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Enhanced security Measures Against Phishing threats 5

Enhanced Security Measures Against Phishing Threats

引用

5th International conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Tamilkodi, R. Harika, A. Harika, Mogalapalli Abhilash, P. Venkata Sai Sai, Chenagana Harish Pranav, P.S. A.P. Rajahmundry India A.P. Rajahmundry India

ISBN: (纸本)9798350389609

the rapid advancement of digital transformation has significantly heightened cybersecurity threats on a global scale. As digitization progresses, it creates more opportunities for cybercriminals. One prevalent form of cyber threat is phishing, which aims to steal confidential user credentials. Cybercriminals use phishing tactics to gain access to an organization's digital assets and networks. through these security breaches, they can execute ransomware attacks, gain unauthorized access, shut down systems, and demand ransoms for access restoration. Despite the existence of anti-phishing software and techniques, phishers continually find ways to bypass these defenses. threat intelligence and behavioural analytics systems help organizations identify unusual traffic patterns, yet the most effective practice remains a comprehensive defense strategy. In this article, we introduce an approach for detecting phishing attacks through the application of machine learning (ML) methods, particularly focusing on Random Forest (RF) and Decision Tree (DT) algorithms. We utilized a widely recognized dataset of phishing attacks available on Kaggle to facilitate our ML analysis. Feature selection was performed using Principal Component Analysis (PCA) to enhance the performance of the model. the Random Forest algorithm produced results with a 92% accuracy rate. the system effectively classifies websites links, providing clear outputs indicating whether a site is classified as phishing or not. © 2024 IEEE.

关键词： Phishing

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Implementation of Power Meter System security in Smart Grids with QRNG Method 5

Implementation of Power Meter System Security in Smart Grids...

引用

5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Patel, Aum Shrivastava, Simran Sujatha, G. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350383522

Smart grids face emerging threats as connectivity expands attack surfaces. To strengthen smart meter data encryption through optimized integration of a cloud-based quantum random number generator (QRNG) service. QRNGs leverage intrinsic quantum properties to produce true randomness for cryptographic keys. An architecture is presented utilizing a QRNG service on the IBM quantum computing platform. Quantum entropy is remotely sampled and delivered on-demand via APIs to generate quantum random AES encryption keys for smart meter communications. Homomorphic encryption secures credential transmission. Compared to mathematical pseudorandom number generators, the quantum origin provides stronger security against brute force attacks and future quantum computing decryption. Tailoring QRNG generation and integration focuses on the performance, cost, and deployment constraints of grid-wide encryption hardening. the optimized approach aims to maximize randomness while minimizing complexity. By hardening cryptography via readily available quantum key generation, the QRNG integration delivers indispensable security enhancement. © 2024 IEEE.

关键词： Cryptography cryptography protocols cybersecurity encryption privacy QKD QRNG quantum entropy quantum key distribution quantum random number generator quantum uncertainty security smart grid smart meter true randomness

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From Attacks to Insights: XAI and Defense in Network security 5

From Attacks to Insights: XAI and Defense in Network Securit...

引用

5th International conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Kumar, Bharathi Ganesh Shanthini, E. PSG College of Technology Department of Computer Science and Engineering Tamil Nadu India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331530013

Network intrusion poses significant threats to cyber security, as malicious attacks can disrupt systems and compromise sensitive information. Real-world scenarios include unauthorized access to corporate networks, data breaches exposing sensitive customer information, and Distributed Denial of Service (DDoS) attacks that can impair critical infrastructure. this paper addresses these challenges using the UNSW-NB15 dataset, a comprehensive collection of real-world network traffic data. this dataset was chosen for its diversity in capturing contemporary cyber threats, making it ideal for evaluating model performance in detecting intrusions. While numerous classification models exist for this task, the Multi-Layer Perceptron (MLP) classifier is selected due to its ability to capture complex patterns and provide higher accuracy in non-linear datasets. To enhance the interpretability of the model, Explainable AI (XAI) techniques are employed, including SHAP, LIME, and permutation feature importance, which offer insights into model decisions by explaining feature contributions. However, adversarial machine learning introduces an additional layer of threat by exploiting model vulnerabilities. this paper focuses on the Zeroth Order Optimization (ZOO) attack, a black-box method that only requires access to the model's predictions, without needing knowledge of the architecture or gradients, to demonstrate how adversarial examples can drastically reduce model accuracy and compromise network security. Upon generating adversarial examples, a significant drop in accuracy is observed, identifying the features most susceptible to manipulation. then XAI methods are applied again to analyze the features that contribute most to the misclassification, allowing us to understand the underlying vulnerabilities. Based on these insights, adversarial training is implemented as a defense mechanism, successfully restoring model accuracy and strengthening resilience against attacks. the results

关键词： Network intrusion

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Secure Multi-Party Computation for Digital Assets Custody Purpose-Analysis of Open-Source Implementations 5

Secure Multi-Party Computation for Digital Assets Custody Pu...

引用

5th International conference on Blockchain Computing and applications, BCCA 2023

作者： Roszak, Daniel Jaroucheh, Zakwan School of Computing Engineering & the Built Environment Edinburgh Napier University United Kingdom

ISBN: (纸本)9798350339239

the increasing incidents of digital asset theft have highlighted vulnerabilities in their secure management and storage. In this context, Multi-party computation (MPC) emerges as a game-changing solution, offering robust cryptographic protocols that allow multiple parties to compute over their inputs while preserving data privacy. this study examines contemporary open-source multi-party computation (MPC) algorithms and protocols, with a focus on their applications for the custody of digital assets. the paper provides a thorough chronology of the evolution and development of various MPC methods. Following a detailed examination of current open-source implementations, the most promising solutions are put to the test experimentally against a wide range of benchmarks. the best MPC-based approach for digital asset management is determined using stringent evaluation criteria that take performance, security, and usability into account. the presentation of a real-world application of an MPC-based system, demonstrating its significant impact on digital asset management and storage, is an important contribution of this study. this exhaustive exploration provides invaluable insights for developers, researchers, and organizations keen to leverage MPC's benefits. the paper concludes by outlining prospective directions for MPC research in the future, emphasizing the challenges and immense opportunities in its practical applications, underscoring its potential to usher in a safer digital future. © 2023 IEEE.

关键词： data privacy

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security identification of abnormal access to network databases based on GSA-SVM algorithm and deep features 5

Security identification of abnormal access to network databa...

引用

5th International conference on Computer Communication and Network security, CCNS 2024

作者： Jiang, Yuchen Jia, Zhen Liu, Wenting Yang, Luyu Liu, Haitao State Grid Information & Telecommunication Branch Beijing100761 China

ISBN: (纸本)9781510681842

In response to the problem of low success rate in identifying abnormal access security in network databases, a network database abnormal access security identification method based on GSA-SVM algorithm and deep features is proposed. By combining deep feature extraction technology and learning high-level abstract features from raw data, the accuracy and efficiency of anomaly access detection are effectively improved by utilizing the global optimization ability of GSA algorithm and the strong classification performance of SVM algorithm. the experimental results show that this method has high accuracy and stability in identifying abnormal access to network databases, providing an effective technical means for database security. this method has broad application prospects in the field of abnormal access detection in network databases, and can provide strong support for the security protection of network databases. © 2024 SPIE.

关键词： Simulated annealing

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Research progress and prospects of water quality models in water quality prediction of Danjiangkou Reservoir 5

Research progress and prospects of water quality models in w...

引用

5th International conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Jiang, Haochen Zhang, Jing Beijing Laboratory of Water Resources Security Capital Normal University Beijing100048 China The Key Lab of Resource Environment and GIS of Beijing Capital Normal University Beijing100048 China

ISBN: (纸本)9781510683006

this article predominantly studies the application of water quality models in Danjiangkou Reservoir, and explores the principles and research progress of water quality models. Subsequently, the application of water quality models in Danjiangkou Reservoir was introduced. this article analyzes the water quality data of Danjiangkou in 2012 and looks forward to the next step of using the Environmental Fluid Dynamics Code (EFDC) model to study the Danjiangkou Reservoir. this study has important theoretical and practical significance for a deeper understanding of the principles and research progress of water quality models, as well as their application in the Danjiangkou Reservoir. © 2024 SPIE.

关键词： Water quality Computer simulations Motion models Mathematical modeling 3D modeling data modeling Analytical research Climate change Geographic information systems Safety

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A Study on Large Scale applications of Big data in Modern Era 5

A Study on Large Scale Applications of Big Data in Modern Er...

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Kapadiya, Dharmi Shekhawat, Chezal Sharma, Priyanka Swami Keshvanand Institute of Technology Management & Gramothan Rajasthan Jaipur302017 India

ISBN: (纸本)9798400709418

Big data has brought a profound revolution across industries, redefining data management and decision-making paradigms. the utilization of vast and diverse datasets enables institutions to make more informed risk assessments, identify fraudulent activities with greater accuracy and offer personalized services tailored to individual needs. A lot of transformation has been seen various areas as big data analytics is empowering various industries to deliver personalized medicine, predict disease outbreaks, and optimized treatment plans based on comprehensive data analysis. Additionally, the marketing domain has been reshaped by big data, allowing businesses to gain valuable insights into customer preferences and behaviors, enabling targeted marketing campaigns and ultimately enhancing customer satisfaction. the influence of big data also extends to the transportation sector, where it plays a pivotal role in real-time traffic management, route optimization, and predictive maintenance. this data-driven approach results in reduced congestion, improved transportation efficiency, and enhanced overall travel experiences. While the potential of big data applications is immense, the paper also acknowledges the challenges posed by handling massive datasets, emphasizing the importance of scalable infrastructure, robust data privacy measures, and stringent security protocols. Here, broad applications of big data are demonstrated that are shaping a future where data-driven decision-making and innovation drive progress across various sectors. © 2023 acm.

关键词： Quality control

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Cryptographic algorithm development and application for encryption and decryption 5

Cryptographic algorithm development and application for encr...

引用

5th International conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Pitale, Rahul Tajane, Kapil Mahajan, Prathamesh Nehate, Nishita Mulimani, Apeksha Lokhande, Devarsh Pimpri Chinchwad College of Engineering Akurdi Railway Station Road Sector No.26 Maharastra Nigdi411044 India

ISBN: (纸本)9798400709418

Cryptographic algorithms are used to maintain the data confidentiality and integrity. In the era of ubiquitous digital communication and information exchange, the security of sensitive data has become a paramount concern. Cryptographic algorithms play a vital role in ensuring the confidentiality and integrity of data by facilitating encryption and decryption processes. this paper presents an exploration into the development and application of cryptographic algorithms for robust encryption and decryption purposes. the first section of the paper delves into the foundational principles of cryptography, elucidating various encryption techniques and their historical evolution. It highlights the significance of algorithmic complexity and key management in achieving secure communication. In this paper we discuss the eight known cryptographic algorithms namely, DES, 3-DES, RSA, AES, IDEA, Blowfish algorithm, Homomorphic encryption. this document summarizes the complex interaction between the creation of cryptographic algorithms and their use in encryption and decryption. It highlights the constantly evolving methods used to thwart new attacks and the dynamic nature of cryptographic research. Stakeholders can choose and use cryptographic algorithms to protect sensitive data in a digital environment that is becoming more interconnected by understanding the interaction between theoretical principles and actual execution. © 2023 acm.

关键词： Sensitive data

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Social security Event Prediction Based on Machine Learning 5

Social Security Event Prediction Based on Machine Learning

引用

5th International conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Li, Chang Jin, Tianye School of International Relations Guangdong University of Foreign Studies Guangzhou China School of Humanities Law Northeastern University Boston United States

ISBN: (纸本)9798331539818

In the construction and development of modern society, prediction, as an effective technology to evaluate the development trend and future state of things, can scientifically deal with the problems existing in traditional technical methods, and provide the basic guarantee for social economy and people's security. At present, domestic and foreign scholars have put forward a number of forecasting methods in the comprehensive study, which are mainly divided into quantitative forecasting and qualitative forecasting. Although China has begun to pay attention to social risk forecasting in the past construction, the technical means mastered in practice cannot reach the level of developed countries under the influence of factors such as theoretical knowledge and technical level. therefore, after understanding the current development status of social security event prediction in China, this paper mainly explores the application effect of credit risk prediction according to the machine learning model structure and social risk prediction method. the final experimental results prove that the construction of social risk prediction model based on machine learning algorithm can not only grasp more valuable data information, but also effectively prevent various events that endanger social security. © 2024 IEEE.

关键词： Prediction models

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application of LSTM for Differentiating Real and Fake Bank Names from Provided Uniform Resource Locator 5

Application of LSTM for Differentiating Real and Fake Bank N...

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5th International conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dashrath, Prajakta Giri, Manish India

ISBN: (纸本)9798331518097

the most crucial element of our everyday activities is security. As we progress in life, we must constantly keep our financial situation in mind. A phony online or offline transfer could result in an inevitable loss in our existence. To prevent this, we must confirm the correct bank name from uniform resource locator (URL) prior to making any changes. Every action taken with the bank's name assistance depends on its proper URL. It is necessary to identify the correct URL before any transaction or access to bank website. there are various methods which can be applied to identify correct bank URL, Long Short-Term Memory (LSTM), a supervised deep learning technique can be efficient method to identify bank URL. the main component of the LSTM algorithm is memory, which allows users to commit genuine bank names to memory. this helps users to confirm the authenticity of the bank name before making any website utilization. In the implemented system, URL identification is done through feature extraction, data preprocessing on collected data set for training and prediction purpose. Overall results shows that LSTM can provide approximately 94 % accuracy to detect the fake bank name from URL. © 2024 IEEE.

关键词： data Preprocessing Deep Learning fake bank name detection feature extraction LSTM online fraud

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