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Predicting Highway Accident Severity in Brazil: Environmental Factors and Vehicle Features

Predicting Highway Accident Severity in Brazil: Environmenta...

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international conference on Distributed computing in Sensor Systems (DCOSS)

作者： Júlio C. W. Scholz Yan P. Pinheiro Thiago H. Silva Informatics Univ. Tecnológica Federal do Paraná Curitiba Brazil

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

This work supports the problematization of the high rate of traffic accidents due to the increased use of automobiles for utility or transport in Brazil. Every year, the lives of approximately 1.3 million people are interrupted due to traffic accidents worldwide. In this regard, this study proposes the use of data mining as an approach to analyze and explore datasets of traffic accidents that occurred on Brazilian highways between the years 2017 and 2022, as provided by the Federal Highway Police. Additionally, vehicle price data were included, allowing for a more comprehensive analysis that also considers the financial value of the vehicles. The goal is to assess the predictive capability of classification models regarding the severity of accidents, focusing on vehicle characteristics and environmental factors. By applying classification algorithms and machine learning explainability techniques, we acquired relevant knowledge regarding the studied data, contributing to understanding and preventing accidents. As a result, the attributes related to vehicle characteristics had a more positive impact on the predictive capability of the models when compared to the attributes describing the environment and other variables.

关键词： Law enforcement Roads Focusing machine learning Predictive models Environmental factors Internet of Things

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Edge computing Ransomware Detection in IoT Using machine learning

Edge Computing Ransomware Detection in IoT Using Machine Lea...

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international conference on computing, Networking and Communications (ICNC)

作者： Tejesh Radhakrishna Nahid Ebrahimi Majd Department of Computer Science and Information Systems California State University San Marcos United States

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

The resurgence of ransomware has emerged as a pressing security threat in computer networks and Internet connected machines and IoT devices. To address this challenge, accurate ransomware detectors are required to automatically detect and block the malicious traffic. Most ransomware detectors only detect whether the traffic is benign or ransomware. However, detecting the family of ransomware would be greatly useful to promptly eliminate or mitigate its destructive effects. To tackle this issue, we propose machine learning models that accurately detect each ransomware family. Our models aim to detect the ransomware network traffic and thwart it at the network edge before it enters the network. Considering that ransomwares directly work with the memory dump and file system, the information extracted from the operating system's functions on the memory dump is very useful to detect a ransomware attack. However, that information could be collected only when the ransomware has already infected the device and is actively disrupting the file system. In our research, we propose a framework that blocks the ransomware at the network edge. This restricts our research to using a dataset that extracts network traffic features with no access to the device's operating system's functionalities. An edge computing intrusion detection system is also beneficial for resource contained network devices, such as IoT, which have limited computational resources and cannot dynamically analyze the network traffic and run a strong intrusion detection system. We worked on CICAndMal2017 dataset and proposed a feature selection-based framework along with different machine learning models. We also applied a data augmentation technique to the training set to strengthen the data used to build our models. We extensively studied our proposed framework. Our experimental results demonstrated that chi-square feature selection with Random Forest and XGBoost models surpass other models and the state of the a

关键词： File systems Computational modeling Image edge detection Intrusion detection Telecommunication traffic Detectors Feature extraction

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Intelligent Intrusion Detection System Using Deep learning Technique 1st

Intelligent Intrusion Detection System Using Deep Learning T...

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1st international conference on computing, Communication and learning, CoCoLe 2022

作者： Henry, Azriel Gautam, Sunil Department of Computer Sciences and Engineering Institute of Advanced Research Gujarat Gandhinagar India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat Gandhinagar India

ISBN: (纸本)9783031217494

There is constant growth in the digitization of information across the world. However, this rapid growth has raised concerns over the security of the information. Today’s internet is made up of nearly half a million different networks. Network intrusions are very common these days which put user information at high risk. An intrusion detection system (IDS) is a software/system to analyze and monitor the data for the detection of intrusions in the host/network. An intrusion Detection System competent in detecting zero-day attacks and network anomalies is highly demanded. Researchers have used different methods to develop robust IDS. However, none of the methods is exceptionally well and meets every requirement of IDS. machine learning/Deep learning (ML/DL) are among the widely used methods to develop IDS. This proposed technique uses a DL model, Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU) framework. There are several datasets to evaluate the performance of the learning techniques. CICIDS 2017 is the dataset that contains a variety of cyber-attacks. The proposed technique will use the same to evaluate the deep learning technique. Moreover, the study will also showcase the comparison between the results of existing machine learning algorithms and the proposed algorithm. The comparison would be done on different matrices such as True positive (TP) and False positive (FP) rates, accuracy, precision, etc. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Zero-day attack

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Verifiable machine learning Models in Industrial IoT via Blockchain 14th

Verifiable Machine Learning Models in Industrial IoT via B...

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12th international Advanced computing conference, IACC 2022

作者： Stodt, Jan Ghajar, Fatemeh Ghovanlooy Reich, Christoph Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen Germany Offenburg University of Applied Sciences Offenburg Germany Plymouth University Portland Square PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9783031356438

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model. © 2023, Springer Nature Switzerland AG.

关键词： Blockchain

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Sniffing Android Malware Using Deep learning 26th

Sniffing Android Malware Using Deep Learning

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26th annual international conference on Advanced computing and Communications, ADCOM 2020

作者： Tirkey, Anand Mohapatra, Ramesh Kumar Kumar, Lov National Institute of Technology Rourkela Odisha Rourkela India BITS Pilani Hyderabad Campus Telangana India

ISBN: (纸本)9789811900181

Android malware classification problem seems to have been solved with published AUC and F1 scores up to 0.99 or is it a facade, hiding an inherent problem? In this paper, we bring forward a novel method of recognising android malware using object-oriented software metrics-based dataset and deep learning. We realise that the real-world android malware is a minority class and its distribution according to 2017 Google’s android security report, and Miller et al. [17] is estimated to be about 8–12%. The malware distribution in our dataset of 93K samples spanning over three years is around 10.9%. In this study, four data-sampling methods, six feature selection techniques and five deep learning networks with varying hidden layers are used over the imbalanced dataset of 93K samples. A total of 120 different machine-learned models are developed, and its classification potential is compared using area under ROC curve (AUC) metric. Finally, a machine-learned model obtained using upscale sampling (USD) data-sampling method applying significant set of metrics (SGM) feature selection technique over deep learning network with two hidden layers (DL2) yields a better AUC value of 0.893681. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Prompt-Based Generative Multi-label Emotion Prediction with Label Contrastive learning 11th

Prompt-Based Generative Multi-label Emotion Prediction with ...

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11th CCF international conference on Natural Language Processing and Chinese computing (NLPCC)

作者： Chai, Yuyang Teng, Chong Fei, Hao Wu, Shengqiong Li, Jingye Cheng, Ming Ji, Donghong Li, Fei Wuhan Univ Sch Cyber Sci & Engn Minist Educ Key Lab Aerosp Informat Secur & Trusted Comp Wuhan Peoples R China Natl Univ Singapore Sch Comp Singapore Singapore Zhengzhou Univ Affiliated Hosp 1 Zhengzhou Peoples R China

ISBN: (纸本)9783031171208;9783031171192

Multi-label emotion prediction, which aims to predict emotion labels from text, attracts increasing attention recently. It is ubiquitous that emotion labels are highly correlated in this task. Existing state-of-the-art models solve multi-label emotion prediction in sequenceto-sequence (Seq2Seq) manner, while such label correlations are merely leveraged in decoding side. In this work, we propose an emotion prediction framework to jointly generate emotion labels and template sentences via Seq2Seq language model. On the one hand, our template-based natural language generation method makes better use of generative language model compared with generating label sequences in the prior Seq2Seqbased generative classification model. On the other hand, we introduce the Correlation-based Label Prompts (CLP) through soft prompt learning and contrastive learning, which enables our model to further consider emotion label correlations in encoding side. To demonstrate the effectiveness of our prompt-based generative multi-label emotion prediction model, we perform experiments on the GoEmotions and SemEval 2018 datasets, achieving competitive results, outperforming 7 baselines w.r.t. 3 evaluation metrics. In-depth analyses show the generation manner is much more impressive compared with generating label sequences and our model is particularly effective in label correlation modeling.

关键词： Emotion prediction Text generation Prompt learning Contrastive learning

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Diabetic Retinopathy Detection using Deep learning 2020

Diabetic Retinopathy Detection using Deep Learning

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4th international conference on machine learning and soft computing (icmlsc)

作者： Nguyen, Quang H. Muthuraman, Ramasamy Singh, Laxman Sen, Gopa Anh Cuong Tran Nguyen, Binh P. Chua, Matthew Hanoi Univ Sci & Technol Hanoi Vietnam Natl Univ Singapore Inst Syst Sci Singapore Singapore Victoria Univ Wellington Sch Math & Stat Wellington New Zealand

ISBN: (纸本)9781450376310

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. DR is the leading cause of blindness among working aged adults around the world and estimated it may affect more than 93 million people. Progression to vision impairment can be slowed or controlled if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. Currently, detecting DR is a time-consuming and manual process, which requires an ophthalmologist or trained clinician to examine and evaluate digital color fundus photographs of the retina, to identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. The automated method of DR screening will speed up the detection and decision-making process, which will help to control or manage DR progression. This paper presents an automated classification system, in which it analyzes fundus images with varying illumination and fields of view and generates a severity grade for diabetic retinopathy (DR) using machine learning models such as CNN, VGG-16 and VGG-19. This system achieves 80% sensitivity, 82% accuracy, 82% specificity, and 0.904 AUC for classifying images into 5 categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR.

关键词： Diabetic retinopathy classification image processing deep learning segmentation severity grade

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A Novel Deep learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks

A Novel Deep Learning based Model to Defend Network Intrusio...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Khushnaseeb Roshan Aasim Zafar Shiekh Burhan Ul Haque Department of Computer Science Aligarh Muslim University Aligarh India

Network Intrusion Detection System (NIDS) is an essential tool in securing cyberspace from a variety of security risks and unknown cyberattacks. A number of solutions have been implemented for machine learning (ML), and Deep learning (DL) based NIDS. However, all these solutions are vulnerable to adversarial attacks, in which the malicious actor tries to evade or fool the model by injecting adversarial perturbed examples into the system. The main aim of this research work is to study powerful adversarial attack algorithms and their defence method on DL-based NIDS. Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) are four powerful adversarial attack methods implemented against the NIDS. As a defence method, Adversarial Training is used to increase the robustness of the NIDS model. The results are summarized in three phases, i.e., 1) before the adversarial attack, 2) after the adversarial attack, and 3) after the adversarial defence. The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset is used for evaluation purposes with various performance measurements like fl-score, accuracy etc.

关键词： Training Deep learning Measurement Jacobian matrices Neural networks Network intrusion detection Cyberspace

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FairComp: Workshop on Fairness and Robustness in machine learning for Ubiquitous computing

FairComp: Workshop on Fairness and Robustness in Machine Lea...

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Adjunct Proceedings of the 2023 ACM international Joint conference on Pervasive and Ubiquitous computing & the 2023 ACM international Symposium on Wearable computing

作者： Sofia Yfantidou Dimitris Spathis Marios Constantinides Tong Xia Niels Van Berkel School of Informatics Aristotle University of Thessaloniki Greece Nokia Bell Labs United Kingdom and University of Cambridge United Kingdom Nokia Bell Labs United Kingdom and Computer Science University College London United Kingdom Computer Lab University of Cambridge United Kingdom Department of Computer Science Aalborg University Denmark

ISBN: (纸本)9798400702006

This paper explores the intersection of Artificial Intelligence and machine learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.

关键词： bias fairness machine learning mobile computing responsible artificial intelligence

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Edge computing based IDS Detecting Threats using machine learning and PyCaret

Edge Computing based IDS Detecting Threats using Machine Lea...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), international conference on

作者： Amit Kumar Sanjeev Kumar Vivek Kumar Amrita Kumari Ashish Saini Sarthak Gupta Gurukula Kangri (Deemed to be University) Haridwar India G L Bajaj Institute of Technology and Management Greater Noida Quantum University Roorkee Vivek College of Education Bijnor

The Internet of Things is getting connected with the edge computing network day by day. These devices generate large amounts of confidential data that is stored and processed on edge computing. There is a need to protect such data and edge computing networks from new types of attacks. Therefore, intrusion detection system (IDS) is an effective defense to secure edge computing network. In this study, an IDS based on machine learning is designed and developed. This article elaborates the technique of Recursive Feature Elimination (RFE) using the Random Forest algorithm (RF). The contribution of this paper is to select a relevant and efficient feature set using the recently developed Python library PyCaret. This paper proposes a novel approach for detecting new types of attacks in network traffic. The recently released dataset CICIDS2017 has been used to evaluate the proposed method, which includes state-of-the-art attacks. In this paper, two classification techniques are also used to build the IDS model. The first is a binary classification technique using the recently developed PyCaret library, and the second is a multiclass classification technique using RFE.

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