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MAD: A Meta-Learning Approach to Detect Advanced Persistent Threats using Provenance data in Industrial IoT 40

MAD: A Meta-Learning Approach to Detect Advanced Persistent ...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Saha, Bikash Rani, Nanda Shukla, Sandeep Kumar Indian Institute of Technology Kanpur Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331532819

The increasing reliance on Industrial Internet of Things (IIoT) systems in critical infrastructure has made these environments prime targets for stealthy and prolonged Advanced Persistent Threats (APTs) that evade traditional security systems. This study introduces MAD (Meta-learning based APT Detection), a novel framework designed to detect APTs in IIoT environments by leveraging provenance data. This data provides a detailed view of system-level interactions, capturing subtle and persistent malicious behaviors. We evaluate MAD on the CICAPT-IIoT dataset, which simulates real-world APT scenarios with severe class imbalance due to the "low and slow"nature of APT attacks. MAD includes two variants: MAD v1, utilizing a multi-layer perceptron (MLP), and MAD v2, employing Model-Agnostic Meta-Learning (MAML) to improve detection of rare attack behaviors. MAD v1 achieves F1-score of 0.91 and outperforms the existing baselines. MAD v2 achieves F1-score of 0.95 surpassess existing baselines as well as MAD v1, which demonstrates its enhanced capability to detect rare APT activities despite the dominance of benign samples. This work combines provenance-based detection with meta-learning to address class imbalance and enhance APT detection in IIoT, demonstrating MAD's potential in tackling emerging IIoT security challenges. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Dhondiyal, Shiv Ashish Dimri, Sushil Chandra Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1-score of 97%, 96% and 98% respectively. © 2024 IEEE.

关键词： Electroencephalography

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Breast Cancer Detection with Machine Learning-A Review 2

Breast Cancer Detection with Machine Learning-A Review

引用

2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Meghana, Karnam Nandal, Neha Tanwar, Rohit Goel, Lipika Chhabra, Gunjan Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India University of Petroleum and Energy Studies Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665491990

Malignancy in the breast is a significant public health concern, where timely identification is essential for effective treatment. Machine Learning (ML) and Deep Learning (DL) algorithms are potential tools for prompt detection og breast malignancy through examination of medical images such as mammograms. Convolutional neural networks (CNNs), transfer learning, and ensemble learning are some of the recent techniques being used in this field. Despite the advantages of ML and DL algorithms for breast cancer detection, there are still several challenges that need to be addressed. The lack of diversity in the datasets used to train algorithms is one major challenge, with many datasets based on specific populations that may not represent others. Highly annotated data is also limited in medical field. The objective of this study is to provide researchers with valuable insights and guidance. © 2023 IEEE.

关键词： Convolutional neural networks

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Review on TIRADS Scoring Based Thyroid Nodule Malignancy Detection 9

Review on TIRADS Scoring Based Thyroid Nodule Malignancy Det...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Cloud-Based Water Parameter Monitoring for Smart Fisheries

Cloud-Based Water Parameter Monitoring for Smart Fisheries

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bahadur, Arshad Mohammad Sakib, Najmus Moinuddin, Kazi Md. Hussein Islam, Tanzimul Islam, A.K.M. Muzahidul United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350364866

The Cloud-based Water Parameter Monitoring System leverages cloud and IoT technologies for the precise monitoring of water quality across various water bodies. In Bangladesh, fish is a vital dietary component, consumed more frequently and in larger quantities than any other animal-source food. The fisheries sector greatly contributes to GDP, employment, and it is the second-largest export product of the country. However, pollution and inadequate real-time water quality monitoring significantly impact the industry, costing thousands of crores. This research involved collecting water parameter data from different water bodies using specialized sensors connected to Arduino devices. The data was processed and stored on an edge server before being periodically sent to a cloud server. We developed a user-friendly web application and integrated an alert system to notify farm workers when parameters deviate from acceptable ranges. The goal is to enhance sustainable fish farming practices by improving operational efficiency, reducing environmental impact, and ensuring optimal fish health through IoT, Machine Learning techniques, and Cloud technologies. © 2024 IEEE.

关键词： Fish

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Handling the Challenges of Microbiome data through Supervised Autoencoders for the Non-invasive Disease Diagnosis

Handling the Challenges of Microbiome Data through Supervise...

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2024 Discovery science Late Breaking Contributions, DS-LB 2024

作者： Buttaro, Veronica Ceci, Michelangelo Pio, Gianvito Dept. of Computer Science University of Bari Aldo Moro Bari Italy Data Science Lab CINI Consortium Rome Italy Dept. of Knowledge Technologies Jozef Stefan Institute Ljubljana Slovenia

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End-to-End SNR Capacity and throughput enhancement Using Non-Orthogonal Multiple access and Massive MIMO in 5G 4

End-to-End SNR Capacity and throughput enhancement Using Non...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350349900

This study provides a detailed analysis of the potential improvements in 5G network performance due to the adoption of NOMA and Massive MIMO technologies. Obtaining higher data rates, reduced latency, and increased overall network capacities remain a primary objective, and the research provides a comparison based on simulated data. Simulation models are designed to compare the proposed model against the conventional implementation of 5G networks based on a set of primary indicators, namely, throughput and latency. An abstract Wave Efficiency indicator is also presented to showcase a new metric for determining the qualitative improvements in signal processing and spectrum utilization. As the results of the research have demonstrated, the incorporation of NOMA and Massive MIMO increases overall network throughput while reducing latency. This demonstrates the potential contributions of these technologies to making wireless communications systems more efficient and reliable. The introduction of the Wave Efficiency is offered as a measurable variable for displaying the qualitative improvement in the wireless communications technologies, facilitating further implementation of NOMA and Massive MIMO in 5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350388534

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes! © 2024 IEEE.

关键词： Lung cancer

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Machine Learning based Anxiety Detection using Physiological Signals and Context Features 2

Machine Learning based Anxiety Detection using Physiological...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Jain, Arushi Kumar, Rajeev National Institute of Technology Hamirpur Dept. of Computer Science and Engineering Hamirpur India

ISBN: (纸本)9798350371314

Anxiety is a common mental disorder that affects millions of people worldwide. Anxiety can be detected using physiological signals such as heart rate, skin conductance, blood pressure, and respiration. Machine learning is the ability to learn from data and forecast future events. Physiological signals can be analyzed and classified into different anxiety levels using machine learning. This paper aims to present the contribution of context features in detection of anxiety using physiological signals via machine learning. It is an imbalanced multiclassification problem using ECG and EDA signals with context features collected from 15 healthy individuals. These signals of sampling rate 700 Hz collected using RespiBAN from the Wearable Stress and Affect Detection (WESAD) dataset are used here with the 6-STAI questionnaire and the context collected about the subject during the experiment. Scores for each subject are calculated from this questionnaire which categorized subjects into different levels of anxiety. Multiple features are extracted from these signals and scaling is done on the extracted features. With the help of different data balancing techniques and different machine learning algorithms, this study will classify anxiety into three classes, namely, low, moderate and high. Classification of anxiety with fusion of multimodal physiological signals gave better results as compared to single modal physiological signals with an accuracy of 89.8% with ECG signal, 85.9% with EDA signal and 96.7% with ECG and EDA both using Gradient Tree Boosting algorithm with LOOCV after random oversampling. While, the best results are achieved when multimodal physiological signals are fused with context features, it gives the accuracy of 97.3% with Gradient Tree Boosting and LOOCV algorithm. © 2024 IEEE.

关键词： Electrocardiograms

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