2022 IEEE Conference on Dependable and Secure Computing (DSC)最新文献_第2页

Defending OC-SVM based IDS from poisoning attacks 保护基于OC-SVM的IDS免受投毒攻击

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888908

Lu Zhang, R. Cushing, P. Grosso

引用次数: 0

A Call for a New Privacy & Security Regime for IoT Smart Toys 呼吁物联网智能玩具建立新的隐私和安全制度

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888910

Joshua Streiff, Naheem Noah, Sanchari Das

{"title":"A Call for a New Privacy & Security Regime for IoT Smart Toys","authors":"Joshua Streiff, Naheem Noah, Sanchari Das","doi":"10.1109/DSC54232.2022.9888910","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888910","url":null,"abstract":"The current set of reactive regulatory agencies, legal protections, and market forces have proven inadequate for managing the security and privacy of the Internet of Things (IoT). Given the ubiquitous nature of IoT devices, current cybersecurity and privacy laws fail to enforce the protections of the data of vulnerable populations. The most vulnerable of these users are children, who are at the most significant risk of harm and least adequately protected by the current regime of controls for devices such as smart toys. In this paper, we review the currently existing regulatory and legal controls related to IoT devices while giving a brief overview of privacy & security policies that govern the data access, retention, and usage policies of children's smart toys. We detail the impact of such security and privacy vulnerabilities by conducting three case studies on IoT smart toys, including FisherPrice's SmartBear, Spiral Toys CloudPet Unicorn, and Owl's SmartWatch. Finally, we establish reasons for the complete restructuring of the responsibilities, requirements, and proactive options for implementing cybersecurity rules by IoT device manufacturers.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127054605","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: 1

ELSA: Edge Lightweight Searchable Attribute-based encryption Multi-keyword Scalability ELSA: Edge轻量级可搜索属性加密多关键字可扩展性

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888846

Jawhara Aljabri, A. L. Michala, Jeremy Singer

引用次数: 1

Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs 新型dnn位翻转攻击与防御策略的设计与分析

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888943

Yash Khare, Kumud Lakara, M. S. Inukonda, Sparsh Mittal, Mahesh Chandra, Arvind Kaushik

{"title":"Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs","authors":"Yash Khare, Kumud Lakara, M. S. Inukonda, Sparsh Mittal, Mahesh Chandra, Arvind Kaushik","doi":"10.1109/DSC54232.2022.9888943","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888943","url":null,"abstract":"In this paper, we present novel bit-flip attack (BFA) algorithms for DNNs, along with techniques for defending against the attack. Our attack algorithms leverage information about the layer importance, such that a layer is considered important if it has high-ranked feature maps. We first present a classwise-targeted attack that degrades the accuracy of just one class in the dataset. Comparative evaluation with related works shows the effectiveness of our attack algorithm. We finally propose multiple novel defense strategies against untargeted BFAs. We comprehensively evaluate the robustness of both large-scale CNNs (VGG19, ResNext50, AlexNet and Res Net) and compact CNNs (MobileNet-v2, ShuffleNet, GoogleNet and SqueezeNet) towards BFAs. We also reveal a valuable insight that compact CNNs are highly vulnerable to not only well-crafted BFAs such as ours, but even random BFAs. Also, defense strategies are less effective on compact CNNs. This fact makes them unsuitable for use in security-critical domains. Source code is released at https://sites.google.com/view/dsc-2022-paper-bit-flip-attack.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134101514","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: 1

Multi-task Learning Model based on Multiple Characteristics and Multiple Interests for CTR prediction 基于多特征多兴趣的CTR预测多任务学习模型

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888898

Yufeng Xie, Mingchu Li, Kun Lu, Syed Bilal Hussain Shah, Xiao Zheng

{"title":"Multi-task Learning Model based on Multiple Characteristics and Multiple Interests for CTR prediction","authors":"Yufeng Xie, Mingchu Li, Kun Lu, Syed Bilal Hussain Shah, Xiao Zheng","doi":"10.1109/DSC54232.2022.9888898","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888898","url":null,"abstract":"In the era of big data, the acquisition and utilization of information becomes difficult with the skyrocketing amount of data. It is often difficult for ordinary users to find the in-formation or items they need, and personalized recommendation systems can solve this problem well. Currently, recommendation systems increasingly adopt models based on deep learning. The most critical issue in using deep learning for recommendation systems is how to use neural networks to accurately learn user representation vectors and item representation vectors. Many deep learning models used a single vector to represent users, but users' interests were often diverse. Therefore, some researchers consider using multiple vectors to represent user interests, and each interest vector corresponds to a category of items. This method sounds more scientific. However, these models still have problems. Their interpretation of user interests stays at the item level, and does not go deep into the item feature level. In order to solve this problem, we consider user interests from the perspective of item characteristics, and propose 3M (Multi-task, Multi-interest, Multi-feature) model. The 3M model trains multiple interest vectors for each user and extracts multiple characteristic vectors for each item at the same time, then uses a multi-task learning model to connect the characteristic vectors with the interest vectors and train them to obtain multiple interest scores. According to the multiple interest scores, the user click probability can be obtained. Experiments show that our model performs significantly better than the classic CTR(Click - Through Rate) prediction model on the experimental dataset.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129285847","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: 1

Symbolon: Enabling Flexible Multi-device-based User Authentication 符号:启用灵活的基于多设备的用户认证

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888854

Thalia M. Laing, Eduard Marin, M. Ryan, Joshua Schiffman, Gaetan Wattiau

{"title":"Symbolon: Enabling Flexible Multi-device-based User Authentication","authors":"Thalia M. Laing, Eduard Marin, M. Ryan, Joshua Schiffman, Gaetan Wattiau","doi":"10.1109/DSC54232.2022.9888854","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888854","url":null,"abstract":"Hardware tokens are increasingly used to support second-factor and passwordless authentication schemes. While these devices improve security over weaker factors like passwords, they suffer from a number of security and practical issues. We present the design and implementation of Symbolon, a system that allows users to authenticate to an online service in a secure and flexible manner by using multiple personal devices (e.g., their smartphone and smart watch) together, in place of a password. The core idea behind Symbolon is to let users authenticate only if they carry a sufficient number of their personal devices and give explicit consent. We use threshold cryptography at the client side to protect against strong adversaries while overcoming the limitations of multi-factor authentication in terms of flexibility. Symbolon is compatible with FIDO servers, but improves the client-side experience compared to FIDO in terms of security, privacy, and user control. We design Symbolon such that the user can (i) authenticate using a flexible selection of devices, which we call “authenticators”; (ii) define fine-grained threshold policies that enforce user consent without involving or modifying online services; and (iii) add or revoke authenticators without needing to generate new cryptographic keys or manually (un)register them with online services. Finally, we present a detailed design and analyse the security, privacy and practical properties of Symbolon; this includes a formal proof using ProVerif to show the required security properties are satisfied.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114246908","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: 1

Using Poisson Distribution to Enhance CNN-based NB-IoT LDoS Attack Detection 基于泊松分布增强基于cnn的NB-IoT LDoS攻击检测

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888864

Jiang Zeng, Li-En Chang, Hsin-Hung Cho, Chi-Yuan Chen, Han-Chieh Chao, Kuo-Hui Yeh

引用次数: 1

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification 促进边缘计算的深度学习:数据分类的案例研究

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888939

A. Alsalemi, A. Amira, H. Malekmohamadi, Kegong Diao

{"title":"Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification","authors":"A. Alsalemi, A. Amira, H. Malekmohamadi, Kegong Diao","doi":"10.1109/DSC54232.2022.9888939","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888939","url":null,"abstract":"Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correct utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow, and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121961677","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: 1

How National CSIRTs Operate: Personal Observations and Opinions from MyCERT 国家csirt如何运作:来自MyCERT的个人观察和意见

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888803

Sharifah Roziah Mohd Kassim, Solahuddin Bin Shamsuddin, Shujun Li, B. Arief

引用次数: 2

Malicious and Benign URL Dataset Generation Using Character-Level LSTM Models 使用字符级LSTM模型生成恶意和良性URL数据集

2022 IEEE Conference on Dependable and Secure Computing (DSC) Pub Date : 2022-06-22 DOI: 10.1109/DSC54232.2022.9888835

Spencer Vecile, Kyle Lacroix, Katarina Grolinger, J. Samarabandu

{"title":"Malicious and Benign URL Dataset Generation Using Character-Level LSTM Models","authors":"Spencer Vecile, Kyle Lacroix, Katarina Grolinger, J. Samarabandu","doi":"10.1109/DSC54232.2022.9888835","DOIUrl":"https://doi.org/10.1109/DSC54232.2022.9888835","url":null,"abstract":"As technologies advance, so do the attacks on them. Cybersecurity plays a significant role in society to protect everyone. Malicious URLs are links designed to promote scams, attacks, and frauds. Companies often have web filtering algorithms that will blacklist specific URLs as malicious; however, due to privacy concerns, they will not give outside entities access to their cybersecurity data. Unfortunately, this lack of data creates a dire need for more data in cybersecurity research and machine learning applications. This paper proposes using machine learning to generate new synthetic URLs characteristically indistinguishable from the data they replace. To do this two character-level long short-term memory (LSTM) models were trained, one to generate malicious URLs and one to generate benign URLs. To assess the quality of the synthetic data two tests were performed. (1) Classify the URLs into malicious and benign to ensure the characteristics of the original data were preserved. (2) Use the Levenstein ratio to check the similarity between the real and synthetic URLs to ensure sufficient anonymization. The results from the classification test show that the synthetic data classifier only slightly underperformed the real data classifier; however, with having accuracy, precision, recall, sensitivity, and specificity above 99%, it can be concluded that the characteristics of the malicious and benign URLs were preserved. The Levenstein ratio tests showed a mean of 67% and 79% similarity for the benign and malicious URLs, respectively. In the end, the character-level LSTM model successfully generated an anonymized, synthetic dataset, that was characteristically similar to the original, which could pave the way for the publication of many more datasets in this way.","PeriodicalId":368903,"journal":{"name":"2022 IEEE Conference on Dependable and Secure Computing (DSC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122235087","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: 1