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Cryptography, Informatics, and cybersecurity (ICoCICs), IEEE International Conference on

作者： Yudhistira Dimas Wahyu Utomo Charles Lim Cyber Security Engineering Cyber and Crypto Polytechnic Bogor Indonesia Information Technology Department Swiss German University Tangerang Banten Indonesia

Malware, a shortened term for malicious software, refers to software or code designed to harm targets using malicious activities. Effective classification of malware is essential for developing robust detection and prevention mechanisms. This study presents a systematic review of the literature on image-based malware classification. The research methodology employed the DICARe technique. The results and discussion section revealed that 31 papers met the criteria and were highly quality. The study also examined the distribution of research on image-based malware classification by country of origin, with contributions from 15 different countries. Various classification techniques were identified in the literature, indicating the continuous development and evolution of solutions in this area. The findings suggest increased research output due to the growing importance of cybersecurity and increased funding for research in this field. This study provides valuable insights into the current state of image-based malware classification research and highlights the need for further advancements in this area.

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Actuation Sequence Tampering Attack Detection on Field-Level Micro-Electrode Dot Arrays Based on 2-Layer Authentication 23

Actuation Sequence Tampering Attack Detection on Field-Level...

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Proceedings of the 2023 13th International Conference on Communication and Network security

作者： Xiaodong Guo Chenxi Lyu Zhenyi Chen Xiaoding Wang Ruishen Bao Chen Dong College of Computer and Big Data Fuzhou University China Department of Computer Science and Engineering University of South Florida USA College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

As a next-generation digital microfluidic biochip (DMFB) platform, micro-electrode-dot -array (MEDA) play an important role in the biochemical industry with more advanced fluidic manipulation and higher reliability. As the MEDA biochip market has grown by leaps and bounds, there are still some issues with MEDA that have not kept pace in terms of security. One of them is that an attacker of a MEDA biochip can change the protocol parameters by tampering with the actuation sequence (AS) in the field before performing the bioassay, which can lead to catastrophic consequences. The attackers could be untrustworthy third parties introduced by the distributed supply chain and malicious remote parties at the time of cyber-physical MEDA biochip deployment. In this paper, a two-layer authentication solution for AS tampering attack detection is proposed, based on SHA-256 hash function to ensure the security of the authentication process and at the same time, it is able to detect and locate the tampered AS with simplicity, security and efficiency. Experiments demonstrate the obvious advantages in security, performance and applicability within a controllable time complexity.

关键词： actuation sequence micro-electrode-dot-array security tempering attack

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Enhancing Side-Channel Attacks Prediction using Convolutional Neural Networks

Enhancing Side-Channel Attacks Prediction using Convolutiona...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Khalid Alemerien Sadeq Al-Suhemat Fadi Alsuhimat Enshirah Altarawneh Information Technology Department Tafila Technical University Tafila Jordan Faculty of Information Technology Isra University Amman Jordan Cyber Security Department Philadelphia University Amman Jordan Computer Engineering Department Hashemite University Zarqa Jordan

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

A common type of cyberattack is the side channel attack (SCA), which affects many devices and equipment connected to a network. These attacks have different types, like power attacks such as DPA, electromagnetic attacks, storage attacks, and others. Researchers and information security experts are concerned about SCA targeting devices, as they can lead to the loss and theft of important information. Using deep learning (DL) techniques in SCA analysis can be an efficient tool for detecting the SCA. Many previous works have tried to carry out the SCA in order to mitigate the impact of these attacks, but they encountered difficulties in detecting the SCA, whether in selecting the suitable dataset or applying the most efficient machine learning or deep learning techniques for achieving high performance. Therefore, we developed in this paper a deep learning-based model to detect SCAs using a dataset related to power attacks (the DPAv4 dataset) and the Convolutional Neural Networks (CNN) algorithm to train and test the DPAv4 dataset. The findings of our experiments showed a significant improvement in the performance of DL-based techniques in the detection of SCA. The proposed CNN-based model achieved an accuracy of 0.814 in detecting SCA and a loss rate of 0.581.

关键词： Deep learning Machine learning algorithms Computational modeling Information security Side-channel attacks Prediction algorithms Convolutional neural networks Information technology Electromagnetics cyberattack

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Securing Healthcare Data in IoMT Network Using Enhanced Chaos Based Substitution and Diffusion

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computer Systems Science & engineering 2023年第11期47卷 2361-2380页

作者： Musheer Ahmad Reem Ibrahim Alkanhel Naglaa FSoliman Abeer D.Algarni Fathi E.Abd El-Samie Walid El-Shafai Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many *** modern-day applications,patient data usually holds clinical imagery,records,and other medical ***,the Internet of Medical Things(IoMT),equipped with cloud computing,has come out to be a beneficial paradigm in the healthcare ***,the openness of networks and systems leads to security threats and illegal ***,reliable,fast,and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT *** paper presents an image protection scheme for healthcare applications to protect patients’medical image data exchanged in IoMT *** proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security *** optimized S-box has an excellent nonlinearity score of *** new image protection scheme is efficient enough to exhibit correlation values less than 0.0022,entropy values higher than 7.999,and NPCR values around 99.6%.To reveal the efficacy of the scheme,several comparison studies are *** comparison studies reveal that the novel protection scheme is robust,efficient,and capable of securing healthcare imagery in IoMT systems.

关键词： Secure communication healthcare data encryption Internet of Medical Things(IoMT) discrete chaotic map substitution box(S-box)

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A Hybird Alignment Loss for Temporal Moment Localization with Natural Language

A Hybird Alignment Loss for Temporal Moment Localization wit...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Guo, Chao Liu, Daizong Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Electronic Information and Communication Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

This paper addresses the problem of temporal moment localization with natural language, which aims to localize a target video moment according to a language description. A key challenge in this task is how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Traditional methods generally utilize the vanilla attention mechanism to simulate soft alignment between extracted video and sentence features, however, they lack the training signal to alleviate the domain gap between video and sentence domains. In this paper, we propose a novel cross-domain alignment module to deeply align the features in two domains, yielding to learn better representations. We evaluate our model on three public datasets: Charades-STA, ActivityNet Captions and TACoS, and the experiments prove that our proposed cross-domain alignment module can bring significant improvement, and can be also applied to other baseline models. © 2022 IEEE.

关键词： Alignment

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Comparative Analysis of Machine Learning Algorithms and Datasets for Detecting cyberbullying on Social Media Platforms

Comparative Analysis of Machine Learning Algorithms and Data...

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Computing and Intelligent Reality Technologies (ICCIRT), International Conference on

作者： Anusha Palagati Santhosh Kumar Balan S Arun Joe Babulo Laxmi Raja K.K. Natarajan Rajkumar Kalimuthu Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering SMIEEE Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications K.S.R College of Arts and Science for Women Tiruchengode Tamilnadu India Department of Cyber Security Karpagam Academy of Higher Education Coimbatore Tamilnadu India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman Department of Cyber Security Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

cyberbullying is defined as sharing of undesirable material in the cyberspace, primarily- the use of the social sites that instigate hostility and create feelings of hatred among the individuals. Another increasing threat due to increase use of internet-based communication is cyberbullying especially on the site such as Twitter. Different ML and NLP approaches including support vector machine (SVM), Naϊve bayes, Random Forest, and logistic regression is employed to detect cyberbul Bullivating. These methods for supervised learning mine patterns in text and image, allowing computers to promptly flag risky interactions. Some of these approaches also utilizes OCR for the identification of image-based bullying. Consequently, the objective of this research is to identify the correct algorithm and dataset for improving the identification of cyberbullying and solving the problem that manifests itself in the steady increase in cases of this type of bullying.

关键词： Support vector machines computers Logistic regression Machine learning algorithms Supervised learning Optical character recognition cyberbullying Bayes methods Object recognition Random forests

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New Coefficient Grouping for Complex Affine Layers

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Chinese Journal of Electronics 2025年第2期34卷 520-532页

作者： Qiao Wenxiao Sun Siwei Chen Ying Hu Lei Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing 100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing 100049 China School of Cryptology University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Cryptology Beijing 100878 China Department of Cryptology Science & Technology Beijing Electronic Science & Technology Institute Beijing 100070 China

Recently, designing symmetric primitives for applications in cryptographic protocols including multi-party computation, fully homomorphic encryption, and zero-knowledge proofs has become an important research topic. Among many such new symmetric schemes, a power function over a large finite field \begin{document}$ \mathbb{F}_q $\end{document} is commonly used. In this paper, we revisit the algebraic degree’s growth for a substitution-permutation network (SPN) cipher over \begin{document}$ \mathbb{F}_{2^n} $\end{document}\begin{document}$ (n\ge3) $\end{document}, whose S-box is defined as a composition of a power function \begin{document}$ P(x)=x^{2^d+1} $\end{document} where \begin{document}$ d\ge1 $\end{document} with a polynomial \begin{document}$ A(x)=a_0+ $\end{document}\begin{document}$ \sum\limits_{w=1}^Wa_wx^{2^{\beta_w}} $\end{document} where \begin{document}$ a_i\in\mathbb{F}_{2^n} $\end{document} for \begin{document}$ 0\le i\le W $\end{document} and \begin{document}$ a_w\neq0 $\end{document} for \begin{document}$ 1\le w\le W $\end{document}. We propose a new coefficient grouping technique, which is based on our new description of the monomials that will probably appear in the state. Specifically, we propose a new measure to find proper \begin{document}$ (\beta_1,\beta_2,\dots,\beta_W) $\end{document} for the algebraic degree’s fastest growth and a new method to compute the algebraic degree’s upper bound for arbitrary \begin{document}$ A(x) $\end{document}. Especially for Chaghri, which was presented at ACM CCS 2022, we obtained a tighter upper bound on the algebraic degree.

关键词： Binary extension field Algebraic degree Coefficient grouping technique

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AntiMal: an Approach of Malware Detection Employing Swin Transformer 23

AntiMal: an Approach of Malware Detection Employing Swin Tra...

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Proceedings of the 2023 13th International Conference on Communication and Network security

作者： Chenxi Lyu Yinan Yao Xiaodong Guo Zihong Huang Chen Dong Yuanyuan Zhang Zhenyi Chen College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China Department of Computer Science and Engineering University of South Florida USA

ISBN: (纸本)9798400707964

In the present era, the menace of malicious software is growing continuously, posing a significant challenge in the realm of cybersecurity. Currently, traditional machine learning algorithms have been widely applied to the analysis of malicious software and are considered effective methods. However, these approaches often require extensive feature engineering, feature learning, and feature representation, which can be time-consuming and resource-intensive. In this paper, a method for the identification of malicious software, named "AntiMal," is proposed. This method utilizes the Swin Transformer as the backbone network and combines it with image features for static analysis. It leverages the dataset provided by Microsoft for the Kaggle Microsoft Malware Classification Challenge. Initially, it converts the binary data of malicious code into grayscale images, treating every 8 bits as a pixel. Subsequently, it employs PyTorch to build and train the Swin Transformer, classifying the images in the validation set. The results demonstrate that this approach excels in the problem of malicious software classification, achieving an impressive accuracy of up to 95%, showcasing remarkable precision and generalization capabilities. Furthermore, it exhibits significant advantages when dealing with large datasets, requiring fewer computational resources, thus offering exceptional computational efficiency.

关键词： cybersecurity Deep learning Malware Detection Swin Transformer

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Temara, Sheetal Samanthapudi, Sagar Varma Rohella, Piyush Gupta, Ketan Ashokkumar, S.R. University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Centre For Blockchain and Cyber Security Department of Computer and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure. © 2024 IEEE.

关键词： Artificial intelligence

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Ali Hamza Najim Ali Jafer Mahdi Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Faculty of Information Science and Technology Center for Cyber Security Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Department of Scientific Affairs AI-Zahraa University for Women Bangi Malaysia Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Innovative grid technology leverages Information and Communication technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a “novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)” ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces.

关键词： Renewable energy sources Smart homes Microgrids Data processing Energy efficiency Smart grids Information and communication technology Internet of Things Optimization Edge computing

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