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Cross-Layer Distributed Attack Detection Model for the IoT

International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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FoodSavior: Distributing Surplus Food to NGOs or Manure Producers Using Internet of Things and Machine Learning 5

FoodSavior: Distributing Surplus Food to NGOs or Manure Prod...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Uke, Shailaja Jadhav, Prithviraj Kakarwal, Urmila Kolawale, Sanika Nikam, Vivek Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331530013

By evaluating the quality of leftover food and enabling its best possible redistribution, this research presents a novel approach to addressing food waste in the restaurant sector. The system uses a Flutter an Dart-based application to control the food distribution process and hardware sensors like MQ4 to identify gasses that indicate spoiling. While spoiled food is sent to manure producers for use in agriculture, non-spoiled food is sent to NGOs to feed people in need. In order to minimize waste, the system analyzes sensor data and uses machine learning algorithms, such as Decision Trees, to forecast spoiling patterns and suggest preparation techniques. Restaurants can quickly enter food data into the app, which NGOs and manure producers may use to collaborate in real time. This data is processed by the Django-developed backend server, and Arduino C++ is used to control hardware activities. This solution promotes sustainability, social responsibility, and community welfare by facilitating a smooth collaboration between farmers, restaurants, and non-governmental organizations. The method helps create a more sustainable and just food environment by decreasing food waste and improving resource efficiency. This version emphasizes the objectives, innovation, and societal impact of the work while maintaining technical details. © 2024 IEEE.

关键词： C++ (programming language)

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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Automated Building Damage Detection Post Hurricane: Enhancing Accuracy using Hybrid Ensemble Model 5

Automated Building Damage Detection Post Hurricane: Enhancin...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Kulkarni, Radhika V. Mude, Vedant Nagrale, Rutuj Nirgude, Aarya Nirmal, Tejashri Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350389609

One of the most destructive natural disasters, hurricanes destroy a great deal of property, harm people, and disrupt the ecosystem and world economy. Earlier, through ground surveys, the impact of the hurricane was measured, by locating the damaged and undamaged infrastructure in the affected area, which is a very time-consuming and labor-intensive method. Damage detection is carried out for rapid response and recovery. A fast and enhanced method is proposed in this study that uses a hybrid ensemble model on the post-hurricane satellite images of the 2017 Hurricane Harvey in Greater Houston. The structured approach that includes data handling, model development, and image preparation is introduced. Leveraging a hybrid ensemble model combining EfficientNetB0, VGG19, and InceptionResNetV2, the proposed model is built, trained, fine-tuned, and tested, achieving improved accuracy of 93%, 90% precision, 99% recall, 85% AUC, and 95% F1-score in automated damage detection. This framework not only accelerates disaster recovery but also offers valuable insights for future resilience planning. © 2024 IEEE.

关键词： Convolutional neural networks

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Ethereum-Based Decentralized Crowdfunding Platform 6th

Ethereum-Based Decentralized Crowdfunding Platform

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Jadhav, Swati Patil, Rohit Patil, Saee Patil, Shweta Patil, Varun Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819940707

Crowdfunding refers to investments of people in some projects and businesses. Sometimes, it also refers to donations from people for societal goodness. Many online platforms are available that support crowdfunding for different purposes. But most of the time investors or donors have a kind of doubt about whether their money is being utilized for the right purpose or not. The platforms are centralized;once the organization receives the donation, donors have no idea about the further transactions which the organization does. Many times, crowdfunding campaigns are not monitored and some of them have proven to be bogus. Due to such issues, projects have been extensively delayed in their completion. So, crowdfunding platforms are one area in which blockchain technologies can be used. Blockchain has been used in many different sectors to increase transparency among the users and people involved in the system. Blockchain is predicted to be used by the majority of technology as an effective method of conducting transactions online in the future. By integrating Ethereum-based contracts into the crowdfunding campaign, this project aims to overcome these problems, allowing the contracts to be fully automated, preventing fraud, and ensuring that projects are completed within the specified timeframe. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Smart contract

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Using Website Content for Detecting Phishing URLs: A Novel Approach 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ganganwar, Vaishali Hussain, Syed Iarshed Powar, Ankur Gaur, Shushant Kumar, Amit Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Phishing attacks have become a rampant cybercrime, where hackers use deceptive methods to trick individuals into sharing their personal information. However, with the advent of AI models like ChatGPT, detecting phishing URLs accurately has become increasingly challenging for traditional machine learning techniques due to the creation of fake content by hackers. To address this issue, we propose a new feature called "human-content" that helps in differentiating legitimate and phishing websites based on human-generated content on a website. To classify phishing URLs, we employed various features, such as domain-based, JavaScript-based, and lexical-based features, along with the novel "human-content" feature. To perform the classification, we used five machine learning classifiers, including Gradient Boosting Classifier, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression. Our experimental results on a dataset consisting of both legitimate URLs obtained from Alexa and phishing URLs from PhishTank demonstrate that the Gradient Boosting Classifier has the best performance, achieving an accuracy of 95.8%. Our proposed approach provides promising results in detecting phishing URLs and is more robust to AI-generated fake content. As phishing attacks continue to become more sophisticated, our method can help enhance online user security by effectively detecting phishing URLs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Websites

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Traceability of Counterfeit Product Detection in Supply Chain through Blockchain 15

Traceability of Counterfeit Product Detection in Supply Chai...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Asthagi, Rashmi Kedar, Shardul Khatal, Shruti Khardekar, Sujal Khaple, Siddesh Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331300579

In recent years, industries involved in manufacturing products have experienced an increasing reliance on counterfeit items. This phenomenon significantly impacts businesses' sales and profits. To address this challenge and ensure the authentication of genuine goods across the supply chain, a functional blockchain system is essential in preventing fraud and counterfeiting of goods. Blockchain technology allows customers to verify the product's origin without depending on trusted third parties for secure understanding of the product's authenticity. An application utilizing blockchain technology inherently ensures "tamper-resistant" data content. A blockchain serves as a decentralized and distributed digital ledger that stores transactional documents, known as public blocks, across numerous networks' databases. This study proposes the use of a barcode scanner to identify counterfeit goods, connecting each barcode to a Blockchain-Based Management (BCBM) system. The suggested technique involves recording product information and its unique code as database blocks within the blockchain. It begins by acquiring the customer's specific code and then cross-references it with the records in the blockchain database. If the code matches, the buyer receives confirmation;if not, the purchaser is prompted to provide the product's purchase location. This step aids in determining the source of any counterfeit items. © Grenze Scientific Society, 2024.

关键词： Blockchain

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Improved Energy Efficient Divergent Path Routing for Energy-Harvesting Wireless Sensor Networks 9

Improved Energy Efficient Divergent Path Routing for Energy-...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sivakumar, S. Sabaresan, V. Prakash, S. Om Dharmaraj, T.B. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering India St.Joseph's Institute of Technology Department of Computer Science and Engineering India Ppg Institute of Technology Department of Mechanical Engineering India Ppg Institute of Technology Department of Information Technology India

ISBN: (纸本)9798350377972

Since packets are dropped when there is an insufficient connection between each of the sensor nodes in the network, a sensor node in a sensor network is unable to efficiently aggregate the data packets. Since each area has a different sensor node location, the sender node rarely shares information with its neighbour. Only a certain number of packets are received for each communication, the rest are dropped, and the communication is lost. In order to determine each node's minimum residual energy level, the node transmits packets on the path once more. It raises the energy consumption and packet loss rate. Energy-efficient packet aggregation along the network environment is the goal of the suggested Improved Energy Efficient Divergent Path Routing (IEEDP) technique. The node's position is clearly tracked before a different route is assigned for routing. Using metrics such as end-to-end delay, overhead for communication, packet delivery ratio, link stability, consumption of energy, packet loss, and network lifetime using a simulation model, the suggested improved energy efficient divergent path routing (IEEDP) approach outperformed existing methods like Wireless Access Range (WAR) and Congestion-Aware Clustering And Routing (CCR). Energy preservation is a process to extend the network's lifetime by examining the residual Energy Effects on EH-WSN. The Combining Packet Aggregation algorithm is made for sensor networks and collects data packets without any loss during the entire transmission period from the origin node to a target node in a network environment. It lowers energy consumption and packet loss rate. © 2024 IEEE.

关键词： Packet loss

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Bettercall: AI based legal assistant 5

Bettercall: AI based legal assistant

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Dhore, Manikrao Vimal, Aryan Agrawal, Avish Bajaj, Raghav Barde, Rohan Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350367171

This research introduces an AI-based chat-bot designed to enhance access to legal and judicial information in India. The system leverages advanced natural language processing techniques to transform legal texts into vector embeddings, facilitating semantic search capabilities that improve the user experience in retrieving relevant legal information. The chat-bot aims to provide primary legal aid, support informed decision-making, offer instant access to legal rights and information, and promote legal awareness among users. The methodology includes rigorous data collection, cleaning, and pre-processing processes to ensure high accuracy and reliability. The research also navigates to a potential implementation of the solution and addresses the complexities, results and challenges faced by the authors. The research highlights the novelty of the approach and addresses existing gaps in scalability, multilingual support, and domain coverage, presenting a comprehensive system ready for real-world deployment. © 2024 IEEE.

关键词： Embeddings

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AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance 5th

AI-Driven Glaucoma Susceptibility Assessment and Lifestyle G...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Talla, Ganesh Venkata Sundar Akhil, Toram Rajeev Thatikonda, Surendra potnuru, Tarun Lalitha, Chirravuri Surya Naga Sai Bonthu, Sridevi Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770746

This project addresses the global health challenge posed by glaucoma through the development of an innovative Python-based application. Utilizing advanced machine learning techniques, specifically the VGG16 model with transfer learning, the application aims to enhance glaucoma diagnosis and management. By automating glaucoma severity classification into distinct categories, including No_DR, Proliferate_DR, Mild, Moderate, and Severe, the application provides personalized medical advice tailored to the predicted severity. The user-friendly interface, powered by the Tkinter library, enables individuals to upload eye images for real-time analysis, empowering them to actively monitor their eye health. Through early detection and informed decision-making, the application strives to slow down the progression of glaucoma and enhance overall quality of life. This project exemplifies the intersection of technology and healthcare, promising to positively impact lives by promoting early awareness and proactive glaucoma management. © ICST institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Decision making

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