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Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

computers, Materials & Continua 2023年第12期77卷 3637-3657页

作者： Derwin Suhartono Alif Tri Handoyo Franz Adeta Junior Computer Science Department School of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia Cyber Security Program Computer Science DepartmentSchool of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online *** study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and *** research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training *** inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original ***,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text *** exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original *** methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation ***,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using *** increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in *** proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

关键词： Data augmentation Generative Adversarial Network(GAN) Reverse GAN(RGAN) sarcasm detection

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Machine Learning for Threat Detection

Machine Learning for Threat Detection

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Sutanto, Dustin Fransisca, Febri Liem, Steven Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila School of Computer Science Cyber Security Program Computer Science Department Bina Nusantara Jakarta Indonesia School of Computer Science Computer Science Program Computer Science Department Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

These days almost all people in the world use the Internet as the internet is constantly evolving. cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since the 1980s, Intrusion Detection Systems (IDS) have been developed to defend computer networks against malicious activity and unauthorized access. Companies choose to implement IDS the part of their cyber security strategy to protect their data and network from potential threats. in this case, IDS has played a crucial role in detecting abnormal traffic and protecting digital assets. furthermore, a high-accuracy IDS is needed in concern of having false positive feedback from the IDS machine itself that can cause various problems. to accompany the need for high accuracy and trainable IDS machines, we experimented with a hybrid system using machine learning that is combined with traditional IDS machines resulting in higher accuracy and lower error rate. By adjusting the parameter the accuracy improved and could achieve an accuracy of 99.66% of classifying the traffic. © 2024 IEEE.

关键词： Network intrusion

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security Challenges and Issues in Cloud Computing

Security Challenges and Issues in Cloud Computing

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Zakiyyah, Rika Permana, Daniel Valenecio, Devan Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia Computer Science Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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An Efficient Privacy-Enhanced Federated Learning with Single-Key Homomorphic Encryption

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IEEE Internet of Things Journal 2025年第13期12卷 23598-23613页

作者： Liu, Yilong Zhang, Hong Wang, Miao Xie, Qiqi Wang, Liqiang Hebei University School of Cyber Security and Computer Baoding071032 China University of Central Florida Department of Computer Science OrlandoFL32816 United States

As the proliferation of Internet of Things (IoT) devices continues, vast amounts of data are being collected on various end devices. However, uploading these data to the cloud for centralized processing poses significant privacy risks. Federated learning (FL) addresses this issue by sharing model updates instead of raw data, which helps mitigate privacy concerns. Nonetheless, model updates transmitted in plaintext remain vulnerable to inference and reconstruction attacks. While homomorphic encryption (HE) can enhance security, traditional single-key schemes struggle to defend against collusion. Multi-key HE schemes introduce substantial computational and communication overhead, making them impractical for resource-constrained IoT environments. In this paper, we propose a novel FL framework that integrates single-key HE, elliptic curve cryptography (ECC), and trusted execution environments (TEE) to achieve robust protection against collusion attacks. Specifically, we utilize ECC-based public key encryption to secure HE private key and employ secret sharing to split the ECC private key into multiple sub-secrets, which are distributed to edge nodes, ensuring no single party can independently decrypt model updates. Additionally, we delegate the HE private key reconstruction and global model decryption processes to the TEE, and introduce a hash verification mechanism to ensure that only aggregated global model updates can be decrypted. Finally, we provide comprehensive security proofs and extensive experimental results, demonstrating the effectiveness and superiority of the proposed framework. © 2014 IEEE.

关键词： Federated learning

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security Flaw in TCP/IP and Proposed Measures 1

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International Conference on Recent Developments in cyber security, ReDCySec 2023

作者： Upadhyay, Sourav Kumar Kumar, Prakash Department of Computer Science and Engineering BIT Sindri Dhanbad India Department of Computer Science and Cyber Security JRSU Ranchi India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

The whole Internet is based on TCP/IP protocol suite. The inherent flaw in the TCP/IP makes a cascading effect for other attacks like flooding, fingerprinting, and many more. All the switching and routing mechanisms are based on the TCP/IP, so it is safe to say that TCP/IP is the backbone protocol suite for communication for all major Internet applications. The architectural flaw of TCP/IP protocol and possible ways to mitigate those flaws are discussed in this paper. Especially, the problem with connection phase in TCP and how SYN/ACK/RST packets can be used to breach an ongoing handshake are brought in light. In order to help create a safe information network environment in the future, this study aims to provide some references. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Transmission control protocol

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Electronic Medical Records Management and Administration: Current Trends, Issues, Solutions, and Future Directions

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SN computer science 2024年第5期5卷 460页

作者： Abdulkadir, Umar Waziri, Victor Onomza Alhassan, John Kolo Ismaila, Idris Department of Cyber Security Science Federal University of Technology Minna Nigeria Department of Computer Science Federal University of Technology Minna Nigeria

Electronic Medical Records (EMR) is often used to refer to as electronic personal health (EPH) records or electronic healthcare records (EHR). These are considered vivacious assets of health facilities and patients. The relevance of the EMRs has motivated diverse innovations in the collecting, organizing, managing and administering for purpose of treatment primarily and other reasons. There are various concerns raised about legitimate usages, reproducibility, accuracy, and privacy breaches of EMRs across majority of health and medical facilities globally. This phenomenon became popular due to its support of electronic devices, wireless links, transmission and storage of data in the cloud, and communication across a gateway (or central-point). Therefore, physicians and medical facilities were empowered to undertake virtual consultations to patients through telemedicine applications for the remote treatment and diagnosis. Internet of Things (IoT) systems, medical wearable objects, and sensors were the basic components that collect and transmit patient data on real-time basis to a base station or centralized servers managed by hospitals. However, there is the need to improve creation and adoption of EMRs not without understanding their roles and shortfalls as well possible means of improvement. Consequently, this study conducts a systematic literature review on electronic medical records management and administration under current trends, issues, solutions and future directions. To this end, eighty-one (81) peer-reviewed articles including conferences and journals papers were included in the final stage of the study after applying Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRIMA-P) methodology of records selection. The contributions of this study include: the recognition of the informed consent strategy as the best solution to illegitimate access to patient electronic records;the use of permissioned access strategies provided by blockc

关键词： Access permission scheme Data administration Medical records Privacy security

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Genetic Algorithm-optimized k-nearest Neighbors and Support Vector Machines for Breast Cancer Detection in Resource-constrained Environments

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 848-861页

作者： Alemu, Abebe Girma, Anteneh Abebe, Mesfin Srinivasagan, Ramasamy Adama Science and Technology University Adama Ethiopia Computer Science/Cyber-Security department University of the District of Columbia WashingtonDC United States Computer Science and Engineering department Adama Science and Technology University Adama Ethiopia Computer Engineering department CCSIT King Faisal University Al Hufuf Saudi Arabia

Breast cancer poses a significant global threat, highlighting the urgent need for early detection to reduce mortality rates. Researchers are working to minimize the occurrence of false positives and false negatives, thereby improving the efficiency of breast cancer detection models. To achieve this, they employ advanced techniques such as artificial intelligence, machine learning, deep learning, and computational intelligence. Support vector machines (SVM) and k-nearest neighbors (KNN) are two popular lightweight machine-learning techniques.;however, their effectiveness depends on proper feature selection and parameter tuning. Genetic algorithm optimization provides a solution by intelligently selecting relevant features and fine-tuning parameters, which enhances classification accuracy for early diagnosis. This study demonstrates the effectiveness of a hybrid computational intelligence model that utilizes genetic algorithms for feature selection. The proposed GAKNN-SVM model shows superior performance in detecting breast tumors, utilizing the Wisconsin Breast Cancer Diagnostic Dataset. The results indicate significant improvements, with accuracy, sensitivity, and specificity rates reaching 98.25%, 98.15%, and 98.41%, respectively, based on 171 test samples. Overall, genetic algorithms and machine learning approaches hold great promise for improving breast cancer detection accuracy, ultimately leading to better diagnostic outcomes and reduced mortality rates, especially in resource-constrained environments. © (2025), (International Association of Engineers). All rights reserved.

关键词： Support vector machines

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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A hybrid encryption for secure data deduplication the cloud

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International Journal of Cloud Computing 2023年第2-4期12卷 295-307页

作者： Ebinazer, Silambarasan Elkana Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Science and Engineering Cyber Security IIIT Kottayam India Department of Computer Applications National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

Cloud computing (CC) is a cost-effective platform for users to store their data on the internet rather than investing in additional devices for storage. Data deduplication (DD) defines a process of eliminating redundant data in the cloud by leaving a single copy. This paper provides a hybrid encryption scheme using genetic algorithm (GA) and attribute-based encryption (ABE) to ensure secure data transmission in the cloud and preventing the data deduplication by using message locked encryption (MLE). The hybrid encryption scheme provides a secure data transmission in the cloud. From the simulation output, the time requirement is reduced up to 94% and consumes 43% bandwidth compared with existing works. The proposed method tends to be quite effective in reducing storage and bandwidth costs. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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