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intelligent automated Penetration Testing Using Reinforcement learning to Improve the Efficiency and Effectiveness of Penetration Testing 1

引用

11th EAI international conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications, MOBILWARE 2022

作者： Aqra, Mohammed Y. A. Di, Xiaoqiang Changchun University of Science and Technology Changchun China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

A penetration test is a process that involves planning, generating, and evaluating attacks that are designed to find and exploit vulnerabilities in digital assets. It can be used in large networks to evaluate the security of their infrastructure. Despite the use of automated tools, it can still be very time consuming and repetitive. the goal of this paper is to develop an intelligent automated penetration testing framework that uses reinforcement learning to improve the efficiency and effectiveness of penetration testing. It utilizes a model-based approach to automate the sequential decision-making process. the framework’s main component is a partial observed Markov decision process that is solved using an external algorithm. One of the biggest challenges in performing penetration tests on large networks is finding and evaluating clusters of vulnerabilities. this paper presents a method that combines a hierarchical network model with a cluster-based approach. It allows for faster and more accurate testing compared to previous methods. the results of the study show that the IAPTF method outperforms other approaches in terms of time, accuracy, and human performance. One of the main advantages of IAPTF is its ability to perform repetitive tests, which is typically not possible with traditional methods. this method could potentially replace manual pen testing. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

引用

11th international conference on Electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. this paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. the developed model will predict energy consumption in classrooms and laboratories based on weather conditions. the study is being conducted in two stages. the first stage describes how to collect data using electronic devices such as Internet of things (IoT) controllers of Smart Building Management Systems. the second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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Automatic Parking System Using Deep learning and Internet of things 4

Automatic Parking System Using Deep Learning and Internet of...

引用

4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Jyothi, Saddi Rami Reddy, M. Prasad, Chinthalapudi Siva Vara Rishita, Nimmagadda Prasad, Toka Shyam Satya Vara Ali, Shaik Irfan Lakireddy Bali Reddy College of Engineering Department of Information Technology Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Freshman Engineering Department Andhra Pradesh Mylavaram India

ISBN: (纸本)9798350349856

To address the labour - intensive requirements of traditional parking lot operations, we have developed an automated system for access control, occupancy tracking, and revenue processing. the proposed system employs computer vision and deep learning techniques to autonomously handle parking lot ingress and egress while reducing operating costs. Vehicles entering and exiting the automated parking facility will be detected using infrared sensors interfaced to a Raspberry Pi microcomputer. License plate images will be captured by a Pi camera module and processed in real-time using an optical character recognition (OCR) algorithm based on a convolutional recurrent neural network architecture optimized for number plate recognition. Human access control can be handled in parallel by leveraging object detection models like Faster R-CNN or YOLO to differentiate pedestrian from vehicle ingress. the onboard occupancy tracking functionality will alleviate the need for manual counting of available spaces. Additionally, real-time parking availability stats will be published to a monitoring website allowing customers to view open slots prior to arrival. Lot capacity constraints can be configured to automatically disable entrance gates when maximum capacity is reached. the system's sensor fusion and computer vision capabilities eliminate tedious manual tallying and oversight required by legacy parking systems. By autonomously controlling access gates, tracking and publishing occupancy stats, and extracting revenue data from license plates, the proposed automated parking lot system aims to reduce operating costs while creating a scalable solution applicable to facilities ranging from apartment complexes to large shopping malls. the use of a low-cost, open-source development platform promotes maintainability while allowing for customized enhancements. © 2024 IEEE.

关键词： Operating costs

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A Machine learning and Deep learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

the most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. the proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. the resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. the models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine learning and Deep learning is made here. the accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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Driver Drowsiness Detection Using Deep learning Models Based on Different Camera Positions 11

Driver Drowsiness Detection Using Deep Learning Models Based...

引用

11th international conference on Information and Communication Technology, ICoICT 2023

作者： Bin Rozaimi, Ismail Danish Zaman, Fadhlan Hafizhelmi Kamaru Abdullah, Syahrul Afzal Che Abidin, Husna Zainol Mazalan, Lucyantie Universiti Teknologi MARA Vehicle Intelligence and Telematics Lab School of Electrical Engineering College of Engineering Selangor Shah Alam40450 Indonesia

ISBN: (纸本)9798350321982

Driver drowsiness is among the most critical causes of vehicular incidents. Consequently, a solution to mitigate this problem is exponentially increasing where an intelligent vehicle system is being implemented in vehicle development. In these studies, a solution is developed by implementing Convolutional Neural Network (CNN), which is tasked to identify the driver's drowsiness behaviour. For the datasets that are needed for training and testing of CNN models, datasets are collected at VITAL Laboratories located at UiTM Shah Alam that is recorded from 3 DVR Camera that is placed in the left (CL), middle (CM), and right (CR) positions. the models proposed in these studies are MobileNet-V2, ResNet50, and InceptionV3 architecture, where the performance of each model is evaluated by classifying the behaviour based on three different camera positions. the architecture that is being chosen is InceptionV3 from camera 5, which is placed in the middle positions that successfully achieved an overall accuracy of 85.5% in classifying drowsiness behaviour. © 2023 IEEE.

关键词： Convolutional neural networks

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Decision Transformer-Powered Traffic Control System 11

Decision Transformer-Powered Traffic Control System

引用

11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Nawar, Mahmoud Saad, Abdallah Elhalawany, Basem M. Fares, Ahmed Kuwait College of Science and Technology Doha District93004 Kuwait Benha University Faculty of Engineering at Shoubra Cairo Egypt The School of Electronics E-JUST University Communication and Computer Engineering New Borg El-Arab City Egypt

ISBN: (纸本)9798350369328

Today, traffic congestion presents a real challenge in urban areas, with consequences beyond affecting commuters by increasing travel times and causing frustrating experiences. there has been consistent research using reinforcement learning methods to approach this issue. this manuscript considers a shift in this direction and proposes a novel intelligent controller based on a GPT-based decision transformer. Using transformer models will open significant opportunities for exploiting the available pre-trained transformer models. this paper prepared an offline dataset using the DQN agent and published it online, followed by training a decision transformer. the evaluation results show that the proposed method outperforms the DQN agent used for gathering the dataset;this manifests that transformers are promising in revealing knowledge that reinforcement learning agents cannot discover during their interaction with the environment. © 2023 IEEE.

关键词： Traffic congestion

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A Wheat Grain Variety Recognition Based on Deep learning 11

A Wheat Grain Variety Recognition Based on Deep Learning

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Yushuai, Zhao Co, Celso Bation Qi, Gu Yi, Zhi Gendi, Zhou Batangas State University Batangas Philippines Yancheng Polytechnic College Yancheng China

ISBN: (纸本)9798350355314

this study introduces a deep learning-based approach to wheat grain variety recognition, a critical task for enhancing wheat quality and yield. It details the creation of a wheat kernel image dataset, capturing images from multiple angles of 22 varieties, followed by preprocessing steps to standardize and prepare the data for analysis. the study leverages convolutional neural networks (CNNs) and transfer learning to develop a model capable of identifying wheat varieties with high accuracy, addressing the limitations of traditional machine learning methods. the results demonstrate the superiority of deep learning in wheat kernel identification, with the optimized moachieving a remarkable recognition rate of 99.3%. this high performance is attributed to the ability of CNN s to automatically extract features and learn from the data, thus eliminating the need for manual feature engineering. © 2024 IEEE.

关键词： Federated learning

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Automatic Detection and Analysis of Concrete Cracks Using YOLO 4

Automatic Detection and Analysis of Concrete Cracks Using YO...

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Hota, C P Pavan Kumar Sowjanya, Vvnls Sree, G Ramya Neeharika, K. Supritha, G. Shri Vishnu Engineering College for Women Department of Artificial Intelligence India

ISBN: (纸本)9798350349900

this research introduces a novel approach for the automated detection and analysis of concrete cracks using deep learning techniques, with a focus on predicting the lifespan of structures. Concrete infrastructure is susceptible to various forms of deterioration, and cracks are early indicators of potential structural issues. Traditional methods for crack detection and analysis are often time-consuming and subjective. In this study, we utilize deep learning calculations, explicitly convolutional neural networks (CNNs), to distinguish and order breaks in substantial surfaces naturally. the proposed model is trained on a diverse dataset of concrete images, enabling it to generalize across different environmental conditions and crack patterns. Furthermore, the research extends beyond crack detection to predict the potential lifespan of concrete structures. By leveraging historical data on concrete deterioration and utilizing advanced predictive modeling. Based on detected cracks, the system seeks to offer insights into the anticipated durability of structures. this predictive capability serves as a proactive tool for maintenance planning and resource allocation, contributing to the long-term sustainability of civil infrastructure. Our experiments' outcomes show how well the deep learning model works to identify concrete cracks and forecast possible structural degradation. © 2024 IEEE.

关键词： Convolutional neural networks

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Routing and Charge Planning Strategies for Ridesharing EV Fleets 11

Routing and Charge Planning Strategies for Ridesharing EV Fl...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Singh, Ashutosh Gupta, Arobinda Dept. of Computer Science & Engineering Indian Institute of Technology KharagpurWB-721302 India

ISBN: (纸本)9789897587450

Ridesharing systems have become an important part of urban transportation. At the same time, electric vehicle (EV) adoption is also growing at a fast pace as an eco-friendly and sustainable transportation option. To operate a ridesharing system with EV fleets, scheduling an EV fleet to serve passenger requests requires consideration of both the requests, and the available charge and potential future charge requirements of the EVs. In this paper, we address the problem of scheduling EVs by a ridesharing operator, and propose four algorithms that schedule passenger requests while taking into consideration charging requirements of the EVs. Detailed simulation results are presented on a real world data set to show that the algorithms perform well. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Urban transportation

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Text-to-image Retrieval Based on Zero-shot Transfer learning with CLIP Model and Vector database 11

Text-to-image Retrieval Based on Zero-shot Transfer Learning...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Xie, Junfeng Chen, Junying School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331531904

With the continuous increase in the number of internet users, the explosion of multimodal data on the web has led to a growing demand for image retrieval. Current text-to-image retrieval systems often rely on keyword matching, which frequently fails to accurately capture users' retrieval needs. this work proposes a text-to-image retrieval method based on zero-shot transfer learning with the CLIP model and vector database, designed to be applied in text-to-image retrieval systems. By leveraging the semantic alignment capability of the CLIP model for text and images, this study effectively addresses the semantic gap problem in text-to-image retrieval. Additionally, the method utilizes the Milvus database for efficient storage and retrieval of image feature vectors, thereby enhancing system stability. Experimental results on the Flickr30k dataset demonstrate that this method improves the accuracy of text-to-image retrieval, verifying its effectiveness. Furthermore, this work extends the proposed method by incorporating the iFLYTEK Spark Cognitive Model to support image retrieval using Chinese text descriptions, further broadening the system's applicability and reliability. © 2024 IEEE.

关键词： Zero-shot learning

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