2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)最新文献_第8页

A three-stage machine learning network security solution for public entities 面向公共实体的三阶段机器学习网络安全解决方案

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00145

Stanisław Saganowski

引用次数: 0

Robust Scheduling for Large-Scale Distributed Systems 大规模分布式系统的鲁棒调度

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00019

Young Choon Lee, Jayden King, Young Ki Kim, Seok-Hee Hong

{"title":"Robust Scheduling for Large-Scale Distributed Systems","authors":"Young Choon Lee, Jayden King, Young Ki Kim, Seok-Hee Hong","doi":"10.1109/TrustCom50675.2020.00019","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00019","url":null,"abstract":"In large-scale distributed systems, such as clouds, failures are rather the norm than the exception. These failures include job failures, server failures, network outage and power failure. Among them, server failures are most common. With the wide adoption of cloud computing, the impact of server failures in clouds is far greater than that in traditional computer clusters as jobs of different tenants are often co-located (multi-tenancy). In this paper, we address the problem of robust scheduling, with realistic failure modeling, to minimize such impact on the execution of (co-located) jobs. To this end, we develop four online failure-aware (FA) scheduling algorithms, FAFF-WJ, FAFF-FC, FABF-WJ and FABF-FC, considering the availability and reliability of servers. In particular, FF (First-Fit) and BF (Best-Fit) indicate how the availability of servers is checked while WJ (Waiting Job) and FC (Failure Count) differ primarily in whether the reliability is measured from job's perspective or server's perspective. All four algorithms are designed essentially by combining these availability and reliability check methods. We evaluate our scheduling algorithms with failures generated based on our failure modeling of six real-world server failure traces. Our evaluation results show the effectiveness of our scheduling algorithms in robust job execution, with respect to both performance and cost.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129110558","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

SmartITC 2020 Organizing and Program Committees SmartITC 2020组织委员会和项目委员会

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/trustcom50675.2020.00014

引用次数: 0

Prihook: Differentiated context-aware hook placement for different owners' smartphones Prihook:针对不同用户的智能手机设置不同的上下文感知钩子

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00087

Chen Tian, Yazhe Wang, Peng Liu, Yu Wang, Ruirui Dai, Anyuan Zhou, Zhen Xu

引用次数: 1

Adaptive Random Test Case Generation Based on Multi-Objective Evolutionary Search 基于多目标进化搜索的自适应随机测试用例生成

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00020

Chengying Mao, Linlin Wen, T. Chen

{"title":"Adaptive Random Test Case Generation Based on Multi-Objective Evolutionary Search","authors":"Chengying Mao, Linlin Wen, T. Chen","doi":"10.1109/TrustCom50675.2020.00020","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00020","url":null,"abstract":"Diversity is the key factor for test cases to detect program failures. Adaptive random testing (ART) is one of the effective methods to improve the diversity of test cases. Being an ART algorithm, the evolutionary adaptive random testing (eAR) only increases the distance between test cases to enhance its failure detection ability. This paper presents a new ART algorithm, MoesART, based on multi-objective evolutionary search. In this algorithm, in addition to the dispersion diversity, two other new diversities (or optimization objectives) are designed from the perspectives of the balance and proportionality of test cases. Then, the Pareto optimal solution returned by the NSGA-II framework is used as the next test case. In the experiments, the typical block failure pattern in the cases of two-dimensional and three-dimensional input domains is used to validate the effectiveness of the proposed MoesART algorithm. The experimental results show that MoesART exhibits better failure detection ability than both eAR and the fixed-sized-candidate-set ART (FSCS-ART), especially for the programs with three-dimensional input domain.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132628421","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

A Scoring System to efficiently measure Security in Cyber-Physical Systems 一种有效测量网络物理系统安全性的评分系统

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00151

A. Aigner, Abdelmajid Khelil

{"title":"A Scoring System to efficiently measure Security in Cyber-Physical Systems","authors":"A. Aigner, Abdelmajid Khelil","doi":"10.1109/TrustCom50675.2020.00151","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00151","url":null,"abstract":"The importance of Cyber-Physical Systems (CPS) gains more and more weight in our daily business and private life. Although CPS build the backbone for major trends, like Industry 4.0 and connected vehicles, they also propose many new challenges. One major challenge can be found in achieving a high level of security within such highly connected environments, in which an unpredictable number of heterogeneous systems with often-distinctive characteristics interact with each other. In order to develop high-level security solutions, system designers must eventually know the current level of security of their specification. To this end, security metrics and scoring frameworks are essential, as they quantitatively express security of a given design or system. However, existing solutions may not be able to handle the proposed challenges of CPS, as they mainly focus on one particular system and one specific attack. Therefore, we aim to elaborate a security scoring mechanism, which can efficiently be used in CPS, while considering all essential information. We break down each system within the CPS into its core functional blocks and analyze a variety of attacks in terms of exploitability, scalability of attacks, as well as potential harm to targeted assets. With this approach, we get an overall assessment of security for the whole CPS, as it integrates the security-state of all interacting systems. This allows handling the presented complexity in CPS in a more efficient way, than existing solutions.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130441282","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

A Framework for Measuring IoT Data Quality Based on Freshness Metrics 基于新鲜度指标的物联网数据质量测量框架

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00167

Fatma Mohammed, A. Kayes, E. Pardede, W. Rahayu

{"title":"A Framework for Measuring IoT Data Quality Based on Freshness Metrics","authors":"Fatma Mohammed, A. Kayes, E. Pardede, W. Rahayu","doi":"10.1109/TrustCom50675.2020.00167","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00167","url":null,"abstract":"Over the last decade, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of continuous streaming data. A massive amount of IoT data will be generated in the future. Therefore, it is necessary to create more sophisticated frameworks to measure IoT data quality, considering relevant attributes such as the freshness, reliability and trustworthiness of IoT data. Existing data freshness models and frameworks mostly depend on the timestamp. However, the frequency of IoT data (e.g., data generated by sensors which is measured per millisecond or minute) needs to be considered, that is, IoT data can change frequently. We introduce a new model for measuring IoT data freshness. In our model, we define unreliable IoT data and discard them while considering fresh data. We introduce a formal approach to IoT data freshness including the underlying concepts and definitions. Using this formal approach, we propose an algorithm for the numerical calculation of the freshness attributes. We conduct several sets of experiments and demonstrate the feasibility of the proposed framework by quantifying the performance of the freshness measurement algorithm. We also demonstrate the capability of the framework to capture freshly generated IoT data through a software prototype and several case studies. Finally, we provide a roadmap for future research considering other IoT data quality attributes, such as reliability and trustworthiness.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130696326","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: 2

TEADS: A Defense-aware Framework for Synthesizing Transient Execution Attacks TEADS:用于合成瞬态执行攻击的防御感知框架

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00052

Tianlin Huo, Wenhao Wang, Pei Zhao, Yufeng Li, Tingting Wang, Mingshu Li

{"title":"TEADS: A Defense-aware Framework for Synthesizing Transient Execution Attacks","authors":"Tianlin Huo, Wenhao Wang, Pei Zhao, Yufeng Li, Tingting Wang, Mingshu Li","doi":"10.1109/TrustCom50675.2020.00052","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00052","url":null,"abstract":"Since 2018, a broad class of microarchitectural attacks called transient execution attacks (e.g., Spectre and Meltdown) have been disclosed. By abusing speculative execution mechanisms in modern CPUs, these attacks enable adversaries to leak secrets across security boundaries. A transient execution attack typically evolves through multiple stages, termed the attack chain. We find that current transient execution attacks usually rely on static attack chains, resulting in that any blockage in an attack chain may cause the failure of the entire attack. In this paper, we propose a novel defense-aware framework, called TEADS, for synthesizing transient execution attacks dynamically. The main idea of TEADS is that: each attacking stage in a transient execution attack chain can be implemented in several ways, and the implementations used in different attacking stages can be combined together under certain constraints. By constructing an attacking graph representing combination relationships between the implementations and testing available paths in the attacking graph dynamically, we can finally synthesize transient execution attacks which can bypass the imposed defense techniques. Our contributions include: (1) proposing an automated defense-aware framework for synthesizing transient execution attacks, even though possible combinations of defense strategies are enabled; (2) presenting an attacking graph extension algorithm to detect potential attack chains dynamically; (3) implementing TEADS and testing it on several modern CPUs with different protection settings. Experimental results show that TEADS can bypass the defenses equipped, improving the adaptability and durability of transient execution attacks.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130728856","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

Towards Network Traffic Monitoring Using Deep Transfer Learning 基于深度迁移学习的网络流量监控

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00144

Harsh Dhillon, A. Haque

{"title":"Towards Network Traffic Monitoring Using Deep Transfer Learning","authors":"Harsh Dhillon, A. Haque","doi":"10.1109/TrustCom50675.2020.00144","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00144","url":null,"abstract":"Network traffic is growing at an outpaced speed globally. The modern network infrastructure makes classic network intrusion detection methods inefficient to classify an inflow of vast network traffic. This paper aims to present a modern approach towards building a network intrusion detection system (NIDS) by using various deep learning methods. To further improve our proposed scheme and make it effective in real-world settings, we use deep transfer learning techniques where we transfer the knowledge learned by our model in a source domain with plentiful computational and data resources to a target domain with sparse availability of both the resources. Our proposed method achieved 98.30% classification accuracy score in the source domain and an improved 98.43% classification accuracy score in the target domain with a boost in the classification speed using UNSW -15 dataset. This study demonstrates that deep transfer learning techniques make it possible to construct large deep learning models to perform network classification, which can be deployed in the real world target domains where they can maintain their classification performance and improve their classification speed despite the limited accessibility of resources.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131211798","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: 13

DNRTI: A Large-scale Dataset for Named Entity Recognition in Threat Intelligence DNRTI:威胁情报中命名实体识别的大规模数据集

2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) Pub Date : 2020-12-01 DOI: 10.1109/TrustCom50675.2020.00252

Xuren Wang, Xinpei Liu, Shengqin Ao, Ning Li, Zhengwei Jiang, Zongyi Xu, Zihan Xiong, Mengbo Xiong, Xiaoqing Zhang

{"title":"DNRTI: A Large-scale Dataset for Named Entity Recognition in Threat Intelligence","authors":"Xuren Wang, Xinpei Liu, Shengqin Ao, Ning Li, Zhengwei Jiang, Zongyi Xu, Zihan Xiong, Mengbo Xiong, Xiaoqing Zhang","doi":"10.1109/TrustCom50675.2020.00252","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00252","url":null,"abstract":"Named entity recognition is an important and challenging problem in Natural language processing. Although the past decade has witnessed major advances in entity recognition in many fields, such successes have been slow to network security field, not only because of the data in the network security field is very professional, but also due to the sensitive information in the data. To advance named entity recognition research in network security field, we introduce a large-scale Dataset for Named Entity Recognition in Threat Intelligence (DNRTI). To this end, we collect more than 300 pieces of threat intelligence. The data in DNRTI is all annotated by experts in threat intelligence interpretation using 13 object categories. The fully annotated DNRTI contains 175220 words. To build a baseline for named entity recognition in the threat intelligence field, we evaluate some deep learning model on DNRTI. Experiments demonstrate that DNRTI well represents the key information in threat intelligence and are quite challenging.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132488293","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: 10