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arXiv 2023年

作者： Yang, Suorong Zhang, Geng Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Interpolation methodologies have been widely used within the domain of indoor positioning systems. However, existing indoor positioning interpolation algorithms exhibit several inherent limitations, including reliance on complex mathematical models, limited flexibility, and relatively low precision. To enhance the accuracy and efficiency of indoor positioning interpolation techniques, this paper proposes a simple yet powerful geometric-aware interpolation algorithm for indoor positioning tasks. The key to our algorithm is to exploit the geometric attributes of the local topological manifold using manifold learning principles. Therefore, instead of constructing complicated mathematical models, the proposed algorithm facilitates the more precise and efficient estimation of points grounded in the local topological manifold. Moreover, our proposed method can be effortlessly integrated into any indoor positioning system, thereby bolstering its adaptability. Through a systematic array of experiments and comprehensive performance analyses conducted on both simulated and real-world datasets, we demonstrate that the proposed algorithm consistently outperforms the most commonly used and representative interpolation approaches regarding interpolation accuracy and efficiency. Furthermore, the experimental results also underscore the substantial practical utility of our method and its potential applicability in real-time indoor positioning scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

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Security Risk Management Plan. Student Portal-Case Study 20

Security Risk Management Plan. Student Portal-Case Study

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2020 Asia Service sciences and software engineering Conference, ASSE 2020

作者： Mogos, Gabriela Xi'an Jiaotong-Liverpool University Computer Science and Software Engineering Department Suzhou China

ISBN: (纸本)9781450377102

A student portal is a regularly utilized expression to describe the login page where students can give a username and password to access an instruction association's projects and other learning related materials. For instance, a student who has taken a crack at an online program may utilize a university portal to get to online course materials, for example, articles, lectures and recordings, facilitated on the school's data bases. university portal might be utilized to give data about the school, unique activities and events, course information, schedules, scholarly resources and contact data. The paper presents several security issues that were detected at the Prince Sultan University, Riyadh, Saudi Arabia's portal. The Prince Sultan University (PSU) portal allows students to access the main website which is connected to PSU Moodle website and also connected to PSU Edugate website. Starting from the determined vulnerabilities, the paper presents a risk analysis of the sensitive data of a public institution, in cyberspace. © 2020 ACM.

关键词： Students

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Security-based Safety Hazard Analysis using STAMP, STPA & HAZOP: A DAM Case Study

Security-based Safety Hazard Analysis using STAMP, STPA & HA...

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Frontiers of Information Technology (FIT)

作者： Muhammad Farooq Irum Inayat Maya Daneva Department of Software Engineering National University of Computer Emerging Sciences Islamabad Pakistan Department of Creative Technology Air University Islamabad Pakistan LERO University College Cork Cork Ireland Faulty of Electrical Engineering Mathematics and Computer Science (EEMCS) Services Cybersecurity & Safety (SCS) University of Twente Enschede The Netherlands

Security and safety emerged as an interconnected concept for Cyber-Physical Systems (CPS). In recent years, CPS witnessed an enormous cyber-attack that endorsed the significance of security over safety of the CPS. Many cyber incidents have been recorded where intruders exploit security vulnerabilities that result in safety hazards. In literature, often times security and safety are discussed as a combined concern. This presses the need to treat security and safety as a mutual apprehension. However, more empirical evidence is required to support this mutuality of security and safety. To close this gap, we conducted an empirical study to identify hazard(s) using several methods including the Systematic Theoretical Accidental Model and Process (STAMP), Systematic Theoretic Process Analysis (STPA), Hazard and Operability Study (HAZOP) of a CPS i.e., case study of a Dam , and to performed the risk management (risk identification, risk analysis and mitigation) for the mentioned case study. Our focus remained on security and safety risks only. As a result, we identified all the possible unsafe control events and their potential hazards along with the severity level. Moreover, we suggested the risk mitigation mechanism against the identified hazards which may contribute to the accidents. This study holds implications for CPS practitioners and researchers exploring risk in CPS.

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Is GPT-4 conscious?

arXiv

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arXiv 2024年

作者： Tait, Izak Bensemann, Joshua Wang, Ziqi Computer Science and Software Engineering Department Auckland University of Technology 55 Wellesley Street East CBD Auckland1010 New Zealand The NAOInstitute The University of Auckland 314 Khyber Pass Road New Market Auckland1023 New Zealand

GPT-4 is often heralded as a leading commercial AI offering, sparking debates over its potential as a steppingstone toward artificial general intelligence. But does it possess consciousness? This paper investigates this key question using the nine qualitative measurements of the Building Blocks theory. GPT-4’s design, architecture and implementation are compared to each of the building blocks of consciousness to determine whether it has achieved the requisite milestones to be classified as conscious or, if not, how close to consciousness GPT-4 is. Our assessment is that, while GPT-4 in its native configuration is not currently conscious, current technological research and development is sufficient to modify GPT-4 to have all the building blocks of consciousness. Consequently, we argue that the emergence of a conscious AI model is plausible in the near term. The paper concludes with a comprehensive discussion of the ethical implications and societal ramifications of engineering conscious AI entities. © 2024, CC BY.

关键词： Artificial intelligence

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs

SemLog: A Semantics-based Approach for Anomaly Detection in ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaoman Tan Ningning Han Siyang Lu Wu Chen Dongdong Wang College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Civil Environmental and Construction Engineering University of Central Florida Orlando FL US

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system’s run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection.

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Secure digital databases using watermarking based on english-character attributes

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Recent Advances in computer science and Communications 2020年第3期13卷 477-481页

作者： Khatatneh, Khalaf Odeh, Ashraf Mashaleh, Ashraf Hamadeen, Hind Department of Computer Science Al Balqa Applied University As-Salt Jordan Department of Science and Technology Computer Quality Society Jordan Computer Center Al Balqa Applied University As-Salt Jordan Department of Software Engineering Al-Balqa Applied University As-Salt Jordan

Introduction: The single space and the double space (DS). In this procedure, an image is used to watermark a digital database, where the image bytes are divided into binary strings that block the text attributes of the selected database, we proposed an algorithm to defend against four common database attacks. Objective: Perform the watermark is Embedding and makes extraction of the watermark. We also describe the principal of the Embedding and extraction the watermark. Methods: The procedure to extract the watermark does not require knowledge of the original database that does not carry the same watermark. This feature is extremely important because it allows the discovery of a watermark in a copy of the original database, regardless of the subsequent updates to the asset. The extraction procedure is a direct reflection of the procedure used to embed the watermark is six steps. Results: Using new algorithm ability to develop a database watermark that would make it difficult for an attacker to remove or change the watermark without discovering the value of the object. To be judged effective, the database algorithm had to be able to create a strong enough watermark that could sustain the security of the database in the face of the following four types of attack: deletion of a sub-dataset, addition of a sub-dataset. Conclusion: The performance of the proposed algorithm was assessed in respect of its ability to defend the database against four common attacks for all tuples selection. © 2020 Bentham science Publishers.

关键词： Database systems

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MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence

arXiv

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arXiv 2024年

作者： Tian, Hongduan Liu, Feng Liu, Tongliang Du, Bo Cheung, Yiu-Ming Han, Bo TMLR Group Hong Kong Baptist University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong TMLR Group University of Melbourne Australia Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan Univeristy China

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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F2A2: flexible fully-decentralized approximate actor-critic for cooperative multi-agent reinforcement learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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The Minimum Covariance Determinant Technique for Insider Threat Detection in Communications and Network Security

SSRN

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SSRN 2024年

作者： Al-Shehari, Taher Kadri, Mohammed Al-Mhiqani, Mohammed Nasser Al-Razgan, Muna Alfakih, Taha Computer Skills Department of Self-Development Skill Common First Year Deanship King Saud University Riyadh11362 Saudi Arabia Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Insider threats present a critical concern for network security of organizations, as malicious activities (data theft, hacking, security breaching, etc.) perpetrated by insiders with privileged access systems can result in substantial damage. Many approaches are proposed to detect insider threats, but still there are many challenges on the field such as the imbalanced nature of insider threat datasets, where instances of malicious behavior are often limited in comparison to benign activities. This makes insider threat detection an understudied research area. Existing approaches tried to address the imbalance issue of the insider threat dataset by manipulating the data level utilizing the duplication (over-sampling) and removal (under-sampling) of the dataset’s observations which is not reflecting the real-world data stream within organizations’ networks. To tackle this challenge, Artificial Intelligent (AI) algorithms like the Minimum Covariance Determinant (MCD) algorithm is adopted due to its robustness against outliers and its potential to handle imbalanced data effectively. The proposed approach is evaluated using the CERT insider threat dataset, which encompasses diverse real-world scenarios. Through comprehensive experimentation, the MCD algorithm demonstrates promising results, achieving a remarkable detection rate of 98%. This study underscores the significance of leveraging advanced anomaly detection techniques to address imbalanced insider threat datasets and highlights the practical applicability of the MCD algorithm in enhancing detection accuracy of threats within organizations’ networks. © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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