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Detect and Alleviate DDoS Attacks in Cloud Environment 1

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8th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Kulwanth, Bandi Srinivasarengan, V. Anish, Peddinte Abirami, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789819976331

ISBN: (纸本)9789819976324

The Internet is now a crucial aspect of our lives, and distributed computing has grown significantly in size, capability, and complexity, becoming an essential element of our daily lives. There are numerous vulnerabilities in distributed network computing, including viruses, worms, distributed denial of service (DDoS), DoS, and more. One of the more newsworthy attacks recently has been distributed denial-of-service attacks. Attacks that cause denials of service (DoS) have developed and had an influence on the Internet infrastructure. A DDoS attack aims to prevent the targeted users from accessing a computer resource. In this paper, we perform a comprehensive survey of detecting DDoS packets via couple of machine learning algorithms such as Random Forest, Gradient Descent, decision tree, Multi-layer Perceptron, and radial basis function using the CIC-DDoS2019 dataset. We also combine all our machine learning models into one hybrid model via an ensemble classifier. After that, we experiment with a DDoS attack on a local server via Ufonet and capture the data packets during the attack using the Wireshark tool. The captured data will be converted to a CSV file and can be given as input to machine learning models. The results show that each machine learning algorithm is predicting DDoS packets incoming into the server. The experimental results show that the proposed DDoS attack detection method based on machine learning has a good detection rate for the current popular DDoS attack. Finally, directions for future research work have been pointed out. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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machine learning-based target prediction for terrorist attacks 2

Machine learning-based target prediction for terrorist attac...

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2023 2nd international conference on machine Vision, Automatic Identification and Detection, MVAID 2023

作者： Pan, Xiaohui Zhang, Tao School of Artificial Intelligence and Law Shanghai University of Political Science and Law Shanghai China Enflame Technology Shanghai China

To address the problem that terrorist attacks are frequent and attack targets are widely difficult to predict, we use multidimensional features of terrorist attacks to predict terrorist targets based on a quantitative statistical analysis of data in the global terrorism database (GTD) from 1970 to 2019. In this paper, a machine learning-based prediction model (i.e., a classifier framework) is proposed. The model pre-processes GTD data and uses the OneHotEncoder and KBinsDiscretizer methods for data category transformation, classifies terrorist targets by four algorithms - SVM, decision trees, KNN and DNN. Each algorithm was trained with their parameters optimised, and the performance of the proposed model was tested and evaluated. The experimental results show that the prediction model achieved good performance in predicting the attack targets. The neural network had the best prediction performance in predicting twenty-two attack targets with 100% accuracy, indicating that the proposed model is accurate and effective. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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FedShare: Secure Aggregation based on Additive Secret Sharing in Federated learning 23

FedShare: Secure Aggregation based on Additive Secret Sharin...

引用

27th international database Engineered Applications Symposium, IDEAS 2023

作者： Fazli Khojir, Hamid Alhadidi, Dima Rouhani, Sara Mohammed, Noman University of Windsor WindsorON Canada University of Manitoba Manitoba Winnipeg Canada

ISBN: (纸本)9798400707445

Federated learning is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. In FedAvg, the main federated learning algorithm, clients train machine learning models locally and share the trained model with the server. While the sensitive data will never be sent to the server, a malicious server can construct the original training data by having access to the clients' models in each training round. Secure aggregation techniques such as cryptography, trusted execution environment, or differential privacy are used to solve this problem. However, these techniques incur computation and communication overhead or affect the model's accuracy. In this paper, we consider a secure multi-party computation setup where clients use additive secret sharing to send their models to multiple servers. Our solution provides secure aggregation as long as there are at least two non-colluding servers. Moreover, we provide mathematical proof to show that the securely aggregated model at the end of each training round is exactly equal to the one provided by FedAvg without affecting accuracy and with efficient communication and computation. In comparison with SCOTCH, the state-of-the-art secure aggregation solution, experimental results show that our approach is 557% faster compared to SCOTCH and at the same time it reduces the communication cost of clients by 25%. Additionally, the accuracy of the trained model is exactly as FedAvg under balanced, unbalanced, IID, and Non-IID data distributions while it is only 8% slower. © 2023 ACM.

关键词： Sensitive data

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An Autoencoder-Based Approach for DDoS Attack Detection Using Semi-Supervised learning

An Autoencoder-Based Approach for DDoS Attack Detection Usin...

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2023 international conference on Next-Generation Computing, IoT and machine learning, NCIM 2023

作者： Fardusy, Tamanna Afrin, Sayma Sraboni, Ifrat Jahan Dey, Uttam Kumar Uttara University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

A Distributed Denial of Service (DDoS) attack is a malicious cyber-attack strategy that seeks to disrupt normal traffic to a specific server by overwhelming it with an excessive amount of requests or data. In recent years, there has been a persistent increase in the use of DDoS attacks to exploit Internet networks. Although advanced intrusion detection and protection systems have been developed, network security remains a difficult problem and requires the development of effective defense mechanisms to detect these threats. Most of the current approaches are based on supervised learning which requires large and well-balanced datasets. Still, they struggle to identify new types of attacks. To address these issues, we propose a semi-supervised DDoS detection model using Autoencoder (AE) and Support Vector machine (SVM). We compared our proposed approach with various supervised and semi-supervised models on the CICDDoS2019 dataset. Our proposed model outperformed the other models by achieving an accuracy of 99.57% and over 99% precision, recall, and F1 score. © 2023 IEEE.

关键词： Denial-of-service attack

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A Novel Weakly Supervised Semantic Segmentation Ensemble Framework for Medical Imaging

A Novel Weakly Supervised Semantic Segmentation Ensemble Fra...

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international Joint conference on Neural Networks (IJCNN)

作者： Ostrowski, Erik Prabakaran, Bharath Srinivas Shafique, Muhammad Tech Univ Wien TU Wien Inst Comp Engn Vienna Austria New York Univ Abu Dhabi NYUAD Div Engn eBrain Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350359329;9798350359312

The use of deep learning networks for vision based computer aided diagnostics (CAD) offers a tremendous opportunity for medical practitioners. However, state-of-the-art vision-based CAD systems rely on huge pixel-wise annotated datasets. Such datasets are rarely available, thus severely limiting the applicability of vision-based CAD systems. Hence, semantic segmentation with image labels offers a viable alternative. Semantic segmentation with image labels is well studied in a general context but seldom applied in the medical sector. The major challenge in applying semantic segmentation with image labels in the medical sector is that predicting on medical datasets is more complex than in the general context. Thus, directly applying methods for semantic segmentation with image labels like class activation maps (CAMs) on medical data generates insufficient results. However, state-of-the-art approaches rely on CAMs as a foundation. To address this problem, we propose a framework to extract useful information from particular low-quality segmentation masks. We achieve this by using our observations that the low-quality predictions have very low false negative detections, and multiple low-quality predictions show high variance among each other. We evaluated our framework on the popular multi-modal BRATS and prostate DECATHLON segmentation challenge datasets to demonstrate an improved dice score of up to 8% on BRATS and 6% on DECATHLON datasets compared to the previous state-of-the-art.

关键词： Semantic Segmentation CAMs Deep learning Weakly Supervised Semantic Segmentation Deep learning machine learning Medical Imaging GradCAM Deep Neural Networks DNN

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Comparative Analysis of Mental Health Prediction Using machine learning: A Study on Meditators and Non-Meditators

Comparative Analysis of Mental Health Prediction Using Machi...

引用

2024 international conference on Communication, Computer sciences and Engineering, IC3SE 2024

作者： Harsha, M.G. Kaur, Amandeep Lovely Professional University Dept Of Computer Science Punjab Phagwara India

ISBN: (纸本)9798350366846

A day-by-day world evolving problems related to human beings are also increasing continuously. Among these issues, health-related concerns stand out as particularly significant. According to the World Health Organization in 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder, with depression and anxiety disorder being the most common. As a result of the COVID-19 pandemic in 2020, there was a significant increase in the number of people suffering from anxiety and depression. According to early projections, major depressive disorders and anxiety disorders will both rise by 26% and 28% in just one year, respectively. The early prediction of Mental health is very important to take Medicine This research article examines how effective machine learning algorithms are in predicting psychological symptoms in those who practice meditation compared to those who do not. Given the rising popularity of meditation as a self-care treatment, it is essential to understand its possible effects on psychological well-being. The research uses psychological symptom ratings and demographic data from both those who practice meditation and those who do not. Several machine learning algorithms are used to forecast psychiatric symptoms using variables like meditation habits, demographics, and other pertinent characteristics. Among the various algorithms, SVM and logistic regression performed very well and meditators showed high resilience towards anxiety, depression, and stress as compared to those who are not. © 2024 IEEE.

关键词： Forecasting

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Online learning Behavior Analysis and Achievement Prediction with Explainable machine learning 1

引用

1st international conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Peng, Haowei Yu, Xiaomei Jiao, Xiaotong Yin, Qiang Zhao, Lixiang Shandong Normal University Jinan China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

With the development of computer technology, "Internet + education" has become a hot topic, which has promoted the development of online education. The impact of the epidemic has brought new challenges to online education, and the effect of online teaching has sparked extensive research. Why online learning has not received satisfying learning effect? And how to improve the quality of online education have become the focus of research. To address these challenges in online learning, this paper studies the important factors affect the students’ learning achievements based on the explicit and implicit features extracted from the online learning behavior data. And a wealth of machine learning models are explored to predict the learner’s achievement, including the original linear models, the ensemble learning models and the advanced deep learning models. With extensive experimental results achieved, we gain reasonable explanation on the influence factors hander the learning achievement, and predict the learning achievements with the explainable machine learning models. Finally, some suggestions are put forward to improve students’ learning behaviors, aiming to promote the quality of online learning in the long run. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Equivariant Diffusion for Crystal Structure Prediction 41

Equivariant Diffusion for Crystal Structure Prediction

引用

41st international conference on machine learning, ICML 2024

作者： Lin, Peijia Chen, Pin Jiao, Rui Mo, Qing Cen, Jianhuan Huang, Wenbing Liu, Yang Huang, Dan Lu, Yutong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China National Supercomputer Center Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China Institute for AIR Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP. Copyright 2024 by the author(s)

关键词： Crystal symmetry

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33.1 A High-Accuracy and Energy-Efficient Zero-Shot-Retraining Seizure-Detection Processor with Hybrid-Feature-Driven Adaptive Processing and learning-Based Adaptive Channel Selection

33.1 A High-Accuracy and Energy-Efficient Zero-Shot-Retraini...

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2024 IEEE international Solid-State Circuits conference, ISSCC 2024

作者： Liu, Jiahao Liu, Xiao Wang, Xu Xie, Ziyi Zhong, Zirui Fan, Jiajing Qiu, Hui Xu, Yiming Qin, Huajing Long, Yu Zhou, Yuhong Shen, Zixuan Zhou, Liang Chang, Liang Liu, Shanshan Lin, Shuisheng Wang, Chao Zhou, Jun University of Electronic Science and Technology of China Chengdu China West China Hospital of Sichuan University Chengdu China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350306200

Seizure-detection processors using machine learning have been proposed to detect the seizure onset of patients for alert or stimulation purposes [1-4]. Existing designs can achieve high accuracy when large amounts of seizure data from a patient is available for the training. However, unlike the collection of non-seizure data, the collection of seizure data with low occurrence requires patients to undergo time-consuming and costly hospitalization, which is difficult in practice. To address this issue, [5] proposed a zero-shot-retraining seizure-detection processor achieving relatively high accuracy without seizure data from the patient for retraining (the zero-shot here means zero seizure data [5]). Instead, only 2-minute non-seizure data from the patient is required to calibrate the clustered features extracted with a neural network (NN) pre-trained on the public seizure dataset. Although this addresses the aforementioned issue, the accuracy (sensitivity 90.3% & specificity 93.6%) of this design is still limited for practical use, and the energy consumption is large for wearable EEG monitoring devices like other seizure-detection processors using NN, as shown in Fig. 33.1.1. In this work, we propose a zero-shot-retraining seizure-detection processor requiring no seizure data from the patient for retraining as in [5] but with much higher accuracy and energy efficiency. It has two major features: 1) a hybrid-feature-driven adaptive processing architecture with on-chip learning requiring no seizure data from the patient to achieve ultra-low energy consumption and high accuracy, and 2) a learning-based adaptive channel-selection technique to further reduce the energy consumption while maintaining high accuracy. © 2024 IEEE.

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Solar Image Synthesis with Generative Adversarial Networks 23

Solar Image Synthesis with Generative Adversarial Networks

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23rd IEEE international conference on machine learning and Applications, ICMLA 2024

作者： Jiang, Haodi Wang, Jason T. L. Sam Houston State University Department of Computer Science HuntsvilleTX77341 United States New Jersey Institute of Technology Department of Computer Science NewarkNJ07102 United States

ISBN: (纸本)9798350374889

Solar activities are caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photo-spheric vector magneto grams of solar active regions have been used to analyze and forecast extreme space weather events such as flares and coronal mass ejections. Unfortunately, the most recent Solar Cycle 24 was relatively weak with few large events, though it is the only solar cycle in which time-series vector magnetograms have been available. In this paper, we focus on two NASA instru-ments, namely the Michelson Doppler Imager (MDI) onboard the Solar and Heliospheric Observatory (SOHO) launched in Solar Cycle 23 (1996-2008), and the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) launched in Solar Cycle 24 (2008-2019). While SOHOIMDI provides data from the more active Solar Cycle 23, it only offers line-of-sight (LOS) magneto grams without vector magnetograms. We propose Solar Image GAN (SIGAN), a generative adversarial network model designed to synthesize vector magnetic field images for Solar Cycles 23 and 24. SIGAN is trained using Ha images, SDOIHMI LOS, and vector magnetograms. It can generate vector magneto grams for both SDOIHMI and SOHOIMDI using Ha images and LOS magneto grams as input. Extensive experiments demonstrated the good performance of the proposed approach. © 2024 IEEE.

关键词： NASA

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