2021 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第4页

The Utility in Conjoint Analysis as a Fast Expert Elicitation Technique 联合分析作为快速专家启发技术的应用

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624683

Brett Jefferson, Natalie C. Heller, Joseph A. Cottam, Nhuy Van, George Chin

引用次数: 1

Attacking DNN-based Cross-modal Retrieval Hashing Framework with Adversarial Perturbations 对抗扰动攻击基于dnn的跨模态检索哈希框架

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-11-02 DOI: 10.1109/ISI53945.2021.9624750

Xingwei Zhang, Xiaolong Zheng, W. Mao

{"title":"Attacking DNN-based Cross-modal Retrieval Hashing Framework with Adversarial Perturbations","authors":"Xingwei Zhang, Xiaolong Zheng, W. Mao","doi":"10.1109/ISI53945.2021.9624750","DOIUrl":"https://doi.org/10.1109/ISI53945.2021.9624750","url":null,"abstract":"The rapid development of Internet and online data explosions elicit strong aspirations for users to search for semantic relevant information based on available samples. While the data online are always released with different modalities like images, videos or texts, effective retrieval models should discover latent semantic information with different structures. Recently, the state-of-the-art deep cross modal retrieval frameworks have effectively enhanced the performance on commonly-used platforms using the deep neural networks (DNNs). Yet DNNs have been verified to be easily misguided by small perturbations, and there are already several attack generation methods proposed on DNN-based models for real-world tasks, but they are all focused on supervised tasks like classification or object recognition. To effectively evaluate the robustness of deep cross-modal retrieval frameworks, in this paper, we propose a retrieval-based adversarial perturbation generation method, and demonstrate that our perturbation could effectively attack the state-of-the-art deep cross-modal and single image retrieval hashing models.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126222457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Open Threat Exchange (OTX) to Understand Spatio-Temporal Trends of Cyber Threats: Covid-19 Case Study 利用开放式威胁交换(OTX)了解网络威胁的时空趋势:Covid-19案例研究

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-09-03 DOI: 10.1109/ISI53945.2021.9624677

Othmane Cherqi, Hicham Hammouchi, M. Ghogho, H. Benbrahim

引用次数: 3

Cyber-Physical System Security Surveillance using Knowledge Graph based Digital Twins - A Smart Farming Usecase 基于知识图谱的数字孪生网络物理系统安全监控——一个智能农业用例

2021 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2021-08-31 DOI: 10.13016/M2XUYO-LZ8V

Sai Sree Laya Chukkapalli, Nisha Pillai, Sudip Mittal, A. Joshi

{"title":"Cyber-Physical System Security Surveillance using Knowledge Graph based Digital Twins - A Smart Farming Usecase","authors":"Sai Sree Laya Chukkapalli, Nisha Pillai, Sudip Mittal, A. Joshi","doi":"10.13016/M2XUYO-LZ8V","DOIUrl":"https://doi.org/10.13016/M2XUYO-LZ8V","url":null,"abstract":"Rapid advancements in Cyber-Physical System (CPS) capabilities have motivated farmers to deploy this ecosystem on their farms. However, there is a growing concern among users regarding the security risks associated with CPS. Especially with rising number of cyber-attacks on CPS, such as modifying sensor readings, interrupting operations, etc. Therefore, this paper describes a security surveillance framework to detect deviations in the ecosystem by incorporating a digital twin supported anomaly detection model. The reason for incorporating digital twins is that they add value by enabling real-time monitoring of connected smart farms. We pre-process the collected data from sensors deployed on the smart farm setup. The pre-processed data is fused with our smart farm ontology to populate a knowledge graph. The generated graph is further queried to extract the necessary sensor data. We utilize the extracted normal data to train the anomaly detection model. Further, we tested our model if it identifies abnormal values from sensors by simulating anomalous use case scenarios specific to our ecosystem.","PeriodicalId":347770,"journal":{"name":"2021 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116486649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6