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GENOME:Genetic Encoding for Novel Optimization of Malware Detection and Classification in Edge Computing

computers, Materials & Continua 2025年第3期82卷 4021-4039页

作者： Sang-Hoon Choi Ki-Woong Park SysCore Lab Sejong UniversitySeoul05006Republic of Korea Department of Computer and Information Security Sejong UniversitySeoul05006Republic of Korea

The proliferation of Internet of Things(IoT)devices has established edge computing as a critical paradigm for real-time data analysis and low-latency ***,the distributed nature of edge computing presents substantial security challenges,rendering it a prominent target for sophisticated malware *** signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of *** paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation(GENOME)framework,a novel solution that is intended to improve the performance of malware detection and classification in peripheral computing *** optimizes data storage and computa-tional efficiency by converting malware artifacts into compact,structured sequences through a Deoxyribonucleic Acid(DNA)encoding *** framework employs two DNA encoding algorithms,standard and compressed,which substantially reduce data size while preserving high detection *** Edge-IIoTset dataset was used to conduct experiments that showed that GENOME was able to achieve high classification performance using models such as Random Forest and Logistic Regression,resulting in a reduction of data size by up to 42%.Further evaluations with the CIC-IoT-23 dataset and Deep Learning models confirmed GENOME’s scalability and adaptability across diverse datasets and *** potential of GENOME to address critical challenges,such as the rapid mutation of malware,real-time processing demands,and resource limitations,is emphasized in this *** offers comprehensive protection for peripheral computing environments by offering a security solution that is both efficient and scalable.

关键词： Edge computing IoT security malware machine learning malware classification malware detection

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Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

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computers, Materials & Continua 2025年第2期82卷 3337-3353页

作者： Tanzila Saba Muhammad Mujahid Shaha Al-Otaibi Noor Ayesha Amjad Rehman Khan Artificial Intelligence&Data Analytics Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.

关键词： Medical image processing deep learning healthcare image classification histopathology

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Efficient multi-scale community search method based on spectral graph wavelet

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Frontiers of computer Science 2023年第5期17卷 89-101页

作者： Cairui YAN Huifang MA Qingqing LI Fanyi YANG Zhixin LI College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China

Community search is an important problem in network analysis,which has attracted much attention in recent *** a query-oriented variant of community detection problem,community search starts with some given nodes,pays more attention to local network structures,and gets personalized resultant communities *** existing community search method typically returns a single target community containing query nodes by *** is a strict requirement and does not allow much *** many realworld applications,however,query nodes are expected to be located in multiple communities with different *** address this limitation of existing methods,an efficient spectral-based Multi-Scale Community Search method(MSCS)is proposed,which can simultaneously identify the multi-scale target local communities to which query node *** MSCS,each node is equipped with a graph Fourier multiplier *** access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community *** addition,an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral *** experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

关键词： community search multi-scale spectral wavelets

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Aspect-level sentiment analysis based on semantic heterogeneous graph convolutional network

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Frontiers of computer Science 2023年第6期17卷 87-99页

作者： Yufei ZENG Zhixin LI Zhenbin CHEN Huifang MA Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541004China College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis(ABSA).However,the accuracy of the dependency parser cannot be determined,which may keep aspect words away from its related opinion words in a dependency ***,few models incorporate external affective knowledge for *** on this,we propose a novel architecture to tackle the above two limitations,while fills up the gap in applying heterogeneous graphs convolution network to ***,we employ affective knowledge as an sentiment node to augment the representation of ***,linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency ***,we design a multi-level semantic heterogeneous graph convolution network(Semantic-HGCN)to encode the heterogeneous graph for sentiment *** experiments are conducted on the datasets SemEval 2014 Task 4,SemEval 2015 task 12,SemEval 2016 task 5 and ACL 14 *** experimental results show that our method achieves the state-of-the-art performance.

关键词： heterogeneous graph convolution network multi-head attention network aspect-based sentiment analysis deep learning affective knowledge

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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular Networking

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 2217-2230页

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

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Phishing Classification Based on Text Content of an Email Body Using Transformers

Phishing Classification Based on Text Content of an Email Bo...

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International Conference on information security, Privacy, and Digital Forensics, 2022

作者： Somesha, M. Pais, Alwyn R. Department of Computer Science and Engineering Information Security Research Lab National Institute of Technology Karnataka Surathkal575025 India

ISBN: (纸本)9789819950904

Phishing attacks steal sensitive credentials using different techniques, tools, and some sophisticated methods. The techniques include content injection, information re-routing, social engineering, server hacking, social networking, SMS and WhatsApp mobile applications. To overcome such attacks and minimize risks of such attacks, many phishing detection and avoidance techniques were introduced. Among various techniques, deep learning algorithms achieved the efficient results. In the proposed work, a transformers-based technique is used to classify phishing emails. The proposed method outperformed the other similar mechanisms for the classification of phishing emails. The phishing classification accuracy achieved by the proposed work is 99.51% using open-source datasets. The proposed model is also used to learn and validate the correctness of the in-house created datasets. The obtained results with in-house datasets are equally competitive. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic mail

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CYBERSHIELD: A Competitive Simulation Environment for Training AI in Cybersecurity 11

CYBERSHIELD: A Competitive Simulation Environment for Traini...

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11th International Conference on Internet of Things: Systems, Management and security, IOTSMS 2024

作者： Carrasco, José Álvaro Fernández Pagola, Iñigo Amonarriz Urrutia, Raúl Orduna Román, Rodrigo San Sebastian Donostia20009 Spain University of Malaga Network Information and Computer Security Lab Malaga29071 Spain

ISBN: (纸本)9798350366501

Simulation environments are crucial in cybersecurity for training AI algorithms, providing a secure and controlled space to explore the impact derived from diverse cyber threads. They offer a flexible platform for accelerated learning and development, especially useful to train Decision Support Systems in response contexts. This paper introduces CyberShield, an advanced simulation environment tailored for cybersecurity tasks, specifically focusing on supporting the implementation and evaluation of AI algorithms, with an emphasis on RL. CyberShield encompasses a comprehensive environment with multiple computers, each hosting various services with unique vulnerabilities. Within this environment, two opposing agents, defender and attacker, participate in a strategic battle, each equipped with distinct actions aimed at outsmarting the other. CyberShield is optimized for competitive multi-agent training using RL algorithms. The specifically adapted environment facilitates the exploration of novel strategies, thereby fostering the automation of defense and attack strategies. Taking inspiration from Capture The Flag (CTF) competitions, CyberShield offers a dynamic and realistic platform for refining advanced cybersecurity techniques, fostering innovation, and strengthening the resilience of AI algorithms against sophisticated cyber threats. © 2024 IEEE.

关键词： Chatbots

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Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第12期20卷 1-23页

作者： Yu, Mengzhu Tang, Zhenjun Liang, Xiaoping Zhang, Xianquan Li, Zhixin Zhang, Xinpeng Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science Fudan University Shanghai China

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances. © 2024 Copyright held by the owner/author(s).

关键词： Tensors

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Enhancing TrUStAPIS Methodology in the Web of Things with LLM-Generated IoT Trust Semantics 26th

Enhancing TrUStAPIS Methodology in the Web of Things with LL...

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26th International Conference on information and Communications security, ICICS 2024

作者： Ferraris, Davide Kotis, Konstantinos Kalloniatis, Christos Network Information and Computer Security Lab University of Malaga Malaga Spain Department of Cultural Technology and Communication University of the Aegean Mytilene Greece

ISBN: (纸本)9789819787975

In the Internet of Things (IoT) there are ecosystems where their physical’smart’ entities virtually interact with each other. Often, this interaction occurs among unknown entities, making trust an essential requirement to overcome uncertainty in several aspects of this interaction. However, trust is a complex concept, and incorporating it in IoT is still a challenging topic. For this reason, it is highly significant to specify and model trust in early stages of the System Development Life Cycle (SDLC) of IoT-integrated systems, thus enhancing the aforementioned task. TrUStAPIS is a requirements engineering methodology recently introduced for incorporating trust requirements during IoT-based system design. The scope of this paper is to provide an extension of TrUStAPIS by introducing IoT trust semantics compatible with the W3C Web of Things (WoT) recommendations generated with the assistance of Large Language Models (LLMs). Taking advantage of LLMs as a tool for integrating and refining existing methodologies, in this paper we present our work towards a revision of the TrUStAPIS methodology. In this work, we contribute a new conceptual model and a refined JSON-LD ontology that takes into account IoT trust semantics, providing eventually a valuable tool for software engineers to design and model IoT-based systems and services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Requirements engineering

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Robust Quantum Feature Selection With Sparse Optimization Circuit

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第7期44卷 2613-2626页

作者： Li, Jiaye Song, Jiagang Shi, Jinjing Xu, Hang Yu, Hao Chen, Gang Zhang, Shichao Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Institute of Blockchain and Data Security China Central South University School of Computer Science and Engineering Changsha410083 China Central South University School of Electronic Information Changsha410083 China Xiangtan University School of Computer Science and School of Cyberspace Science Hunan Xiangtan411105 China Guangxi Normal University Guangxi Key Lab of Multisource Information Mining and Security Guilin541004 China

High-dimensional data has long been a notoriously challenging issue. Existing quantum dimension reduction technology primarily focuses on quantum principal component analysis. However, there are only a few studies on quantum feature selection (QFS) algorithms, and these algorithms are often not robust. Additionally, there are limited quantum circuits specifically designed for feature selection, and they still cannot address the objective function based on sparse learning. To address these issues, this paper proposes a robust QFS algorithm by designing a novel sparse optimization circuit. Specifically, we first apply sparse regularization and least squares loss to construct the proposed objective function. Then, six types of quantum registers and their initial states are prepared. Furthermore, quantum techniques such as quantum phase estimation and controlled rotation are employed to construct a sparse optimization circuit, which is used to obtain the final quantum state of the feature selection ***, a series of experiments are conducted to verify the accuracy of the feature selection and the robustness of the proposed algorithm. © 1982-2012 IEEE.

关键词： Quantum electronics

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