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Falling for Phishing: An empirical investigation into people's email response behaviors

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arXiv 2021年

作者： Jayatilaka, Asangi Gamagedara Arachchilage, Nalin Asanka Babar, Muhammad Ali CREST Centre for Research on Engineering Software Technologies School of Computer Science The University of Adelaide Australia School of Computer Science The University of Auckland New Zealand CREST Centre for Research on Engineering Software Technology School of Computer Science The University of Adelaide Australia

Despite sophisticated phishing email detection systems, and training and awareness programs, humans continue to be tricked by phishing emails. In an attempt to better understand why phishing email attacks still workand how best to mitigate them, we have carried out an empirical study to investigate people’s thought processes when reading their emails. We used a scenario-based role-play "think aloud" method and follow-up interviews to collect data from 19 participants. The experiment was conducted using a simulated web email client, and real phishing and legitimate emails adapted to the given scenario. The analysis of the collected data has enabled us to identify eleven factors that influence people’s response decisions to both phishing and legitimate emails. Furthermore, based on the user study findings, we discuss novel insights into flaws in the general email decision-making behaviors that could make people susceptible to phishing attacks. © 2021, CC BY.

关键词： Electronic mail

Research of Sampling Method For Cybercrime Black Industry Chain

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Research of Sampling Method For Cybercrime Black Industry Ch...

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Ying’An Cui Xue Li Yande Wu Department of Computer Science and Engineering Xi’an University of Technology Xi’an China School of International Business Shaanxi Normal University Xi’an China Software R&D Department 2 China Unicom Western Innovation Research Institute Co. Ltd Xi’an China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In modern organization theories, the social network established by cyber criminals is termed the crime cyber black industry chain. Under the current technical level, treating a crime cyber black industry chain as a whole dataset has to face severe challenges in many aspects, so the research of the sampling method specifically for it has been one of the current hot topics. Based on the structural feature of a cybercrime industry chain, the overall framework and the detailed algorithm of a multi-stage sampling method are proposed. Tested with real cybercrime datasets, our proposed multi-stage sampling method outperforms existing mainstream single-stage sampling methods at macro, meso, and micro scales, indicating it can solve practical problems with high efficiency and low cost.

关键词： Industries Costs Social networking (online) Organizations Sampling methods Information and communication technology Reliability

FP-VEC: Fingerprinting Large Language Models via Efficient Vector Addition

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arXiv 2024年

作者： Xu, Zhenhua Xing, Wenpeng Wang, Zhebo Hu, Chang Jie, Chen Han, Meng School of Software Technology Zhejiang University Hangzhou China Binjiang Institute of Zhejiang University Zhejiang University Hangzhou China School of Communication Engineering Hangzhou Dianzi University Hangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong GenTel.io Zhejiang University Hangzhou China

Training Large Language Models (LLMs) requires immense computational power and vast amounts of data. As a result, protecting the intellectual property of these models through fingerprinting is essential for ownership authentication. While adding fingerprints to LLMs through fine-tuning has been attempted, it remains costly and unscalable. In this paper, we introduce FP-VEC, a pilot study on using fingerprint vectors as an efficient fingerprinting method for LLMs. Our approach generates a fingerprint vector that represents a confidential signature embedded in the model, allowing the same fingerprint to be seamlessly incorporated into an unlimited number of LLMs via vector addition. Results on several LLMs show that FP-VEC is lightweight by running on CPU-only devices for fingerprinting, scalable with a single training and unlimited fingerprinting process, and preserves the model's normal behavior. The project page is available at https://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

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computers, Materials & Continua 2020年第4期63卷 489-507页

作者： Yong Yu Yongjun Luo Tong Li Shudong Li Xiaobo Wu Jinzhuo Liu Yu Jiang School of Software Key Laboratory in Software Engineering of Yunnan ProvinceYunnan UniversityKunming650091China School of Big Data Yunnan Agricultural UniversityKunming650201China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current *** this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is *** edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between *** can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender *** verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.

关键词： Personalized recommendation one-mode projection weighted bipartite network novelty recommendation diversity

CHIEF: Clustering with Higher-order Motifs in Big Networks

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arXiv 2022年

作者： Xia, Feng Yu, Shuo Liu, Chengfei Li, Jianxin Lee, Ivan School of Engineering IT and Physical Sciences Federation University Australia Ballarat Australia School of Software Dalian University of Technology Dalian China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia School of IT Deakin University Melbourne Australia STEM University of South Australia Adelaide Australia

Clustering a group of vertices in networks facilitates applications across different domains, such as social computing and Internet of Things. However, challenges arises for clustering networks with increased scale. This paper proposes a solution which consists of two motif clustering techniques: standard acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms first find the maximal k-edge-connected subgraphs within the target networks to lower the network scale, then employ higher-order motifs in clustering. In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs. For CHIEF-ST, we illustrate that all target motifs will be kept after this procedure when the minimum node degree of the target motif is equal or greater than k. For CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the Laplacian matrix are relatively stable after this step. That is, CHIEF-ST has no influence on motif clustering, whereas CHIEF-AP introduces limited yet acceptable impact. In the second procedure, we employ higher-order motifs, i.e., heterogeneous four-node motifs clustering in higher-order dense networks. The contributions of CHIEF are twofold: (1) improved efficiency of motif clustering for big networks;(2) verification of higher-order motif significance. The proposed solutions are found to outperform baseline approaches according to experiments on real and synthetic networks, which demonstrates CHIEF's strength in large network analysis. Meanwhile, higher-order motifs are proved to perform better than traditional triangle motifs in clustering. © 2022, CC BY-NC-SA.

关键词： Eigenvalues and eigenfunctions

Learning Hierarchical Graph Representation for Image Manipulation Detection

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arXiv 2022年

作者： Pan, Wenyan Zhou, Zhili Ling, Miaogen Geng, Xin Wu, Q.M. Jonathan School of Computer and Software & Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China School of Computer Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering University of Windsor WindsorON Canada

The objective of image manipulation detection is to identify and locate the manipulated regions in the images. Recent approaches mostly adopt the sophisticated Convolutional Neural Networks (CNNs) to capture the tampering artifacts left in the images to locate the manipulated regions. However, these approaches ignore the feature correlations, i.e., feature inconsistencies, between manipulated regions and non-manipulated regions, leading to inferior detection performance. To address this issue, we propose a hierarchical Graph Convolutional Network (HGCN-Net), which consists of two parallel branches: the backbone network branch and the hierarchical graph representation learning (HGRL) branch for image manipulation detection. Specifically, the feature maps of a given image are extracted by the backbone network branch, and then the feature correlations within the feature maps are modeled as a set of fully-connected graphs for learning the hierarchical graph representation by the HGRL branch. The learned hierarchical graph representation can sufficiently capture the feature correlations across different scales, and thus it provides high discriminability for distinguishing manipulated and non-manipulated regions. Extensive experiments on four public datasets demonstrate that the proposed HGCN-Net not only provides promising detection accuracy, but also achieves strong robustness under a variety of common image attacks in the task of image manipulation detection, compared to the state-of-the-arts. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

Enhancing trading strategies by combining incremental reinforcement learning and self-supervised prediction

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Expert Systems with Applications 2025年 289卷

作者： Chujin Zhou Yuling Huang Yuting Kong Xiaoping Lu School of Computer Science and Engineering Macau University of Science and Technology Macao China School of Computer Science and Software Zhaoqing University Zhaoqing Guangdong China School of Computers Guangdong University of Science and Technology Dongguan Guangdong China

Incremental learning provides critical adaptability in dynamic environments, enabling models to continuously adjust with new data to improve prediction accuracy. This adaptability is especially valuable in volatile financial markets, where incremental learning can help model better capture the emerging patterns of time series data. On the other hand, self-supervised learning gained significant attention during the past few years due to its ability to exploit abundant unlabeled data to uncover complex structures and temporal dependencies, improving model generalization and pattern detection, which can provide a more comprehensive understanding of data structures. In this case, it gradually become a powerful tool in the field of time series analysis, especially in financial domain. In parallel, deep reinforcement learning (DRL) has shown great potential in decision-making tasks, particularly in financial strategy optimization. However, most DRL approaches in finance rely solely on raw market data, limiting the model’s ability to extract key insights and often ignoring future trends essential for profitable trading. To address the aforementioned challenge appeared in DRL domain, Incremental Forecast Fusion Deep Reinforcement Learning (IFF-DRL), an innovative framework that combines incremental learning and self-supervised learning with DRL for financial trading is proposed, for accurately and continually optimizing and improving trading strategies. In this paper, AutoConNet, as a self-supervised learning method, is incorporated to forecast future OHLCV (Open, High, Low, Close, and Volume) data based on observed values. During testing, incremental learning dynamically refines the predictive model to stay aligned with market trends. By utilizing incremental learning algorithms, the self-supervised time series prediction network, AutoConNet, achieved an average reduction of 5.93 % in MSE error across six datasets during the testing phase. The predicted OHLCV is combined w

关键词： Algorithmic trading Deep reinforcement learning Incremental learning Self-supervised learning

Generalizable Metric Network for Cross-domain Person Re-identification

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arXiv 2023年

作者： Qi, Lei Liu, Ziang Shi, Yinghuan Geng, Xin School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education 211189 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning