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Salama, Ramiz, Al-Turjman, Fadi.  2022.  AI in Blockchain Towards Realizing Cyber Security. 2022 International Conference on Artificial Intelligence in Everything (AIE). :471—475.
Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.
Pandey, Amit, Genale, Assefa Senbato, Janga, Vijaykumar, Sundaram, B. Barani, Awoke, Desalegn, Karthika, P..  2022.  Analysis of Efficient Network Security using Machine Learning in Convolutional Neural Network Methods. 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). :170—173.
Several excellent devices can communicate without the need for human intervention. It is one of the fastest-growing sectors in the history of computing, with an estimated 50 billion devices sold by the end of 2020. On the one hand, IoT developments play a crucial role in upgrading a few simple, intelligent applications that can increase living quality. On the other hand, the security concerns have been noted to the cross-cutting idea of frameworks and the multidisciplinary components connected with their organization. As a result, encryption, validation, access control, network security, and application security initiatives for gadgets and their inherent flaws cannot be implemented. It should upgrade existing security measures to ensure that the ML environment is sufficiently protected. Machine learning (ML) has advanced tremendously in the last few years. Machine insight has evolved from a research center curiosity to a sensible instrument in a few critical applications.
Lin, Wei.  2021.  Network Information Security Management in the Era of Big Data. 2021 2nd International Conference on Information Science and Education (ICISE-IE). :806—809.
With the advent of the era of big data, information technology has been rapidly developed and the application of computers has been popularized. However, network technology is a double-edged sword. While providing convenience, it also faces many problems, among which there are many hidden dangers of network information security. Based on this, based on the era background of big data, the network information security analysis, explore the main network security problems, and elaborate computer information network security matters needing attention, to strengthen the network security management, and put forward countermeasures, so as to improve the level of network security.
Boukela, Lynda, Zhang, Gongxuan, Yacoub, Meziane, Bouzefrane, Samia.  2021.  A near-autonomous and incremental intrusion detection system through active learning of known and unknown attacks. 2021 International Conference on Security, Pattern Analysis, and Cybernetics(SPAC). :374—379.
Intrusion detection is a traditional practice of security experts, however, there are several issues which still need to be tackled. Therefore, in this paper, after highlighting these issues, we present an architecture for a hybrid Intrusion Detection System (IDS) for an adaptive and incremental detection of both known and unknown attacks. The IDS is composed of supervised and unsupervised modules, namely, a Deep Neural Network (DNN) and the K-Nearest Neighbors (KNN) algorithm, respectively. The proposed system is near-autonomous since the intervention of the expert is minimized through the active learning (AL) approach. A query strategy for the labeling process is presented, it aims at teaching the supervised module to detect unknown attacks and improve the detection of the already-known attacks. This teaching is achieved through sliding windows (SW) in an incremental fashion where the DNN is retrained when the data is available over time, thus rendering the IDS adaptive to cope with the evolutionary aspect of the network traffic. A set of experiments was conducted on the CICIDS2017 dataset in order to evaluate the performance of the IDS, promising results were obtained.
Yanrong, Wen.  2021.  Research of the Innovative Integration of Artificial Intelligence and Vocational Education in the New Ecology of Education. 2021 2nd International Conference on Education, Knowledge and Information Management (ICEKIM). :468—473.
The development of artificial intelligence will certainly fundamentally change the pattern of human work. With the promotion of top-level strategies, vocational education can only develop sustainably by integrating with science and technology. Artificial intelligence is a branch of computer science that studies the basic theories, methods and techniques of how to apply computer hardware and software to simulate certain intelligent human behaviors. Artificial intelligence applied to vocational education mainly focuses on resource network technology and integrated distributed intelligent system, which organically integrates various different expert systems (ES), management information systems (MIS), intelligent networks, decision support systems (DSS), databases, numerical computing packages and graphics processing programs to solve complex problems. Artificial intelligence will certainly empower vocational education and give rise to a vocational education revolution. In the process of continuous improvement of AI, it is a more practical approach to apply various already mature AI technologies to vocational education practice. Establishing an intelligent vocational education ecology enables traditional education and AI to complement each other's advantages and jointly promote the healthy and sustainable development of vocational education ecology.
Yao, Jiaxin, Lin, Bihai, Huang, Ruiqi, Fan, Junyi, Chen, Biqiong, Liu, Yanhua.  2021.  Node Importance Evaluation Method for Cyberspace Security Risk Control. :127—131.
{With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.
Zhu, Jessica, Van Brummelen, Jessica.  2021.  Teaching Students About Conversational AI Using Convo, a Conversational Programming Agent. 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). :1–5.
Smart assistants, like Amazon's Alexa or Apple's Siri, have become commonplace in many people's lives, appearing in their phones and homes. Despite their ubiquity, these conversational AI agents still largely remain a mystery to many, in terms of how they work and what they can do. To lower the barrier to entry to understanding and creating these agents for young students, we expanded on Convo, a conversational programming agent that can respond to both voice and text inputs. The previous version of Convo focused on teaching only programming skills, so we created a simple, intuitive user interface for students to use those programming skills to train and create their own conversational AI agents. We also developed a curriculum to teach students about key concepts in AI and conversational AI in particular. We ran a 3-day workshop with 15 participating middle school students. Through the data collected from the pre- and post-workshop surveys as well as a mid-workshop brainstorming session, we found that after the workshop, students tended to think that conversational AI agents were less intelligent than originally perceived, gained confidence in their abilities to build these agents, and learned some key technical concepts about conversational AI as a whole. Based on these results, we are optimistic about CONVO'S ability to teach and empower students to develop conversational AI agents in an intuitive way.
Wang, Junchao, Pang, Jianmin, Shan, Zheng, Wei, Jin, Yao, Jinyang, Liu, Fudong.  2021.  A Software Diversity-Based Lab in Operating System for Cyber Security Students. 2021 IEEE 3rd International Conference on Computer Science and Educational Informatization (CSEI). :296—299.
The course of operating system's labs usually fall behind the state of art technology. In this paper, we propose a Software Diversity-Assisted Defense (SDAD) lab based on software diversity, mainly targeting for students majoring in cyber security and computer science. This lab is consisted of multiple modules and covers most of the important concepts and principles in operating systems. Thus, the knowledge learned from the theoretical course will be deepened with the lab. For students majoring in cyber security, they can learn this new software diversity-based defense technology and understand how an exploit works from the attacker's side. The experiment is also quite stretchable, which can fit all level students.
Mittal, Sonam, Kaur, Prabhjot, Ramkumar, K.R..  2021.  Achieving Privacy and Security Using QR-Code through Homomorphic Encryption and Steganography. 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). :1–6.
Security is a most concerning matter for client's data in today's emerging technological world in each field, like banking, management, retail, shopping, communication, education, etc. Arise in cyber-crime due to the black hat community, there is always a need for a better way to secure the client's sensitive information, Security is the key point in online banking as the threat of unapproved online access to a client's data is very significant as it ultimately danger to bank reputation. The more secure and powerful methods can allow a client to work with untrusted parties. Paper is focusing on how secure banking transaction system can work by using homomorphic encryption and steganography techniques. For data encryption NTRU, homomorphic encryption can be used and to hide details through the QR code, a cover image can be embed using steganography techniques.
Wang, Tianma, Zhao, Dongmei, Zheng, Le.  2021.  Information Protection of International Students Based on Network Security. 2021 International Conference on Computer Network, Electronic and Automation (ICCNEA). :172—176.
With China's overall national strength, the education of studying in China has entered a period of rapid development, and China has become one of the important destination countries for international student mobility. With political stability, rapid economic development, and continuous improvement in the quality of higher education, the educational value of studying in China is increasingly recognized by international students. International students study and live in the same way as domestic students. While the development of the Internet has brought convenience to people, it has also created many security risks. How to protect the information security of international students is the focus of this paper. This paper introduces the classification, characteristics and security risks of international students' personal information. In order to protect the private data of international students from being leaked, filtering rules are set in the campus network through WinRoute firewall to effectively prevent information from being leaked, tampered or deleted, which can be used for reference by other universities.
Sun, Lanxin, Dai, JunBo, Shen, Xunbing.  2021.  Facial emotion recognition based on LDA and Facial Landmark Detection. 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE). :64—67.
Emotion recognition in the field of human-computer interaction refers to that the computer has the corresponding perceptual ability to predict the emotional state of human beings in advance by observing human expressions, behaviors and emotions, so as to ensure that computers can communicate emotionally with humans. The main research work of this paper is to extract facial image features by using Linear Discriminant Analysis (LDA) and Facial Landmark Detection after grayscale processing and cropping, and then compare the accuracy after emotion recognition and classification to determine which feature extraction method is more effective. The test results show that the accuracy rate of emotion recognition in face images can reach 73.9% by using LDA method, and 84.5% by using Facial Landmark Detection method. Therefore, facial landmarks can be used to identify emotion in face images more accurately.
Cao, HongYuan, Qi, Chao.  2021.  Facial Expression Study Based on 3D Facial Emotion Recognition. 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS). :375—381.
Teaching evaluation is an indispensable key link in the modern education model. Its purpose is to promote learners' cognitive and non-cognitive development, especially emotional development. However, today's education has increasingly neglected the emotional process of learners' learning. Therefore, a method of using machines to analyze the emotional changes of learners during learning has been proposed. At present, most of the existing emotion recognition algorithms use the extraction of two-dimensional facial features from images to perform emotion prediction. Through research, it is found that the recognition rate of 2D facial feature extraction is not optimal, so this paper proposes an effective the algorithm obtains a single two-dimensional image from the input end and constructs a three-dimensional face model from the output end, thereby using 3D facial information to estimate the continuous emotion of the dimensional space and applying this method to an online learning system. Experimental results show that the algorithm has strong robustness and recognition ability.
Qureshi, Hifza, Sagar, Anil Kumar, Astya, Rani, Shrivastava, Gulshan.  2021.  Big Data Analytics for Smart Education. 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA). :650–658.
The existing education system, which incorporates school assessments, has some flaws. Conventional teaching methods give students no immediate feedback, also make teachers to spend hours grading repetitive assignments, and aren't very constructive in showing students how to improve in their academics, and also fail to take advantage of digital opportunities that can improve learning outcomes. In addition, since a single teacher has to manage a class of students, it gets difficult to focus on each and every student in the class. Furthermore, with the help of a management system for better learning, educational organizations can now implement administrative analytics and execute new business intelligence using big data. This data visualization aids in the evaluation of teaching, management, and study success metrics. In this paper, there is put forward a discussion on how Data Mining and Data Analytics can help make the experience of learning and teaching both, easier and accountable. There will also be discussion on how the education organization has undergone numerous challenges in terms of effective and efficient teachings, student-performance. In addition development, and inadequate data storage, processing, and analysis will also be discussed. The research implements Python programming language on big education data. In addition, the research adopted an exploratory research design to identify the complexities and requirements of big data in the education field.
Chang, Xinyu, Wu, Bian.  2021.  Effects of Immersive Spherical Video-based Virtual Reality on Cognition and Affect Outcomes of Learning: A Meta-analysis. 2021 International Conference on Advanced Learning Technologies (ICALT). :389–391.
With the advancement of portable head-mounted displays, interest in educational application of immersive spherical video-based virtual reality (SVVR) has been emerging. However, it remains unclear regarding the effects of immersive SVVR on cognitive and affective outcomes. In this study, we retrieved 58 learning outcomes from 16 studies. A meta-analysis was performed using the random effects model to calculate the effect size. Several important moderators were also examined such as control group treatment, learning outcome type, interaction functionality, content instruction, learning domain, and learner's stage. The results show that immersive SVVR is more effective than other instructional conditions with a medium effect size. The key findings of the moderator analysis are that immersive SVVR has a greater impact on affective outcomes, as well as under the conditions that learning system provides interaction functionality or integrates with content instruction before virtual exploratory learning.
Guo, Siyao, Fu, Yi.  2021.  Construction of immersive scene roaming system of exhibition hall based on virtual reality technology. 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). :1029–1033.
On the basis of analyzing the development and application of virtual reality (VR) technology at home and abroad, and combining with the specific situation of the exhibition hall, this paper establishes an immersive scene roaming system of the exhibition hall. The system is completed by virtual scene modeling technology and virtual roaming interactive technology. The former uses modeling software to establish the basic model in the virtual scene, while the latter uses VR software to enable users to control their own roles to run smoothly in the roaming scene. In interactive roaming, this paper optimizes the A* pathfinding algorithm, uses binary heap to process data, and on this basis, further optimizes the pathfinding algorithm, so that when the pathfinding target is an obstacle, the pathfinder can reach the nearest place to the obstacle. Texture mapping technology, LOD technology and other related technologies are adopted in the modeling, thus finally realizing the immersive scene roaming system of the exhibition hall.
Li, Xiaojian, Chen, Jinsong.  2021.  Research on the Influence Mechanism of Artificial Intelligence on Lateral Channel Spillover Effect. 2021 International Conference on Internet, Education and Information Technology (IEIT). :90–93.

With big data and artificial intelligence, we conduct the research of the buyers' identification and involvement, and their investments such as time, experience and consultation in various channels are analyzed and iterated. We establish a set of AI channel governance system with the functions of members' behavior monitoring, transaction clearing and deterrence; Through the system, the horizontal spillover effect of their behavior is controlled. Thus, their unfair perception can be effectively reduced and the channel performance can be improved as well.

Nawaz, Alia, Naeem, Tariq, Tayyab, Muhammad.  2021.  Application Profiling From Encrypted Traffic. 2021 International Conference on Cyber Warfare and Security (ICCWS). :1–7.
Everyday millions of people use Internet for various purposes including information access, communication, business, education, entertainment and more. As a result, huge amount of information is exchanged between billions of connected devices. This information can be encapsulated in different types of data packets. This information is also referred to as network traffic. The traffic analysis is a challenging task when the traffic is encrypted and the contents are not readable. So complex algorithms required to deduce the information and form patterns for traffic analysis. Many of currently available techniques rely on application specific attribute analysis, deep packet inspection (DPI) or content-based analysis that become ineffective on encrypted traffic. The article will focused on analysis techniques for encrypted traffic that are adaptive to address the evolving nature and increasing volume of network traffic. The proposed solution solution is less dependent on application and protocol specific parameters so that it can adapt to new types of applications and protocols. Our results shows that processing required for traffic analysis need to be in acceptable limits to ensure applicability in real-time applications without compromising performance.
Kalai Chelvi, T., Ramapraba, P. S., Sathya Priya, M., Vimala, S., Shobarani, R., Jeshwanth, N L, Babisha, A..  2021.  A Web Application for Prevention of Inference Attacks using Crowd Sourcing in Social Networks. 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC). :328—332.
Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.
Qin, Shuangling, Xu, Chaozhi, Zhang, Fang, Jiang, Tao, Ge, Wei, Li, Jihong.  2021.  Research on Application of Chinese Natural Language Processing in Constructing Knowledge Graph of Chronic Diseases. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). :271—274.
Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.
ALSaleem, Bandar Omar, Alshoshan, Abdullah I..  2021.  Multi-Factor Authentication to Systems Login. 2021 National Computing Colleges Conference (NCCC). :1–4,.
Multi-Factor Authentication is an electronic authentication method in which a computer user is granted access to an application or a website only after successfully presenting two or more factors, or pieces of evidence. It is the first step to protect systems against intruders since the traditional log-in methods (username and password) are not completely protected from hackers, since they can guess them easily using tools. Current Systems use additional methods to increase security, such as using two-factor authentication based on a one-time password via mobile or email, or authentication based on biometrics (fingerprint, eye iris or retina, and face recognition) or via token devices. However, these methods require additional hardware equipment with high cost at the level of small and medium companies. This paper proposes a multi-factor authentication system that combines ease of use and low-cost factors. The system does not need any special settings or infrastructure. It relies on graphical passwords, so the user, in registration phase, chooses three images and memorizes them. In the login phase, the user needs only to choose the correct images that he considered during the registration process in a specific order. The proposed system overcomes many different security threats, such as key-loggers, screen capture attack or shoulder surfing. The proposed method was applied to 170 participants, 75% of them are males and 25% are females, classified according to their age, education level, web experience. One-third of them did not have sufficient knowledge about various security threats.
Jia, Yunsong.  2021.  Design of nearest neighbor search for dynamic interaction points. 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE). :389—393.
This article describes the definition, theoretical derivation, design ideas, and specific implementation of the nearest query algorithm for the acceleration of probabilistic optimization at first, and secondly gives an optimization conclusion that is generally applicable to high-dimensional Minkowski spaces with even-numbered feature parameters. Thirdly the operating efficiency and space sensitivity of this algorithm and the commonly used algorithms are compared from both theoretical and experimental aspects. Finally, the optimization direction is analyzed based on the results.
Shukla, Mukul, Joshi, Brijendra Kumar.  2021.  A Trust Based Approach to Mitigate Wormhole Attacks in Mobile Adhoc Networks. 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). :776–782.
MANET stands for Mobile ad-hoc network, which is also known as a wireless network. It provides a routable networking environment which does not have a centralized infrastructure. MANET is used in many important sectors like economic sector (corporate field), security sector (military field), education sector (video conferences and lectures), law sector (law enforcement) and many more. Even though it plays a vital role in different sectors and improves its economic growth, security is a major concern in MANET. Due to lack of inbuilt security, several attacks like data traffic attack, control traffic attack. The wormhole is a kind of control traffic attack which forms wormhole link between nodes. In this paper, we have proposed an approach to detect and get rid of the wormhole attack. The proposed approach is based on trust values, which will decide whether nodes are affected by using parameters like receiving time and data rate. On evaluation, we have concluded that the wormhole attack decreases the network's performance while using trusted approach its value increases. Means PDR and throughput return best results for the affected network while in case of end to end delay it returns similar results as of unaffected network.
Yifan, Zhao.  2021.  Application of Machine Learning in Network Security Situational Awareness. 2021 World Conference on Computing and Communication Technologies (WCCCT). :39–46.
Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed.
Contașel, Cristian, Trancă, Dumitru-Cristian, Pălăcean, Alexandru-Viorel.  2021.  Cloud based mobile application security enforcement using device attestation API. 2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet). :1–5.
Today the mobile devices are more and more present in our lives, and the mobile applications market has experienced a sharp growth. Most of these applications are made to make our daily lives easier, and for this a large part of them consume various web services. Given this transition, from desktop and web applications to mobile applications, many critical services have begun to expose their APIs for use by such application clients. Unfortunately, this transition has paved the way for new vulnerabilities, vulnerabilities used to compress cloud services. In this article we analyzed the main security problems and how they can be solved using the attestation services, the services that indicate that the device running the application and the client application are genuine.
Wynn, Nathan, Johnsen, Kyle, Gonzalez, Nick.  2021.  Deepfake Portraits in Augmented Reality for Museum Exhibits. 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). :513—514.
In a collaboration with the Georgia Peanut Commission’s Education Center and museum in Georgia, USA, we developed an augmented reality app to guide visitors through the museum and offer immersive educational information about the artifacts, exhibits, and artwork displayed therein. Notably, our augmented reality system applies the First Order Motion Model for Image Animation to several portraits of individuals influential to the Georgia peanut industry to provide immersive animated narration and monologue regarding their contributions to the peanut industry. [4]