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Li, Zeyi, Wang, Yun, Wang, Pan, Su, Haorui.  2021.  PGAN:A Generative Adversarial Network based Anomaly Detection Method for Network Intrusion Detection System. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :734—741.
With the rapid development of communication net-work, the types and quantities of network traffic data have in-creased substantially. What followed was the frequent occurrence of versatile cyber attacks. As an important part of network security, the network-based intrusion detection system (NIDS) can monitor and protect the network equippments and terminals in real time. The traditional detection methods based on deep learning (DL) are always in supervised manners in NIDS, which can automatically build end-to-end detection model without man-ual feature extraction and selection by domain experts. However, supervised learning methods require large-scale labeled data, yet capturing large labeled datasets is a very cubersome, tedious and time-consuming manual task. Instead, unsupervised learning is an effective way to overcome this problem. Nonetheless, the ex-isting unsupervised methods are prone to low detection efficiency and are difficult to train. In this paper we propose a novel NIDS method called PGAN based on generative adversarial network (GAN) to detect the abnormal traffic from the perspective of Anomaly Detection, which leverage the competitive speciality of adversarial training to learn the normal traffic. Based on the public dataset CICIDS2017, three experimental results show that PGAN can significantly outperform other unsupervised methods like stacked autoencoder (SAE) and isolation forest (IF).
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.
Chen, Tong, Xiang, Yingxiao, Li, Yike, Tian, Yunzhe, Tong, Endong, Niu, Wenjia, Liu, Jiqiang, Li, Gang, Alfred Chen, Qi.  2021.  Protecting Reward Function of Reinforcement Learning via Minimal and Non-catastrophic Adversarial Trajectory. 2021 40th International Symposium on Reliable Distributed Systems (SRDS). :299—309.
Reward functions are critical hyperparameters with commercial values for individual or distributed reinforcement learning (RL), as slightly different reward functions result in significantly different performance. However, existing inverse reinforcement learning (IRL) methods can be utilized to approximate reward functions just based on collected expert trajectories through observing. Thus, in the real RL process, how to generate a polluted trajectory and perform an adversarial attack on IRL for protecting reward functions has become the key issue. Meanwhile, considering the actual RL cost, generated adversarial trajectories should be minimal and non-catastrophic for ensuring normal RL performance. In this work, we propose a novel approach to craft adversarial trajectories disguised as expert ones, for decreasing the IRL performance and realize the anti-IRL ability. Firstly, we design a reward clustering-based metric to integrate both advantages of fine- and coarse-grained IRL assessment, including expected value difference (EVD) and mean reward loss (MRL). Further, based on such metric, we explore an adversarial attack based on agglomerative nesting algorithm (AGNES) clustering and determine targeted states as starting states for reward perturbation. Then we employ the intrinsic fear model to predict the probability of imminent catastrophe, supporting to generate non-catastrophic adversarial trajectories. Extensive experiments of 7 state-of-the-art IRL algorithms are implemented on the Object World benchmark, demonstrating the capability of our proposed approach in (a) decreasing the IRL performance and (b) having minimal and non-catastrophic adversarial trajectories.
Herwanto, Guntur Budi, Quirchmayr, Gerald, Tjoa, A Min.  2021.  A Named Entity Recognition Based Approach for Privacy Requirements Engineering. 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). :406—411.
The presence of experts, such as a data protection officer (DPO) and a privacy engineer is essential in Privacy Requirements Engineering. This task is carried out in various forms including threat modeling and privacy impact assessment. The knowledge required for performing privacy threat modeling can be a serious challenge for a novice privacy engineer. We aim to bridge this gap by developing an automated approach via machine learning that is able to detect privacy-related entities in the user stories. The relevant entities include (1) the Data Subject, (2) the Processing, and (3) the Personal Data entities. We use a state-of-the-art Named Entity Recognition (NER) model along with contextual embedding techniques. We argue that an automated approach can assist agile teams in performing privacy requirements engineering techniques such as threat modeling, which requires a holistic understanding of how personally identifiable information is used in a system. In comparison to other domain-specific NER models, our approach achieves a reasonably good performance in terms of precision and recall.
Abuah, Chike, Silence, Alex, Darais, David, Near, Joseph P..  2021.  DDUO: General-Purpose Dynamic Analysis for Differential Privacy. 2021 IEEE 34th Computer Security Foundations Symposium (CSF). :1—15.
Differential privacy enables general statistical analysis of data with formal guarantees of privacy protection at the individual level. Tools that assist data analysts with utilizing differential privacy have frequently taken the form of programming languages and libraries. However, many existing programming languages designed for compositional verification of differential privacy impose significant burden on the programmer (in the form of complex type annotations). Supplementary library support for privacy analysis built on top of existing general-purpose languages has been more usable, but incapable of pervasive end-to-end enforcement of sensitivity analysis and privacy composition. We introduce DDuo, a dynamic analysis for enforcing differential privacy. DDuo is usable by non-experts: its analysis is automatic and it requires no additional type annotations. DDuo can be implemented as a library for existing programming languages; we present a reference implementation in Python which features moderate runtime overheads on realistic workloads. We include support for several data types, distance metrics and operations which are commonly used in modern machine learning programs. We also provide initial support for tracking the sensitivity of data transformations in popular Python libraries for data analysis. We formalize the novel core of the DDuo system and prove it sound for sensitivity analysis via a logical relation for metric preservation. We also illustrate DDuo's usability and flexibility through various case studies which implement state-of-the-art machine learning algorithms.
Wang, Zisen, Liang, Ying, Xie, Xiaojie, Liu, Zhengjun.  2021.  Privacy Protection Method for Experts' Evaluation Ability Calculation of Peer Review. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). :611—615.
Most of the existing calculation method of expert evaluation ability directly call data onto calculation, which leads to the risk of privacy leakage of expert review information and affects the peer review environment. With regard to this problem, a privacy protection method of experts' evaluation ability calculation of peer review is proposed. Privacy protection and data usability are adjusted according to privacy preferences. Using Gauss distribution and combining with the distributive law of real evaluation data, the virtual projects are generated, and the project data are anonymized according to the virtual projects. Laplace distribution is used to add noise to the evaluation sub score for perturbation, and the evaluation data are obfuscation according to the perturbation sub score. Based on the protected project data and evaluation data, the expert evaluation ability is calculated, and the review privacy is protected. The experimental results show that the proposed method can effectively balance the privacy protection and the accuracy of the calculation results.
Øye, Marius Mølnvik, Yang, Bian.  2021.  Privacy Modelling in Contact Tracing. 2021 International Conference on Computational Science and Computational Intelligence (CSCI). :1279—1282.
Contact tracing is a particularly important part of health care and is often overlooked or forgotten up until right when it is needed the most. With the wave of technological achievements in the last decade, a digital perspective for aid in contact tracing was a natural development from traditional contact tracing. When COVID-19 was categorized as a pandemic, the need for modernized contact tracing solutions became apparent, and highly sought after. Solutions using the Bluetooth protocol and/or Global Positioning System data (GPS) were hastily made available to the public in nations all over the world. These solutions quickly became criticized by privacy experts as being potential tools for tracking.
Tan, Mingtian, Wan, Junpeng, Zhou, Zhe, Li, Zhou.  2021.  Invisible Probe: Timing Attacks with PCIe Congestion Side-channel. 2021 IEEE Symposium on Security and Privacy (SP). :322—338.
PCIe (Peripheral Component Interconnect express) protocol is the de facto protocol to bridge CPU and peripheral devices like GPU, NIC, and SSD drive. There is an increasing demand to install more peripheral devices on a single machine, but the PCIe interfaces offered by Intel CPUs are fixed. To resolve such contention, PCIe switch, PCH (Platform Controller Hub), or virtualization cards are installed on the machine to allow multiple devices to share a PCIe interface. Congestion happens when the collective PCIe traffic from the devices overwhelm the PCIe link capacity, and transmission delay is then introduced.In this work, we found the PCIe delay not only harms device performance but also leaks sensitive information about a user who uses the machine. In particular, as user’s activities might trigger data movement over PCIe (e.g., between CPU and GPU), by measuring PCIe congestion, an adversary accessing another device can infer the victim’s secret indirectly. Therefore, the delay resulted from I/O congestion can be exploited as a side-channel. We demonstrate the threat from PCIe congestion through 2 attack scenarios and 4 victim settings. Specifically, an attacker can learn the workload of a GPU in a remote server by probing a RDMA NIC that shares the same PCIe switch and measuring the delays. Based on the measurement, the attacker is able to know the keystroke timings of the victim, what webpage is rendered on the GPU, and what machine-learning model is running on the GPU. Besides, when the victim is using a low-speed device, e.g., an Ethernet NIC, an attacker controlling an NVMe SSD can launch a similar attack when they share a PCH or virtualization card. The evaluation result shows our attack can achieve high accuracy (e.g., 96.31% accuracy in inferring webpage visited by a victim).
Cheng, Jie, Zhang, Kun, Tu, Bibo.  2021.  Remote Attestation of Large-scale Virtual Machines in the Cloud Data Center. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :180—187.
With the development of cloud computing, remote attestation of virtual machines has received extensive attention. However, the current schemes mainly concentrate on the single prover, and the attestation of a large-scale virtualization environment will cause TPM bottleneck and network congestion, resulting in low efficiency of attestation. This paper proposes CloudTA, an extensible remote attestation architecture. CloudTA groups all virtual machines on each cloud server and introduces an integrity measurement group (IMG) to measure virtual machines and generate trusted evidence by a group. Subsequently, the cloud server reports the physical platform and VM group's trusted evidence for group verification, reducing latency and improving efficiency. Besides, CloudTA designs a hybrid high concurrency communication framework for supporting remote attestation of large-scale virtual machines by combining active requests and periodic reports. The evaluation results suggest that CloudTA has good efficiency and scalability and can support remote attestation of ten thousand virtual machines.
Liu, Xu, Fang, Dongxu, Xu, Peng.  2021.  Automated Performance Benchmarking Platform of IaaS Cloud. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1402—1405.
With the rapid development of cloud computing, IaaS (Infrastructure as a Service) becomes more and more popular. IaaS customers may not clearly know the actual performance of each cloud platform. Moreover, there are no unified standards in performance evaluation of IaaS VMs (virtual machine). The underlying virtualization technology of IaaS cloud is transparent to customers. In this paper, we will design an automated performance benchmarking platform which can automatically install, configure and execute each benchmarking tool with a configuration center. This platform can easily visualize multidimensional benchmarking parameters data of each IaaS cloud platform. We also rented four IaaS VMs from AliCloud-Beijing, AliCloud-Qingdao, UCloud and Huawei to validate our benchmarking system. Performance comparisons of multiple parameters between multiple platforms were shown in this paper. However, in practice, customers' applications running on VMs are often complex. Performance of complex applications may not depend on single benchmarking parameter (e.g. CPU, memory, disk I/O etc.). We ran a TPC-C test for example to get overall performance in MySQL application scenario. The effects of different benchmarking parameters differ in this specific scenario.
Fu, Zhihan, Fan, Qilin, Zhang, Xu, Li, Xiuhua, Wang, Sen, Wang, Yueyang.  2021.  Policy Network Assisted Monte Carlo Tree Search for Intelligent Service Function Chain Deployment. 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1161—1168.
Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.
Khadhim, Ban Jawad, Kadhim, Qusay Kanaan, Khudhair, Wijdan Mahmood, Ghaidan, Marwa Hameed.  2021.  Virtualization in Mobile Cloud Computing for Augmented Reality Challenges. 2021 2nd Information Technology To Enhance e-learning and Other Application (IT-ELA). :113—118.
Mobile cloud computing has suggested as a viable technology as a result of the fast growth of mobile applications and the emergence of the cloud computing idea. Mobile cloud computing incorporates cloud computing into the mobile environment and addresses challenges in mobile cloud computing applications like (processing capacity, battery storage capacity, privacy, and security). We discuss the enabling technologies and obstacles that we will face when we transition from mobile computing to mobile cloud computing to develop next-generation mobile cloud applications. This paper provides an overview of the processes and open concerns for mobility in mobile cloud computing for augmented reality service provisioning. This paper outlines the concept, system architecture, and taxonomy of virtualization technology, as well as research concerns related to virtualization security, and suggests future study fields. Furthermore, we highlight open challenges to provide light on the future of mobile cloud computing and future development.
Wilke, Luca, Wichelmann, Jan, Sieck, Florian, Eisenbarth, Thomas.  2021.  undeSErVed trust: Exploiting Permutation-Agnostic Remote Attestation. 2021 IEEE Security and Privacy Workshops (SPW). :456—466.
The ongoing trend of moving data and computation to the cloud is met with concerns regarding privacy and protection of intellectual property. Cloud Service Providers (CSP) must be fully trusted to not tamper with or disclose processed data, hampering adoption of cloud services for many sensitive or critical applications. As a result, CSPs and CPU manufacturers are rushing to find solutions for secure and trustworthy outsourced computation in the Cloud. While enclaves, like Intel SGX, are strongly limited in terms of throughput and size, AMD’s Secure Encrypted Virtualization (SEV) offers hardware support for transparently protecting code and data of entire VMs, thus removing the performance, memory and software adaption barriers of enclaves. Through attestation of boot code integrity and means for securely transferring secrets into an encrypted VM, CSPs are effectively removed from the list of trusted entities. There have been several attacks on the security of SEV, by abusing I/O channels to encrypt and decrypt data, or by moving encrypted code blocks at runtime. Yet, none of these attacks have targeted the attestation protocol, the core of the secure computing environment created by SEV. We show that the current attestation mechanism of Zen 1 and Zen 2 architectures has a significant flaw, allowing us to manipulate the loaded code without affecting the attestation outcome. An attacker may abuse this weakness to inject arbitrary code at startup–and thus take control over the entire VM execution, without any indication to the VM’s owner. Our attack primitives allow the attacker to do extensive modifications to the bootloader and the operating system, like injecting spy code or extracting secret data. We present a full end-to-end attack, from the initial exploit to leaking the key of the encrypted disk image during boot, giving the attacker unthrottled access to all of the VM’s persistent data.
Raafat, Maryam A., El-Wakil, Rania Abdel-Fattah, Atia, Ayman.  2021.  Comparative study for Stylometric analysis techniques for authorship attribution. 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). :176—181.
A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.
Gonçalves, Luís, Vimieiro, Renato.  2021.  Approaching authorship attribution as a multi-view supervised learning task. 2021 International Joint Conference on Neural Networks (IJCNN). :1—8.
Authorship attribution is the problem of identifying the author of texts based on the author's writing style. It is usually assumed that the writing style contains traits inaccessible to conscious manipulation and can thus be safely used to identify the author of a text. Several style markers have been proposed in the literature, nevertheless, there is still no consensus on which best represent the choices of authors. Here we assume an agnostic viewpoint on the dispute for the best set of features that represents an author's writing style. We rather investigate how different sources of information may unveil different aspects of an author's style, complementing each other to improve the overall process of authorship attribution. For this we model authorship attribution as a multi-view learning task. We assess the effectiveness of our proposal applying it to a set of well-studied corpora. We compare the performance of our proposal to the state-of-the-art approaches for authorship attribution. We thoroughly analyze how the multi-view approach improves on methods that use a single data source. We confirm that our approach improves both in accuracy and consistency of the methods and discuss how these improvements are beneficial for linguists and domain specialists.
Saini, Anu, Sri, Manepalli Ratna, Thakur, Mansi.  2021.  Intrinsic Plagiarism Detection System Using Stylometric Features and DBSCAN. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). :13—18.
Plagiarism is the act of using someone else’s words or ideas without giving them due credit and representing it as one’s own work. In today's world, it is very easy to plagiarize others' work due to advancement in technology, especially by the use of the Internet or other offline sources such as books or magazines. Plagiarism can be classified into two broad categories on the basis of detection namely extrinsic and intrinsic plagiarism. Extrinsic plagiarism detection refers to detecting plagiarism in a document by comparing it against a given reference dataset, whereas, Intrinsic plagiarism detection refers to detecting plagiarism with the help of variation in writing styles without using any reference corpus. Although there are many approaches which can be adopted to detect extrinsic plagiarism, few are available for intrinsic plagiarism detection. In this paper, a simplified approach is proposed for developing an intrinsic plagiarism detector which is helpful in detecting plagiarism even when no reference corpus is available. The approach deals with development of an intrinsic plagiarism detection system by identifying the writing style of authors in the document using stylometric features and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The proposed system has an easy to use interactive interface where user has to upload a text document to be checked for plagiarism and the result is displayed on the web page itself. In addition, the user can also see the analysis of the document in the form of graphs.
Cardaioli, Matteo, Conti, Mauro, Sorbo, Andrea Di, Fabrizio, Enrico, Laudanna, Sonia, Visaggio, Corrado A..  2021.  It’s a Matter of Style: Detecting Social Bots through Writing Style Consistency. 2021 International Conference on Computer Communications and Networks (ICCCN). :1—9.
Social bots are computer algorithms able to produce content and interact with other users on social media autonomously, trying to emulate and possibly influence humans’ behavior. Indeed, bots are largely employed for malicious purposes, like spreading disinformation and conditioning electoral campaigns. Nowadays, bots’ capability of emulating human behaviors has become increasingly sophisticated, making their detection harder. In this paper, we aim at recognizing bot-driven accounts by evaluating the consistency of users’ writing style over time. In particular, we leverage the intuition that while bots compose posts according to fairly deterministic processes, humans are influenced by subjective factors (e.g., emotions) that can alter their writing style. To verify this assumption, by using stylistic consistency indicators, we characterize the writing style of more than 12,000 among bot-driven and human-operated Twitter accounts and find that statistically significant differences can be observed between the different types of users. Thus, we evaluate the effectiveness of different machine learning (ML) algorithms based on stylistic consistency features in discerning between human-operated and bot-driven Twitter accounts and show that the experimented ML algorithms can achieve high performance (i.e., F-measure values up to 98%) in social bot detection tasks.
White, Riley, Sprague, Nathan.  2021.  Deep Metric Learning for Code Authorship Attribution and Verification. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). :1089—1093.
Code authorship identification can assist in identifying creators of malware, identifying plagiarism, and giving insights in copyright infringement cases. Taking inspiration from facial recognition work, we apply recent advances in metric learning to the problem of authorship identification and verification. The metric learning approach makes it possible to measure similarity in the learned embedding space. Access to a discriminative similarity measure allows for the estimation of probability distributions that facilitate open-set classification and verification. We extend our analysis to verification based on sets of files, a previously unexplored problem domain in large-scale author identification. On closed-set tasks we achieve competitive accuracies, but do not improve on the state of the art.
Khan, Aazar Imran, Jain, Samyak, Sharma, Purushottam, Deep, Vikas, Mehrotra, Deepti.  2021.  Stylometric Analysis of Writing Patterns Using Artificial Neural Networks. 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT). :29—35.
Plagiarism checkers have been widely used to verify the authenticity of dissertation/project submissions. However, when non-verbatim plagiarism or online examinations are considered, this practice is not the best solution. In this work, we propose a better authentication system for online examinations that analyses the submitted text's stylometry for a match of writing pattern of the author by whom the text was submitted. The writing pattern is analyzed over many indicators (i.e., features of one's writing style). This model extracts 27 such features and stores them as the writing pattern of an individual. Stylometric Analysis is a better approach to verify a document's authorship as it doesn't check for plagiarism, but verifies if the document was written by a particular individual and hence completely shuts down the possibility of using text-convertors or translators. This paper also includes a brief comparative analysis of some simpler algorithms for the same problem statement. These algorithms yield results that vary in precision and accuracy and hence plotting a conclusion from the comparison shows that the best bet to tackle this problem is through Artificial Neural Networks.
Muldoon, Connagh, Ikram, Ahsan, Khan Mirza, Qublai Ali.  2021.  Modern Stylometry: A Review & Experimentation with Machine Learning. 2021 8th International Conference on Future Internet of Things and Cloud (FiCloud). :293—298.
The problem of authorship attribution has applications from literary studies (such as the great Shakespeare/Marlowe debates) to counter-intelligence. The field of stylometry aims to offer quantitative results for authorship attribution. In this paper, we present a combination of stylometric techniques using machine learning. An implementation of the system is used to analyse chat logs and attempts to construct a stylometric model for users within the presented chat system. This allows for the authorship attribution of other works they may write under different names or within different communication systems. This implementation demonstrates accuracy of up to 84 % across the dataset, a full 34 % increase against a random-choice control baseline.
Frankel, Sophia F., Ghosh, Krishnendu.  2021.  Machine Learning Approaches for Authorship Attribution using Source Code Stylometry. 2021 IEEE International Conference on Big Data (Big Data). :3298—3304.
Identification of source code authorship is vital for attribution. In this work, a machine learning framework is described to identify source code authorship. The framework integrates the features extracted using natural language processing based approaches and abstract syntax tree of the code. We evaluate the methodology on Google Code Jam dataset. We present the performance measures of the logistic regression and deep learning on the dataset.
Teodorescu, Horia-Nicolai.  2021.  Applying Chemical Linguistics and Stylometry for Deriving an Author’s Scientific Profile. 2021 International Symposium on Signals, Circuits and Systems (ISSCS). :1—4.
The study exercises computational linguistics, specifically chemical linguistics methods for profiling an author. We analyze the vocabulary and the style of the titles of the most visible works of Cristofor I. Simionescu, an internationally well-known chemist, for detecting specific patterns of his research interests and methods. Somewhat surprisingly, while the tools used are elementary and there is only a small number of words in the analysis, some interesting details emerged about the work of the analyzed personality. Some of these aspects were confirmed by experts in the field. We believe this is the first study aiming to author profiling in chemical linguistics, moreover the first to question the usefulness of Google Scholar for author profiling.
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.
Xia, Hongbing, Bao, Jinzhou, Guo, Ping.  2021.  Asymptotically Stable Fault Tolerant Control for Nonlinear Systems Through Differential Game Theory. 2021 17th International Conference on Computational Intelligence and Security (CIS). :262—266.
This paper investigates an asymptotically stable fault tolerant control (FTC) method for nonlinear continuous-time systems (NCTS) with actuator failures via differential game theory (DGT). Based on DGT, the FTC problem can be regarded as a two-player differential game problem with control player and fault player, which is solved by utilizing adaptive dynamic programming technique. Using a critic-only neural network, the cost function is approximated to obtain the solution of the Hamilton-Jacobi-Isaacs equation (HJIE). Then, the FTC strategy can be obtained based on the saddle point of HJIE, and ensures the satisfactory control performance for NCTS. Furthermore, the closed-loop NCTS can be guaranteed to be asymptotically stable, rather than ultimately uniformly bounded in corresponding existing methods. Finally, a simulation example is provided to verify the safe and reliable fault tolerance performance of the designed control method.
Doynikova, Elena V., Fedorchenko, Andrei V., Novikova, Evgenia S., U shakov, Igor A., Krasov, Andrey V..  2021.  Security Decision Support in the Control Systems based on Graph Models. 2021 IV International Conference on Control in Technical Systems (CTS). :224—227.
An effective response against information security violations in the technical systems remains relevant challenge nowadays, when their number, complexity, and the level of possible losses are growing. The violation can be caused by the set of the intruder's consistent actions. In the area of countermeasure selection for a proactive and reactive response against security violations, there are a large number of techniques. The techniques based on graph models seem to be promising. These models allow representing the set of actions caused the violation. Their advantages include the ability to forecast violations for timely decision-making on the countermeasures, as well as the ability to analyze and consider the coverage of countermeasures in terms of steps caused the violation. The paper proposes and describes a decision support method for responding against information security violations in the technical systems based on the graph models, as well as the developed models, including the countermeasure model and the graph representing the set of actions caused the information security violation.