Article
Computer Science, Information Systems
Huanruo Li, Yunfei Guo, Shumin Huo, Hongchao Hu, Penghao Sun
Summary: The study proposes a defensive deception framework for cloud security based on deep reinforcement learning. It effectively disguises cloud assets and distracts attack resources using digital decoys, leading to significant improvement in achieving comprehensive defense goals.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Review
Chemistry, Analytical
Martin Bauer, Luis Sanchez, JaeSeung Song
Summary: The Smart City concept has rapidly developed over the past decade, significantly improving urban life through the application of IoT technology. However, challenges remain in the development of Smart Cities, necessitating further research and technological innovation to address them.
Article
Computer Science, Information Systems
Varun G. Menon, Sunil Jacob, Saira Joseph, Paramjit Sehdev, Mohammad R. Khosravi, Fadi Al-Turjman
Summary: The research aims to develop a new feature that can enhance the existing technology in automobiles, enhance safety, and achieve low costs. By integrating Google Assistant with the previously developed Smart Accident Precognition System, real-time monitoring and intelligent control of vehicles have been achieved, improving vehicle safety.
INTERNET OF THINGS
(2022)
Article
Engineering, Multidisciplinary
Chao Pei, Yang Xiao, Wei Liang, Xiaojia Han
Summary: A detection method based on canonical variate analysis is proposed in this paper to monitor the variation of statistical detection indicators on projected canonical variables before and after attacks. Experimental results demonstrate the effectiveness and accuracy of the proposed method.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Mathematics
Aparna Kumari, Rushil Kaushikkumar Patel, Urvi Chintukumar Sukharamwala, Sudeep Tanwar, Maria Simona Raboaca, Aldosary Saad, Amr Tolba
Summary: This paper proposes an AI-based attack-detection and prevention mechanism for the smart grid system, which ensures data security and integrity using a cryptography-driven recommender system. The proposed scheme outperforms existing approaches and achieves a relatively high accuracy of 99.12%.
Article
Green & Sustainable Science & Technology
Bushra Tahir, Muhammad Tariq
Summary: This article proposes a method to address the potential false data injection attacks in the application of air taxi systems in smart cities. By utilizing sensor data integrity and a decentralized threat detection model, the method can accurately detect and prevent unnoticed attacks while protecting the data privacy of each taxi.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Chemistry, Analytical
Tan Duy Le, Mengmeng Ge, Adnan Anwar, Seng W. Loke, Razvan Beuran, Robin Doss, Yasuo Tan
Summary: The smart grid is a crucial technology for sustainable development, but it has been increasingly targeted by cyber attacks in recent years. To address this challenge, this paper proposes the use of analytical techniques and simulations to create a practical solution for smart grid cybersecurity experimentation. The paper provides a literature review on smart grid attack analysis and introduces a Cyber Attack Analysis Framework, called GridAttackAnalyzer, which incorporates graphical security modeling techniques. A case study involving IoT devices is conducted to validate the framework, and user evaluations show satisfactory usability. The modular and extensible framework can serve various purposes in smart grid research, cybersecurity training, and security evaluation.
Article
Engineering, Civil
Yuanzhe Wang, Qipeng Liu, Ehsan Mihankhah, Chen Lv, Danwei Wang
Summary: This paper explores the cybersecurity challenges faced by autonomous vehicles when under sensor attacks. A model-based framework is proposed to detect attacks and identify their sources for secure localization. Sensor redundancy and a bank of attack detectors are utilized to ensure robustness against cyber-attacks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Shailendra Pratap Singh, Youseef Alotaibi, Gyanendra Kumar, Sur Singh Rawat
Summary: This research proposes an extended form of differential evolution (DE) with an intelligent mutation operator to protect the large volume of data produced by internet systems from unauthorized users and devices. Experimental findings show that this method outperforms the recent evolutionary algorithm (EA) in terms of confidentiality, integrity, authentication, and availability.
Article
Computer Science, Information Systems
Abla El Bekkali, Mohamed Essaaidi, Mohammed Boulmalf
Summary: A smart city improves the quality of life and reduces municipal service costs through the use of digital technologies and IoT. To address the main challenge of cybersecurity, Blockchain technology emerges as a solution that provides data security and confidentiality for smart cities. This paper proposes a comprehensive framework and architecture based on Blockchain, big data, and artificial intelligence to enhance smart cities cybersecurity.
Article
Computer Science, Information Systems
Kim -Hung Le, Khanh-Hoi Le -Minh, Huy -Tan Thai
Summary: With the growing popularity of IoT and AI, Edge Computing has successfully reduced latency, network traffic consumption, and security risks. However, integrating AI and Edge Computing into IoT is challenging due to concerns about device performance, energy efficiency, and privacy. In this paper, brainyEdge, an AI-enabled framework for edge devices, is introduced to address these challenges and satisfy the QoE criteria of IoT applications. By enhancing the intelligence of AI models at edges through transfer learning and incremental learning, personalized and incremental data can be locally stored and used for dynamic retraining. This framework promotes edge-cloud collaboration while preserving data privacy and minimizes network costs through lightweight deployment and compression.
Article
Computer Science, Information Systems
Xiao-Guang Zhang, Guang-Hong Yang, Xiu-Xiu Ren
Summary: This paper proposes a novel network steganography-based security framework to ensure the security operation of the cyber-physical systems (CPSs). It establishes a covert channel using dynamical system's measurements to conceal secret data exchange. Additionally, it explores a data-based attack detection methodology and a defense methodology combining covert transmission with simple linear encryption.
INFORMATION SCIENCES
(2022)
Article
Green & Sustainable Science & Technology
D. Prabakar, M. Sundarrajan, R. Manikandan, N. Z. Jhanjhi, Mehedi Masud, Abdulmajeed Alqhatani
Summary: Given the increasing number of cybersecurity incidents, industries engaged in digital activity are facing major challenges. The use of Internet of Things (IoT) devices in various sectors has led to a rise in malicious attacks. A novel technique is proposed in this research to analyze network traffic and detect malicious attacks using IoT artificial intelligence techniques for a sustainable smart city. The technique employs a kernel quadratic vector discriminant machine for traffic analysis and adversarial Bayesian belief networks for attack detection. Experimental analysis shows high levels of throughput, data traffic analysis, packet delivery ratio, energy efficiency, and QoS.
Article
Computer Science, Artificial Intelligence
Imran Ahmed, Yulan Zhang, Gwanggil Jeon, Wenmin Lin, Mohammad R. Khosravi, Lianyong Qi
Summary: Advancements in digital technologies have revolutionized smart city applications, and the convergence of artificial intelligence and blockchain technology has transformed smart city infrastructures and promoted sustainable IoT applications.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Achref Haddaji, Samiha Ayed, Lamia Chaari Fourati
Summary: This paper presents a comprehensive survey of AI-based techniques for security issues in vehicular networks. It provides background information on vehicular networks and their vulnerabilities, evaluates the impact of AI fundamentals on vehicular security, classifies and compares AI-based solutions related to security in vehicular networks, and analyzes the works included in the survey.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)