检索结果-内蒙古大学图书馆

The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation

The Effect of Data Partitioning Strategy on Model Generaliza...

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Liu, Zoey Dorr, Bonnie J. Department of Linguistics University FL United States Computer & Information Science & Engineering Florida Institute for National Security University FL United States

ISBN: (纸本)9798891761148

Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations related to language diversity, adoption of multiple datasets and splits, and detailed model comparisons. Our study leverages data from 19 languages, including ten indigenous or endangered languages across 10 language families with diverse morphological systems (polysynthetic, fusional, and agglutinative) and different degrees of data availability. We conduct large-scale experimentation with varying sized combinations of training and evaluation sets as well as new test data. Our results show that, when faced with new test data: (1) models trained from random splits are able to achieve higher numerical scores;(2) model rankings derived from random splits tend to generalize more consistently. ©2024 Association for Computational Linguistics.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

Hiding information in a Reordered Codebook Using Pairwise Adjustments in Codewords 5

Hiding Information in a Reordered Codebook Using Pairwise Ad...

引用

5th International Conference on computer Vision and Computational Intelligence, CVCI 2024

作者： Chang, Chin-Chen Liu, Jui-Chuan Chang, Ching-Chun Lin, Yijie Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan Information and Communication Security Research Center Feng Chia University Taichung407 Taiwan

ISBN: (数字)9781510680067

ISBN: (纸本)9781510680050

The fundamental data structure of our proposed scheme is a pixel pair. The pixel values are adjusted through simple yet well-designed equations depending on different conditions. The computation method provides an easy recovery of the original values for the pixel pair as well. For some applications, it’s important to extract the hiding data as well as to recover the original media. In order to apply to a wider range of applications, more and more reversible data hiding (RDH) schemes are introduced or enhanced in the recent decades. Therefore, the two goals of the novel scheme proposed are to increase embedding capacity and to recover original media easily. For vector quantization (VQ) compression, it’s a well-known and efficient method to compress images. A well-trained codebook is crucial to maintain the visual quality of recovered images as good as possible. An index table is generated based on the codebook to full-fill the purpose of compression. Most of studies utilize index tables as the embedding carriers for VQ or VQ-variant compression methods. There are a few state-of-the-art schemes make use of codebooks to embed secret and our proposed scheme extends the study on the codebook embedding. The codewords in a codebook are divided into pixel pairs and secret bits are embedded by adjusting the pixel values of these split pairs to form a stego codebook. During extraction, secret bits are extracted first out from the stego codebook and a recovered codebook is obtained before recovering VQ compressed images. © 2024 SPIE.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

A stacked ensemble approach to detect cyber attacks based on feature selection techniques

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 316-331页

作者： Urmi, Wahida Ferdose Uddin, Mohammed Nasir Uddin, Md Ashraf Talukder, Md. Alamin Hasan, Md. Rahat Paul, Souvik Chanda, Moumita Ayoade, John Khraisat, Ansam Hossen, Rakib Imran, Faisal Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia School of Information Technology Crown Institute of Higher Education 116 Pacific Highway North SydneyNSW2060 Australia Department of Cyber Security Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakoir Gazipur1750 Bangladesh Department of Electrical and Computer Engineering University of Ottawa Canada

The exponential growth of data and increased reliance on interconnected systems have heightened the need for robust network security. Cyber-Attack Detection Systems (CADS) are essential for identifying and mitigating threats through network traffic analysis. However, the effectiveness of CADS is highly dependent on selecting pertinent features. This research evaluates the impact of three feature selection techniques—Recursive Feature Elimination (RFE), Mutual information (MI), and Lasso Feature Selection (LFS)—on CADS performance. We propose a novel stacked ensemble classification approach, combining Random Forest, XGBoost, and Extra-Trees classifiers with a Logistic Regression meta-model. Performance is assessed using CICIDS2017 and NSL-KDD datasets. Results show that RFE achieves 100% accuracy for Brute Force attacks, 99.99% for Infiltration and Web Attacks on CICIDS2017, and 99.95% accuracy for all attacks on NSL-KDD, marking a significant improvement over traditional methods. This study demonstrates that optimizing feature selection and leveraging diverse classifiers can substantially enhance the accuracy of CADS, providing stronger protection against evolving cyber threats. © 2024 The Authors

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

引用

4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Personalized Food Recommendations: A Machine Learning Model for Enhanced Dining Choices 14

Personalized Food Recommendations: A Machine Learning Model ...

引用

14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Al-Hubaishi, Mohammed Ali, Musab A. M. Tahir, Nooritawati Md Halic University Department of Computer Engineering Istanbul Turkey University of Technology and Applied Sciences College of Computing and Information Sciences Oman International Information Technology University Department of Computer Engineering and Information Security Kazakhstan Universiti Teknologi MARA Malaysia Universiti Teknologi MARA College of Engineering Malaysia Riyadh Saudi Arabia

ISBN: (纸本)9798350364507

This research introduces an innovative approach to enhance dining experiences through a personalized meal suggestion system. Our method leverages decision tree-based machine learning to predict user preferences by analyzing various attributes. The aim is to assist individuals in making optimal food choices at restaurants, thereby enriching their culinary journey. QR codes are integrated into an Android restaurant application, facilitating convenient access to the food menu. User inquiries are efficiently managed through a MySQL database accessible across multiple platforms, including a Java GUI, an Android app, and a dedicated website catering to both user and administrator roles. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Supported Network Attack Detection with a Novel Method Based on Random Forest-Based Feature Fusion 17

Machine Learning Supported Network Attack Detection with a N...

引用

17th International Conference on information security and Cryptology, ISCTurkiye 2024

作者： Cinar, Cihan Dogru, Ibrahim Alper Atacak, Ismail Information Security Engineering Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

Nowadays, Innovative techniques, including artificial intelligence and machine learning (ML), have been employed in attack detection systems across numerous domains to create effective detection mechanisms that identify attacks more quickly and accurately. In this study, we propose a novel machine learning-based method that utilizes Random Forest (RF)-based feature fusion (RFBFF) to detect anomalies in computer networks. This method integrates two basic procedures: feature fusion and classification. In feature fusion, unlike traditional feature selection, all features are utilized in the creation of the new data set. This allows for the incorporation of the impact of each feature on the outcome. The feature fusion procedure generates a reduced dataset by combining similar features based on RF-derived gain scores, while the classification procedure uses this refined dataset to label the attack status in the network using various ML algorithms. Specifically, Decision Tree (DT), K-Nearest Neighbor (k-NN), Additive Logistic Regression (Logitboost), and Naïve Bayes (NB) have been employed as classification algorithms. The effectiveness of the proposed method in network attack detection has been validated by comparing it with ML methods based on RF-based feature selection (RFBFS). For a robust comparison and see the performance of the model in the real world, the NSL-KDD and UNSW_NB15 data sets, which contain data packet examples of normal or attack traffic that can be encountered in the internet environment, categorized with binary classification, were preferred as the benchmark data set. Experimental results demonstrate that the proposed approach excels in detecting network attacks, achieving an accuracy rate of 0.995 on the NSL-KDD dataset and 0.924 on the UNSW-NB15 dataset. Leveraging the gain scores of all features, even if they are relatively low, has been found to positively impact the model's overall performance. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Classification of Phishing Email Using Word Embedding and Machine Learning Techniques

引用

Journal of Cyber security and Mobility 2022年第3期11卷 279-320页

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

Email phishing is a cyber-attack, bringing substantial financial damage to corporate and commercial organizations. A phishing email is a special type of spamming, used to trick the user to disclose personal information to access his digital assets. Phishing attack is generally triggered by emailing links to spoofed websites that collect sensitive information. The APWG survey suggests that the existing countermeasures remain ineffective and insufficient for detecting phishing attacks. Hence there is a need for an efficient mechanism to detect phishing emails to provide better security against such attacks to the common user. The existing open-source data sets are limited in diversity, hence they do not capture the real picture of the attack. Hence there is a need for real-time input data set to design accurate email anti-phishing solutions. In the current work, it has been created a real-time in-house corpus of phishing and legitimate emails and proposed efficient techniques to detect phishing emails using a word embedding and machine learning algorithms. The proposed system uses only four email header-based heuristics for the classification of emails. The proposed word embedding cum machine learning framework comprises six word embedding techniques with five machine learning classifiers to evaluate the best performing combination. Among all six combinations, Random Forest consistently performed the best with FastText (CBOW) by achieving an accuracy of 99.50% with a false positive rate of 0.053%, TF-IDF achieved an accuracy of 99.39% with a false positive rate of 0.4% and Count Vectorizer achieved an accuracy of 99.18% with a false positive rate of 0.98% respectively for three datasets used. © 2022 River Publishers.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

An Explainable Deep Learning-based Approach for Multivariate Time Series Anomaly Detection in IoT 20

An Explainable Deep Learning-based Approach for Multivariate...

引用

20th International Conference on Frontiers of information Technology, FIT 2023

作者： Toor, Aafan Ahmad Lin, Jia-Chun Gran, Ernst Gunnar Lee, Ming-Chang Department of Information Security and Communication Technology Gjovik Norway Electrical Engineering and Mathematical Sciences Department of Computer Science Bergen Norway

ISBN: (纸本)9798350395785

Detecting anomalies from Internet of Things (IoT) data is important for a smooth flow of events. Timely detection of anomalies and raising alarms can help avoid any potential health and safety hazards in an IoT environment such as detecting falling people by activity recognition using wearable sensors. However, most IoT data lacks labels and sometimes when the labels are available, the context is missing, which makes anomaly detection and inference a challenging task. In this paper, we propose an explainable deep learning-based approach to detect anomalies from multivariate time series data using Long Short-Term Memory (LSTM) and the results are explained using the Local Interpre table Model-agnostic Explanations (LIME). The experiments show promising classification results with good accuracy, precision, recall, and F1 Score values. LIME provided useful explanations for anomalous and non-anomalous instances. The effectiveness of LIME for time series anomaly detection is discussed, and its limitations are also highlighted. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

引用

computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

来源：评论

学校读者我要写书评

暂无评论

Assessing the Effects of Blackhole Attacks on MANET Reliability and security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：