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检索条件"机构=Dept. of Network and Computer Security"

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Robust Facial Emotion Recognition System via De-Pooling Feature Enhancement and Weighted Exponential Moving Average

Robust Facial Emotion Recognition System via De-Pooling Feat...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Win, Khin Cho Akhtar, Zahid Mohan, C. Krishna Indian Institute of Technology Dept. of Artificial Intelligence Hyderabad India State University of New York Polytechnic Institute Dept. of Network and Computer Security UticaNY United States Indian Institute of Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350350821

Facial expression recognition (FER) in an uncontrolled environment presents a formidable challenge in affective computing and human-machine interaction domains. Existing FER models fail to generalize due to the innate expression nature of the intra-class separability and inter-class compactness. Although most state-of-the-art FER research focuses on more advanced frameworks and well-separated discriminative loss functions, further exploration is needed to obtain high-quality expression features. This paper proposes a robust feature enhancement approach for FER by integrating the De-pooling Feature Enhancement and Weighted Exponential Moving Average (WEMA) of Stochastic Gradient Descent (SGD). The proposed method utilizes De-Pooling Feature Enhancement to capture and enhance the high-quality expression features, while WEMA of SGD optimizes the training process for improved stability and convergence. Our extensive experimental analysis of benchmark datasets signifies that our proposed method prevails over state-of-the-art methods, achieving superior accuracy performances of 86.43% on FER2013, 94.97% on FERPlus, 94.71% on RAF-DB, as well as 80.62% and 72.90% on AffectNet, of 7 class and 8 class, respectively. © 2024 IEEE.

关键词： Stochastic systems

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SES: Smart Education System 5

SES: Smart Education System

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Alyammahi, Wadha Alrabaee, Saed United Arab Emirates University College of Information Technology Dept. of Computer and Network Engineering Dept. of Information Systems and Security AlAin United Arab Emirates

ISBN: (纸本)9798350303254

Responding to inefficiencies in traditional classroom management methods, this paper proposes an innovative, technology-driven approach to enhance educational processes. Central to our proposal is the development of a facial recognition system designed for efficient student attendance tracking, thus eliminating time-consuming roll calls. Additionally, we plan to introduce interactive whiteboard features to enrich classroom dynamics between students and faculty. However, our approach extends beyond mere attendance tracking and interactive learning. We aim to launch a Program Learning Outcomes (PLO) and Course Learning Outcomes (CLO) mapper. Leveraging Natural Language Processing (NLP) techniques, this tool will auto-align CLOs with PLOs, facilitating a more efficient curriculum development process. We also suggest implementing a feature powered by YOLOv5 to monitor and assess student attention in the classroom. Our comprehensive suite of tools is designed to equip educators with resources to refine their teaching strategies and boost student learning outcomes. By integrating facial recognition for attendance, interactive whiteboard features, NLP-based CLO/PLO mapping, and attention monitoring, we aspire to provide a robust solution enabling educators to adapt their teaching methods to students' unique needs. © 2023 IEEE.

关键词： Students

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NetMiner: Identifying Failure-Inducing Patterns in the Logs Generated by network Monitoring Software 14

NetMiner: Identifying Failure-Inducing Patterns in the Logs ...

引用

14th International Conference on Computing Communication and networking Technologies, ICCCNT 2023

作者： Sathi, Gowtham Reddy Vedullapalli, Lokesh Kishan, MacHarla Hemanth Zaman, Tarannum Shaila Islam, Md Tariqul Badr, Mahmoud M. Suny Polytechnic Institute Dept. of Computer and Information Science UticaNY United States SyracuseNY United States Suny Polytechnic Institute Dept. of Network and Computer Security UticaNY United States

ISBN: (纸本)9798350335095

To ensure uninterrupted network communication among multiple sources, the network administrator needs to do real-time monitoring of network logs. Due to its huge volume and streaming nature, it is almost impossible to do a manual analysis of the logs generated by different network monitoring software. Moreover, these logs may contain a limited amount of data regarding the identity, location, capacity, etc. of a user due to privacy issues. Therefore, we propose an automated system named NetMiner for performing log analysis that requires only insensitive data such as the network's current status and messages related to that status. We collect recent 30 days logs using the network monitoring tool PRTG (Paessler Router Traffic Grapher) and perform data analysis by leveraging a data mining technique called Fpmax. Our proposed technique NetMiner can identify patterns that may lead to failure and warn the network administrator about upcoming failures. Our experimental results show that NetMiner is both effective and efficient in identifying failure-inducing patterns in logs generated by network monitoring software. © 2023 IEEE.

关键词： Data mining

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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A study on the Application of Multilevel Secure Hash Function in Blockchain 2

A study on the Application of Multilevel Secure Hash Functio...

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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Jin, Shi-Mei Wang, Yi-Han Li, Yongzhen Yanbian University Information Technology Education Yanji133000 China Yanbian University Network Communication and Information Security Lab. Dept. of Computer Science and Technology Yanji133000 China

ISBN: (纸本)9781510663374

With the rapid development and upgrade of the social network and IT, information security has become increasingly significant. Hash function has become a research key-point in the field of information security. To meeting the requirements of various circumstances, this dissertation puts forward Multilevel security Hash Function (MSHF) and conduct a comparison experiment between MSHF and traditional methods to generate Merkle trees. The experimental results show that the Merkle tree generated based on MSHF has smaller dept., shorter path length, and shorter time used to find the wrong data. © 2023 SPIE.

关键词： Hash functions

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A Hypothesis Testing-based Framework for Cyber Deception with Sludging 60

A Hypothesis Testing-based Framework for Cyber Deception wit...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Kumar, Abhinav Brahma, Swastik Geng, Baocheng Kamhoua, Charles A. Varshney, Pramod K. University of Cincinnati Department of Computer Science OH United States University of Alabama Department of Computer Science BirminghamAL United States Network Security Branch DEVCOM Army Research Laboratory MD United States Syracuse University Dept. of Electrical Eng. and Computer Science NY United States

ISBN: (纸本)9798331541033

In this paper, we present a novel hypothesis testing framework to model an attacker's decision-making during the reconnaissance phase and employ the framework to design strategic deception strategies that can provide the attacker with optimally falsified information structures. We characterize the criterion for such structures to 'blind' the attacker, i.e., to completely confuse it, as well as the optimal falsification strategy when the attacker cannot be blinded. We also characterize optimal information acquisition costs that can be imposed on the attacker to maximally 'sludge' its decision-making process during the reconnaissance phase. Numerical results have been presented to gain important insights into the developed strategies. © 2024 IEEE.

关键词： Cyber attacks

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Towards an in-dept. Evaluation of the Performance, Suitability and Plausibility of Few-Shot Meta Transfer Learning on An Unknown Out-of-Distribution Cyber-attack Detection

Towards an in-Depth Evaluation of the Performance, Suitabili...

引用

2024 International Symposium on networks, computers and Communications, ISNCC 2024

作者： Ige, Tosin Kiekintveld, Christopher Piplai, Aritran Wagler, Amy Kolade, Olukunle Hafiz Matti, Bolanle The University of Texas Dept. of Computer Science El PasoTXUSA United States The University of Texas Dept. of Public Health Science El PasoTX United States United State Navy Office of Naval Research Pentagon United States Palo Alto Networks Inc Office of Network Security TX United States

ISBN: (纸本)9798350364910

The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample. In this research, we did an in-dept. evaluation of meta- learning to determine their plausibility and suitability for previously unknown cyberattack detection by first retrieving the original research artifacts of current state of the art meta learning to repeat the experiment with original dataset before replicating the experiment with two different malware dataset which had not been previously done with meta-transfer learning. On each of the experiments, meta-transfer learning gave good results on digital character recognition dataset but abysmal result on Malimg and Malevis malware images datasets thereby indicating its unreliability for detecting cyberattacks and the need for an improvement to the state-of-the-art meta transfer learning towards a better attack detection. Transfer learning performance is independent on imbalance and hence does not influence its performance since both malware dataset used for this experiment result in high validation loss and balancing the dataset doesn't result in reduced validation loss,the successful learning transfer seen on digital character recognition dataset is not unconnected to the fact that several languages have similar characters and digits thereby enhancing the successful learning transfer unlike malware datasets, and more importantly the finding that current meta-learning transfer approach doesn't generalize well on malware dataset and hence not suitable for detecting previously unseen out-of-distribution attack. © 2024 IEEE.

关键词： Zero-shot learning

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An in-dept. Investigation Into the Performance of State-of-the-Art Zero-Shot, Single-Shot, and Few-Shot Learning Approaches on an Out-of-Distribution Zero-Day Malware Attack Detection

An in-Depth Investigation Into the Performance of State-of-t...

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2024 International Symposium on networks, computers and Communications, ISNCC 2024

作者： Ige, Tosin Kiekintveld, Christopher Piplai, Aritran Wagler, Amy Kolade, Olukunle Hafiz Matti, Bolanle The University of Texas Dept. of Computer Science El PasoTX United States The University of Texas Dept. of Public Health Science El PasoTX United States Office of Naval Research United State Navy Pentagon United States Office of Network Security Palo Alto Networks Inc TX United States

ISBN: (纸本)9798350364910

N-shot learning has emerge in recent year as poten-tial learning approach to solve the problem of data scarcity by learning underlying pattern from a few training sample. Despite recent state-of-the-art research on model-agnostic metal learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample, there remains a big challenge on an actual out-of-distribution zero-day without any similarity to previously known malware family or new variant of an existing malware family. This ultimately questions the effectiveness of cur-rent state-of-the-art few-shot learning approach. In this research, we did an in-dept. investigation into the performance of state-of-the-art Zero-shot, Single-shot, and few-shot learning approaches on zero-day out-of-distribution malware attack detection based on their static properties using Malimg and Malevis mal ware dataset. We ensure our model was aware of an out-of-distribution class during training while varying the number of samples in the out-of-distribution class accordingly zero-shot(no sample), single-shot (1 sample), few-shot(S samples) while using confusion matrix to get the actual number of correct prediction on out-of-distribution mal ware validation samples. we assert that the model should be smart enough to detect and classify previously unseen data into an empty family as an out-of-distribution considering that the model was made to be aware of the existence of such distribution during training. Result shows 0, 0, and 3 correct out-of-distribution predictions on Zero-shot, single-shot, and few-shot experiments respectively, thereby showing limitation of the current state-of-the-art N-shot approaches on out-of-distribution attack. © 2024 IEEE.

关键词： Zero-shot learning

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Enhancing the Transportation of Students with Disabilities on Campus 3

Enhancing the Transportation of Students with Disabilities o...

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3rd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2023

作者： Ibrahim, Walid Alshamsi, Shaikha Alnuaimi, Salama Alneyadi, Alia Aldhaheri, Salama Alsalmi, Aysha Amer, Hoda Uae University Network and Comp. Eng. Dept. Al ain United Arab Emirates United Arab Emirates Univeristy Info. Systems & Security Dept. Al Ain United Arab Emirates United Arab Emirates Univeristy Computer Science Dept. Al Ain United Arab Emirates

ISBN: (纸本)9798350398366

Access to education has always been aconstitutional right provided by countries to their citizens. To fosterinclusion and equality, many countries have made tangible efforts recently toinclude students with disabilities in mainstream education. A recentministerial resolution by the UAE government directs education institutions tocater to the needs of students with disabilities. Moving comfortably, andefficiently on campus is a basic need for students to attend lectures, labsessions, and social activities in different buildings. However, this basicneed forms another major challenge for students with disabilities. Thischallenge is usually aggravated in gated campuses where private cars are notallowed. In such situations, students with disabilities rely on the specialtransportation service provided by the education institution to move on ***, this service is ad-hoc in nature and requires students to communicatewith the service center to book the service in advance. The service booking isdone manually, which reduces the utilization of the available vehicles, andincreases the students' waiting time and the possibility that the booking isrejected. This paper introduces a mobile application with an intelligentbooking system to automate the booking requests. The aim of the system isreducing the students' wait time and booking rejection rate while maximizingthe utilization of the available vehicles. © 2023 IEEE.

关键词： Students

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Comparative Evaluation of Machine Learning-Based Controller Area network Intrusion Detection Systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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