110023 research outputs found
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Orchestration of APT malware evasive manoeuvers employed for eluding anti-virus and sandbox defense
No full text[[abstract]]The modern day cyber attacks are highly targeted and incorporate advanced tactics, techniques and procedures for greater stealth, impact and success. These attacks are also known as Advanced Persistent Threats(APT) because of their evasive and stealth nature along with longer foothold on the victim’s digital infrastructure. The malware involved in APT attacks are sophisticated and developed with the intention of sabotaging the victim’s digital infrastructure or performing espionage. They are capable of targeting multiple operating environments starting from desktop and server operating systems (Windows, Linux and MacOS), Mobile platforms (Android, iOS), Embedded platforms (IoT Devices), to Industrial control systems (ICS/SCADA Devices). The evolution of evasive tactics and techniques employed in such advanced malware leads to extensive research efforts to develop mechanisms that can counter these evasion techniques. The research primarily aims to demonstrate that evasive manoeuvers are currently over-weighing the security countermeasures deployed by the prevalent security solutions. This paper will first explain the evasion mechanism in a systematic manner employed in modern APT malware and aims to implement a novel Evasive Manoeuvers Re-Engineering Framework(EMRF).EMRF aims to establish and demonstrate combinations of evasive manoeuvers with much known APT malware samples to elude security solutions. The payload variants, i.e., executable, dynamic link library, and shell-code, were experimented through a research-based framework EMRF to demonstrate 36% to 96% of evasive behavior countering the majority of defender engines. The EMRF system with its dynamic user defined evasion manoeuvers is able to transform non-zero-day payloads more potent by evading majority of the modern security solutions. This research clearly demonstrates the attacker’s ability to deliver non-zero-day payloads easily rather than investing resources and time in discovering zero-day exploits and developing zero-day payloads. This important observation can potentially disrupt the Advanced Persistent Threat Defenses incorporated in modern day security solution where focus is mainly on to detect zero-day payloads and exploits. Exhibiting the threat landscape poised due to APT, the paper utilizes a dataset of 4403 APT malware samples to extract and orchestrate the prevalence of evasive manoeuvers like stealth, covert communication, and anti-analysis mechanisms. This paper will contribute towards advanced malware analysis as an avenue to analyzing intrusion, evasion, and deception to prevent detection and verification, an association of responsibility, and determination of intent
Pulse-line intersection method with unboxed artificial intelligence for hesitant pulse wave classification
No full text[[abstract]]State-of-the-art artificial intelligence (AI) methods are progressively strengthened in Traditional Chinese Medicine (TCM) pulse palpation, aiding physicians to make comprehensive preliminary clinical decisions through non-invasive diagnostics. One of the well-known proven examinations i.e., hesitant pulse wave diagnosis, is a sign that the blood circulation of a person is sluggish. This examination provides a preliminary diagnosis for physiological problems. Modern AI methods such as artificial neural networks achieve better performance than traditional methods; however, the final decision of such examination lacks of interpretability. In clinical situations, patients need an easy-to-understand diagnosis to be provided for selecting appropriate clinical treatment. Therefore, this study presents feature extraction and clinical decision support systems based on Pulse-Line Intersection (PLI) and eXplainability AI (XAI) methods. The pulses were recorded from 46 patients in six different measurement points for six seconds. In addition, a comparison of several AI methods was provided to classify hesitant and normal pulse. The contribution of each feature in the classification process was analyzed by unboxing each predictive intelligence model. The results revealed that all models performed comparably, evaluated using performance matric on the testing data with average F1-score of Logistic Regression, Support Vector Machine, Random Forest, XGBoost, Multi-Layer Perceptron, and Long Short-Term Memory were 0.74, 0.74, 0.74, 0.78, 0.73, and 0.80, respectively. This work suggests that modern AI methods can provide more comprehensive explainability and higher accuracy than traditional method rankings
Real-Time Traffic Speed Estimation for Smart Cities with Spatial Temporal Data: A Gated Graph Attention Network Approach
No full text[[abstract]]Moving vehicles interact with IoT devices deployed in cities and establish social relationships to provide proactive and intelligent services for smart cities. For example, big-data-driven accurate and timely traffic speed prediction systems play an important role in empowering Intelligent Transportation Systems (ITS) in smart cities. The reason is that it is the foundation of modern traffic management and traffic control. Most of the existing advanced traffic speed prediction models are Spatial-Temporal hybrid models. They improve the predicting accuracy by leveraging Graph Convolutional Network (GCN) and Recurrent Neural Network (RNN) to extract spatial and temporal features from the traffic speed data, respectively. However, these models have complex structures and high computational costs. To improve the accuracy of prediction and reduce the cost of model training, we propose a hybrid model, Spatial-Temporal Gated Graph Attention network (ST-GGAN), based on Graph Attention mechanism (GAT) and Gated Recurrent Unit (GRU). Such a method has a simpler structure, lower computational costs, and higher predicting accuracy. The experimental results show that our model's performance is better than the existing advanced models on a real-world dataset
The Experience of Viewing Sports and The Meaning of Leisure Value for The Visually Impaired
No full textSPSS for Analyses of People's Willingness to Purchase Formosa Sexy Products
No full text[[abstract]]本論文研究目的對於台灣職業籃球聯盟P. LEAGUE+的啦啦隊大眾觀感，與周邊商品及附加價值，以提供未來發展性參考。
本研究問卷由問卷調查法進行資料蒐集，一般民眾作為研究對象。於Instagram限時動態、Facebook社團、LINE社群發放問卷，實際收回76份有效問卷，主要以Google問卷方式進行調查。
問卷分為三個面向:第一面向為「個人基本資料」，第二面向為「了解P. LEAGUE聯盟與對於啦啦隊觀感」，第三面向為「針對Formosa Sexy商業行為之消費意願」。將所得問卷資料以複選題分析、敘述統計分析（descriptive statistics）、信度分析(Cronbach's alpha)、交叉分析 (cross analysis)，進行相關分析。
本研究統計分析結果顯示女性對於商品購買意願相比男性較低，月收入30000 ~ 39999元之間較願意購買周邊商品，學生及上班族較常至球場觀賽，而學歷與球場觀賽及商品購買意願較無關。
因此本研究建議可以多增加女性取向之商品及學生收入範圍可購買商品之價格為參考依據。[[abstract]]The purpose of this thesis is to study the interests of the Taiwan Professional Basketball League P. LEAGUE+ cheerleaders’ team public perception, and peripheral goods and added value to provide future development reference.
This research questionnaire is collected by the questionnaire method, and the general public is the research object. Questionnaires were distributed on Instagram, Facebook, and LINE, and 76 valid questionnaires were actually collected, mainly in the form of Google questionnaires.
The questionnaire is divided into three aspects: the first is "Basic personal information" and the second is "Understanding P. LEAGUE Alliance and Perception of cheerleaders team", and the third aspect is "Consumer Willingness for Formosa Sexy Business Practices". The resulting: Analysis of multiple-choice questions, descriptive statistics, Cronbach's alpha and cross analysis.
The statistical analysis results of this study show that women are less willing to buy goods than men, with a monthly income of 30000 ~ 39999 yuan, and students and office workers are more likely to watch the stadium, while education has nothing to do with the willingness to watch the game and the purchase of goods.
Therefore, this study suggests that more women-oriented products and the prices of purchasable goods in the student's income range can be used as a reference
Power Quality Disturbances Recognition Using a Pretrained Algorithm and an Efficient Compression Combination Framework
No full text[[abstract]]本研究側重於電能質量擾動 (PQD) 的數據預處理，用於電能質量擾動識別過程。 最具挑戰性的問題是如何在不丟失關鍵信息或特徵的情況下分析海量數據。數據處理的缺點包括訓練時間、過擬合以及由於壓縮導致的電能質量擾動數據特徵的大量損失。
提出了一種新技術來克服這個問題，它將有效的一維數據集壓縮與 CNN 分類算法相結合。 所提出的方法表明，通過結合 CNN 壓縮和分類方法，可以有效地識別電能質量擾動。 電能質量擾動信號處理的準確率高達 98.25%，並在嘈雜的條件下管理過擬合。
該研究還分析了基於響應響應的二維 CNN 電能質量分類器如何通過將預訓練的電能質量擾動數據與深度學習方法相結合來顯著提高現場電能質量, 如 ResNet50、MobileNet 和 EfficientNetB0。 結果表明，增強型 MobileNet 是一個相當合適的模型。 該模型具有 99.32% 的準確率、最短的文件大小和最短的計算時間，超過了其他預訓練方法。[[abstract]]This research focuses on the data preprocessing of power quality disturbance (PQDs) to be used in the PQD recognition procedure. The most challenging problem is how to analyze massive amounts of data without losing key information or features. Data processing shortcomings include training time, overfitting, and considerable loss of PQD data features owing to compression.
A new technique is proposed to overcome this problem, combining effective 1-Dimensional dataset compression with the CNN classification algorithm. The proposed method indicates that by combining CNN compression and classification methods, PQDs could be identified efficiently. PQD signal processing reached up to 98.25 percent accuracy and managed to overfit in noisy conditions.
The study also analyzes how responsive response-based 2D CNN power-quality classifiers produce meaningful gains in field power quality by combining pretrained PQD data with deep-learning approaches such as ResNet50, MobileNet, and EfficientNetB0. The outcome demonstrates that enhanced MobileNet is a fairly well-fitting model. This model exceeds the other pretraining approaches with 99.32% accuracy, the shortest file size, and the shortest computation time
Business Model Innovation of Funeral Industry：A Case Study of Lungyen
No full text[[abstract]]台灣殯葬產業近年來因相關法令修改完整而令消費者認同提升，從每年500億市場增加至1250億消費額，且社會高齡化已遠超越聯合國世界衛生組織7％之標準，行政院經建會指出，2018年台灣65歲以上的老年人口比率將達14.65％，到了2026年，台灣更將走入超高齡社會，老年人口比率達20.63％，凸顯台灣殯葬產業急需導入創新商業模式，然而，過去對於殯葬業的研究大多著重於探討殯葬產業動態研究、殯葬設施、消費者行為研究、消費者滿意度研究及生前契約等議題，少有探討殯葬業商業模式創新模式的相關研究。故本研究採用Johnson (2010) 所提出的商業模式架構，以顧客價值主張、關鍵資源、關鍵流程、獲利模式等四構面，探討殯葬業商業模式創新。研究個案為龍巖股份有限公司，資料來源包括次級資料、參與式觀察、與專家訪談，並將以個案歷年所推出之較重大之服務創新事件為分析單元。[[abstract]]Taiwan’s funeral industry has gradually gained recognition from consumers in recent years, thanks to the amendment of relevant laws and regulations. The funeral market has grown from NT125 billion a year as the aged population now far exceeds the World Health Organization’s (WHO) standard of 7% for an aging society. The National Development Council pointed out that Taiwan’s population at ages 65 and above will reach 14.65% in 2018, and will reach 20.63% in 2026, making Taiwan a hyper-aged society. This shows the urgency for Taiwan’s funeral industry to adopt innovative business models. Past studies, however, mainly focused on the funeral industry’s trends, funeral facilities, consumer behavior, consumer satisfaction, and pre-need funeral contracts, and rarely discussed business model innovation. This study adopts the framework for business model innovation proposed by Johnson (2010), which includes four dimensions, namely customer value proposition, key resource, key process, and profit formula, for examining business model innovation in the funeral industry. The case presented in this study is Lungyen Life Service Corporation. Sources of data include secondary data, participatory observation, and expert interviews, and the unit of analysis is business model innovation events of Lungyen over the years
Evaluation of the mechanisms and histological application of EGFR mutation mediated regional lymph node and distant metastasis of oral squamous cell carcinoma
No full text[[note]]科技部[[note]]2018-08-01～2019-07-3
新冠疫情期間危機領導、心理社會安全氛圍、死亡意識對教師工作投入的影響：教師責任感的調節作用
No full text[[abstract]]本研究為二年期計畫，第一年研究目的在於檢驗新冠疫情期間危機領導對教師工作投入的關係模式，並以心理社會安全氛圍、死亡意識為中介變項，建構危機領導對教師工作投入間的兩個中介變項的多項中介效果模式(multiple mediation model with two mediators)。同時以責任感為調節變項，檢視心理社會安全氛圍和死亡意識與教師工作投入間的調節效果。第二年研究目的在於檢驗不同疫情期間對危機事件強度的看法在危機領導與教師工作投入之間的關係的交叉延宕效應。本研究成果可作為國家教育研究院職前校長培訓與在職校長進修班危機領導課程以及高級中等以下學校校長危機領導之實務應用參考。 [[note]]科技部[[note]]2022-08-01～2023-07-3
探討GDF-15在Metformin保護放射治療導致心肌細胞之粒線體損傷的角色
No full text[[abstract]]Metformin是臨床普遍使用之藥物，我們預期本研究結果將能由GDF-15的角度，提供Metformin是否藉由GDF-15調節粒線體而保護放射線治療導致心肌細胞受損的分子機轉。 [[note]]科技部[[note]]2022-08-01～2023-07-3