. Also, it aims in evaluating real-time and short-term SPP forecasting to ensure safety needs of the field using three technique of machine learning: Linear Regression, Decision Tree, and Random Forest. © 2022 IEEE.

关键词： Solar energy

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Harmonizing Generalization and Personalization in Federated Prompt learning 41

Harmonizing Generalization and Personalization in Federated ...

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41st international conference on Machine learning, ICML 2024

作者： Cui, Tianyu Li, Hongxia Wang, Jingya Shi, Ye ShanghaiTech University China

Federated Prompt learning (FPL) incorporates large pre-trained Vision-Language models (VLM) into federated learning through prompt tuning. The transferable representations and remarkable generalization capacity of VLM make them highly compatible with the integration of federated learning. Addressing data heterogeneity in federated learning requires personalization, but excessive focus on it across clients could compromise the model's ability to generalize effectively. To preserve the impressive generalization capability of VLM, it is crucial to strike a balance between personalization and generalization in FPL. To tackle this challenge, we proposed Federated Prompt learning with CLIP Generalization and low-rank Personalization (FedPGP), which employs pre-trained CLIP to provide knowledge-guidance on the global prompt for improved generalization and incorporates a low-rank adaptation term to personalize the global prompt. Further, FedPGP integrates a prompt-wise contrastive loss to achieve knowledge guidance and personalized adaptation simultaneously, enabling a harmonious balance between personalization and generalization in FPL. We conduct extensive experiments on various datasets to explore base-to-novel generalization in both category-level and domain-level scenarios with heterogeneous data, showing the superiority of FedPGP in balancing generalization and personalization. Copyright 2024 by the author(s)

关键词： Federated learning

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Towards Responsible Decisions with Limited Training data Using Human-in-the-Loop 20th

Towards Responsible Decisions with Limited Training Data Us...

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Online Optimization Method for Virtually Coupled Train Operation Deviation from Reference Curve 21

Online Optimization Method for Virtually Coupled Train Opera...

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21st international conference on Networking, Sensing and Control, ICNSC 2024

作者： Lang, Yinghui Liu, Hongjie Liao, Yanli Li, Bosi Wang, Yihan Chai, Ming School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing100044 China Beijing Hollysys System Engineering Co. Ltd. Xi'an Branch Xi'an710061 China

ISBN: (纸本)9798350365221

Virtually coupled train set (VCTS) has become a popular research topic in the railway industry in recent years. It is considered one of the solutions to the uneven spatial and temporal distribution of passenger flow in urban rail transit. A VCTS consists of multiple train units (TUs). The overall operation of the VCTS relies on the coordinated efforts of each TU. A feasible approach involves pre-planning reference curves for the TUs, with the Automatic Train Operation (ATO) system tracking these curves. However, as the TUs operate at close intervals, the control strategy needs to ensure safety and precise control. Consequently, the reference curve of the TU is characterized by frequent changes in acceleration. As a result, the ATO system is prone to deviating from the curve. This paper builds a time-varying vehicle dynamics model and a controller performance model based on data learning. Using these models, we predict deviations and construct an online optimization model. A mesh adaptive direct search (MASD) algorithm is designed to solve for online adjustment curves, promptly correcting any deviations. Simulation experiments demonstrate the effectiveness of the proposed online optimization method when combined with PID and MPC alzorithms. © 2024 IEEE.

关键词： Urban transportation

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Covid-19 data Preprocessing Approach in Machine learning for Prediction 1st

Covid-19 Data Preprocessing Approach in Machine Learning for...

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1st EAI international conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications, SAFER-TEA 2023

作者： Kotva Goudoungou, Samuel Dayang, Paul Tchomte, Naomi Dassi Ngossaha, Justin Moskolaï Moffo, Fabien Mouomene Mitton, Nathalie Department of Mathematics and Computer Science Faculty of Science The University of Ngaoundere Ngaoundere Cameroon Department of Mathematics and Computer Science University Institute of Technology The University of Ngaoundere Ngaoundere Cameroon Department of Computer Science Faculty of Science The University of Douala Douala Cameroon National Institute for Research in Digital Science and Technology University of Lille Villeneuve D’ascq59650 France

ISBN: (纸本)9783031563959

Artificial Intelligence (AI) is a vast field that allows the development of programs capable of simulating human intelligence. One of the most used AI techniques that is very important the preparation of raw data which is called data preprocessing in Machine learning. data preprocessing is a technique that makes the data clean for machine learning and improves the performance of the model. With the different steps that we found already defined we set up a simpler architecture for data preprocessing. For that purpose, we worked with 5644 data simple of Covid-19. After analyzing the contours of our data;we have as steps of this architecture first the collection of data, the cleaning of these data, the encoding, the normalization of the data, the imputation to manage the missing values, then the selection of variables and finally the selection of features. We obtain an accuracy of 78% as a result of our experimentation with a very small error rate. This Covid-19 detection prediction result shows that data preprocessing plays an important role in Machine learning. © ICst Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： intelligent systems

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