Describing spatio-temporal behaviors of cyber-physical systems attracts more and more attention in the filed of intelligent transportation systems and biological systems. The major problem is expressiveness and verifiability for modeling and analysis of spatio-temporal behaviors. In order to verify spatial and spatio-temporal behaviors, in this paper, we propose a methodology to model the evolution of spatial scene snapshots and verify the spatio-temporal models. Firstly, we define a novel Topograph through inducing Bigraph in topological space to characterize cyber-physical systems and verify the model against patterns specified with S4u formulas. Secondly, for spatio-temporal verification, we extend Topograph in dense time, named Temporal Topograph, to describe the evolution of spatial objects, which are verified against spatio-temporal specification language. We evaluate the applicability of the approach on CBTC-based intelligent transportation systems.
{"title":"Modeling and Verification of Spatio-Temporal Intelligent Transportation Systems","authors":"Tengfei Li, Xiaohong Chen, Haiying Sun, Jing Liu, Jiajia Yang, Chenchen Yang, Junfeng Sun","doi":"10.1109/TrustCom50675.2020.00081","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00081","url":null,"abstract":"Describing spatio-temporal behaviors of cyber-physical systems attracts more and more attention in the filed of intelligent transportation systems and biological systems. The major problem is expressiveness and verifiability for modeling and analysis of spatio-temporal behaviors. In order to verify spatial and spatio-temporal behaviors, in this paper, we propose a methodology to model the evolution of spatial scene snapshots and verify the spatio-temporal models. Firstly, we define a novel Topograph through inducing Bigraph in topological space to characterize cyber-physical systems and verify the model against patterns specified with S4u formulas. Secondly, for spatio-temporal verification, we extend Topograph in dense time, named Temporal Topograph, to describe the evolution of spatial objects, which are verified against spatio-temporal specification language. We evaluate the applicability of the approach on CBTC-based intelligent transportation systems.","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":"129491289","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00245
Muhammad Shadi Hajar, M. Al-Kadri, H. Kalutarage
Wireless Medical Sensor Networks (WMSNs) offer ubiquitous health applications that enhance patients' quality of life and support national health systems. Detecting internal attacks on WMSNs is still challenging since cryptographic measures can not protect from compromised or selfish sensor nodes. Establishing a trust relationship between sensor nodes is recognized as a promising measure to reinforce the overall security of Wireless Sensor Networks (WSNs). However, the existing trust schemes for WSNs are not necessarily fit for WMSNs due to their different operation, topology, resources limitations, and critical applications. In this paper, the aforementioned factors are regarded, and accordingly, two different methods to evaluate the trust value have been proposed to fit in-body, on-body, and off-body sensor nodes. Our Lightweight Trust Management System (LTMS) provides a further line of defense to detect packet drop attacks launched by compromised or selfish sensor nodes. Moreover, simulation results show that LTMS is more robust against complicated on-off attacks and can significantly reduce the processing overhead.
{"title":"LTMS: A Lightweight Trust Management System for Wireless Medical Sensor Networks","authors":"Muhammad Shadi Hajar, M. Al-Kadri, H. Kalutarage","doi":"10.1109/TrustCom50675.2020.00245","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00245","url":null,"abstract":"Wireless Medical Sensor Networks (WMSNs) offer ubiquitous health applications that enhance patients' quality of life and support national health systems. Detecting internal attacks on WMSNs is still challenging since cryptographic measures can not protect from compromised or selfish sensor nodes. Establishing a trust relationship between sensor nodes is recognized as a promising measure to reinforce the overall security of Wireless Sensor Networks (WSNs). However, the existing trust schemes for WSNs are not necessarily fit for WMSNs due to their different operation, topology, resources limitations, and critical applications. In this paper, the aforementioned factors are regarded, and accordingly, two different methods to evaluate the trust value have been proposed to fit in-body, on-body, and off-body sensor nodes. Our Lightweight Trust Management System (LTMS) provides a further line of defense to detect packet drop attacks launched by compromised or selfish sensor nodes. Moreover, simulation results show that LTMS is more robust against complicated on-off attacks and can significantly reduce the processing overhead.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"9 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":"129841759","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00055
T. Gasiba, U. Lechner, M. Pinto-Albuquerque, Daniel Méndez Fernández
Software needs to be secure, in particular, when deployed to critical infrastructures. Secure coding guidelines capture practices in industrial software engineering to ensure the security of code. This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines. The approach draws on well-established theories of policy compliance, neutralization theory, and security-related stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry. The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study.
{"title":"Awareness of Secure Coding Guidelines in the Industry - A first data analysis","authors":"T. Gasiba, U. Lechner, M. Pinto-Albuquerque, Daniel Méndez Fernández","doi":"10.1109/TrustCom50675.2020.00055","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00055","url":null,"abstract":"Software needs to be secure, in particular, when deployed to critical infrastructures. Secure coding guidelines capture practices in industrial software engineering to ensure the security of code. This study aims to assess the level of awareness of secure coding in industrial software engineering, the skills of software developers to spot weaknesses in software code, avoid them, and the organizational support to adhere to coding guidelines. The approach draws on well-established theories of policy compliance, neutralization theory, and security-related stress and the authors' many years of experience in industrial software engineering and on lessons identified from training secure coding in the industry. The paper presents the questionnaire design for the online survey and the first analysis of data from the pilot study.","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":"130088768","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00048
C. Fan, Xiaolei Dong, Z. Cao, Jiachen Shen
Searchable Symmetric Encryption(SSE) remains to be one of the hot topics in the field of cloud storage technology. However, malicious servers may return incorrect search results intentionally, which will bring significant security risks to users. Therefore, verifiable searchable encryption emerged. In the meantime, single-keyword query limits the applications of searchable encryption. Accordingly, more expressive searchable encryption schemes are desirable. In this paper, we propose a verifiable conjunctive keyword search scheme based on Cuckoo filter (VCKSCF), which significantly reduces verification and storage overhead. Security analysis indicates that the proposed scheme achieves security in the face of indistinguishability under chosen keyword attack and the unforgeability of proofs and search tokens. Meanwhile, the experimental evaluation demonstrates that it achieves preferable performance in real-world settings.
{"title":"VCKSCF: Efficient Verifiable Conjunctive Keyword Search Based on Cuckoo Filter for Cloud Storage","authors":"C. Fan, Xiaolei Dong, Z. Cao, Jiachen Shen","doi":"10.1109/TrustCom50675.2020.00048","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00048","url":null,"abstract":"Searchable Symmetric Encryption(SSE) remains to be one of the hot topics in the field of cloud storage technology. However, malicious servers may return incorrect search results intentionally, which will bring significant security risks to users. Therefore, verifiable searchable encryption emerged. In the meantime, single-keyword query limits the applications of searchable encryption. Accordingly, more expressive searchable encryption schemes are desirable. In this paper, we propose a verifiable conjunctive keyword search scheme based on Cuckoo filter (VCKSCF), which significantly reduces verification and storage overhead. Security analysis indicates that the proposed scheme achieves security in the face of indistinguishability under chosen keyword attack and the unforgeability of proofs and search tokens. Meanwhile, the experimental evaluation demonstrates that it achieves preferable performance in real-world settings.","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":"128861871","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00231
Jinghui Xu, Yu Wen, Chun Yang, Dan Meng
In the face of the increasingly complex Internet environment, the traditional intrusion detection system is difficult to cope with the unknown variety of attacks. People hope to find reliable anomaly detection technology to help improve the security of cyberspace. The rapid development of artificial intelligence technology provides new development opportunities for anomaly detection technology, and the anomaly detection system based on deep learning performs well in some studies. However, neural networks are highly dependent on data quality, and a small number of poisoned samples injected into the data set will have a huge impact on the results. The online abnormal threat detection system based on deep learning is likely to be attacked by poisoning due to the need for continuous data collection and training. We propose a poisoning attack method using adversarial samples to resist the anomaly detection system based on an unsupervised deep neural network, which can destroy the neural network with as few samples as possible. We verified the effectiveness of poisoning attacks on the network security data set of los alamos national laboratory and further demonstrated its generality on other abnormal detection data set.
{"title":"An Approach for Poisoning Attacks against RNN-Based Cyber Anomaly Detection","authors":"Jinghui Xu, Yu Wen, Chun Yang, Dan Meng","doi":"10.1109/TrustCom50675.2020.00231","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00231","url":null,"abstract":"In the face of the increasingly complex Internet environment, the traditional intrusion detection system is difficult to cope with the unknown variety of attacks. People hope to find reliable anomaly detection technology to help improve the security of cyberspace. The rapid development of artificial intelligence technology provides new development opportunities for anomaly detection technology, and the anomaly detection system based on deep learning performs well in some studies. However, neural networks are highly dependent on data quality, and a small number of poisoned samples injected into the data set will have a huge impact on the results. The online abnormal threat detection system based on deep learning is likely to be attacked by poisoning due to the need for continuous data collection and training. We propose a poisoning attack method using adversarial samples to resist the anomaly detection system based on an unsupervised deep neural network, which can destroy the neural network with as few samples as possible. We verified the effectiveness of poisoning attacks on the network security data set of los alamos national laboratory and further demonstrated its generality on other abnormal detection data set.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"30 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":"121622606","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00032
Roberto Dillon, S. Chawla, Dayana Hristova, Barbara Göbl, Suzana Jovicic
To grant password security, it is still a common practice to request users to comply with a number of rules that need to be met for the resulting password to be valid. Users have no option but to comply with the rules, but is there a specific point where the required rules start being perceived as a nuisance and thus jeopardize security? This paper addresses users' reactions to such a scenario by means of an online survey ($mathrm{N}=51$) where users are being asked to create a password following an increasing number of restrictions. We thereby follow their evolving responses as each further criterion is added. Our analysis confirms that the increase in rule complexity has detrimental effects on usability and can lead to workarounds potentially compromising password security.
{"title":"Password Policies vs. Usability: When Do Users Go “Bananas”?","authors":"Roberto Dillon, S. Chawla, Dayana Hristova, Barbara Göbl, Suzana Jovicic","doi":"10.1109/TrustCom50675.2020.00032","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00032","url":null,"abstract":"To grant password security, it is still a common practice to request users to comply with a number of rules that need to be met for the resulting password to be valid. Users have no option but to comply with the rules, but is there a specific point where the required rules start being perceived as a nuisance and thus jeopardize security? This paper addresses users' reactions to such a scenario by means of an online survey ($mathrm{N}=51$) where users are being asked to create a password following an increasing number of restrictions. We thereby follow their evolving responses as each further criterion is added. Our analysis confirms that the increase in rule complexity has detrimental effects on usability and can lead to workarounds potentially compromising password security.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"220 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":"122520038","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00096
M. Alishahi, Nicola Zannone
Classifiers are often trained over data collected from different sources. Sharing their data with other entities, however, can raise privacy concerns for data owners. To protect data confidentiality while being able to train a classifier, effective solutions have been proposed in the literature to construct various types of classifiers over private data. However, to date an analysis and comparison of the computation and communication costs for the construction of classifiers over private data is missing, making it difficult to determine which classifier can be used in a given application domain. In this work, we show how two well-known classifiers (Naive Bayes and SVM classifiers) can be securely build over private inputs, and evaluate their construction costs. We assess the computation and communication costs for training the classifiers both theoretically and empirically for different benchmark datasets.
{"title":"On the Comparison of Classifiers' Construction over Private Inputs","authors":"M. Alishahi, Nicola Zannone","doi":"10.1109/TrustCom50675.2020.00096","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00096","url":null,"abstract":"Classifiers are often trained over data collected from different sources. Sharing their data with other entities, however, can raise privacy concerns for data owners. To protect data confidentiality while being able to train a classifier, effective solutions have been proposed in the literature to construct various types of classifiers over private data. However, to date an analysis and comparison of the computation and communication costs for the construction of classifiers over private data is missing, making it difficult to determine which classifier can be used in a given application domain. In this work, we show how two well-known classifiers (Naive Bayes and SVM classifiers) can be securely build over private inputs, and evaluate their construction costs. We assess the computation and communication costs for training the classifiers both theoretically and empirically for different benchmark datasets.","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":"120874372","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00243
Shin-Ying Huang, Tao Ban
Publicly available software vulnerabilities and exploit code are often abused by malicious actors to launch cyberattacks to vulnerable targets. Organizations not only have to update their software to the latest versions, but do effective patch management and prioritize security-related patching as well. In addition to intelligence sources such as Computer Emergency Response Team (CERT) alerts, cybersecurity news, national vulnerability database (NBD), and commercial cybersecurity vendors, social media is another valuable source that facilitates early stage intelligence gathering. To early detect future cyber threats based on publicly available resources on the Internet, we propose a dynamic vulnerability-threat assessment model to predict the tendency to be exploited for vulnerability entries listed in Common Vulnerability Exposures, and also to analyze social media contents such as Twitter to extract meaningful information. The model takes multiple aspects of vulnerabilities gathered from different sources into consideration. Features range from profile information to contextual information about these vulnerabilities. For the social media data, this study leverages machine learning techniques specially for Twitter which helps to filter out non-cybersecurity-related tweets and also label the topic categories of each tweet. When applied to predict the vulnerabilities exploitation and analyzed the real-world social media discussion data, it showed promising prediction accuracy with purified social media intelligence. Moreover, the AI-enabling modules have been deployed into a threat intelligence platform for further applications.
{"title":"Monitoring Social Media for Vulnerability-Threat Prediction and Topic Analysis","authors":"Shin-Ying Huang, Tao Ban","doi":"10.1109/TrustCom50675.2020.00243","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00243","url":null,"abstract":"Publicly available software vulnerabilities and exploit code are often abused by malicious actors to launch cyberattacks to vulnerable targets. Organizations not only have to update their software to the latest versions, but do effective patch management and prioritize security-related patching as well. In addition to intelligence sources such as Computer Emergency Response Team (CERT) alerts, cybersecurity news, national vulnerability database (NBD), and commercial cybersecurity vendors, social media is another valuable source that facilitates early stage intelligence gathering. To early detect future cyber threats based on publicly available resources on the Internet, we propose a dynamic vulnerability-threat assessment model to predict the tendency to be exploited for vulnerability entries listed in Common Vulnerability Exposures, and also to analyze social media contents such as Twitter to extract meaningful information. The model takes multiple aspects of vulnerabilities gathered from different sources into consideration. Features range from profile information to contextual information about these vulnerabilities. For the social media data, this study leverages machine learning techniques specially for Twitter which helps to filter out non-cybersecurity-related tweets and also label the topic categories of each tweet. When applied to predict the vulnerabilities exploitation and analyzed the real-world social media discussion data, it showed promising prediction accuracy with purified social media intelligence. Moreover, the AI-enabling modules have been deployed into a threat intelligence platform for further applications.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"58 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":"121232988","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00071
Minghui Li, Yang Li, Liming Fang
Mobile Crowd-Sensing (MCS) is gradually extended to the edge network to reduce the delay of data transmission and improve the ability of data processing. However, a challenge is that there are still loopholes in the protection of privacy data, especially in location-based services. The attacker can reconstruct the location relationship network among the correlation about the environment information, identity information, and other sensing data provided by mobile users. Moreover, in the edge environment, this kind of attack is more accurate and more threatening to the location privacy information. To solve this problem, we propose a location privacy protection scheme (ELPPS) for a mobile crowd-sensing network in the edge environment, to protect the position correlation weight between sensing users through differential privacy. We use the grid anonymous algorithm to confuse the location information in order to reduce the computing cost of edge nodes. The experiment results show that the proposed framework can effectively protect the location information of the sensing users without reducing the availability of the sensing task results, and has a low delay.
{"title":"ELPPS: An Enhanced Location Privacy Preserving Scheme in Mobile Crowd-Sensing Network Based on Edge Computing","authors":"Minghui Li, Yang Li, Liming Fang","doi":"10.1109/TrustCom50675.2020.00071","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00071","url":null,"abstract":"Mobile Crowd-Sensing (MCS) is gradually extended to the edge network to reduce the delay of data transmission and improve the ability of data processing. However, a challenge is that there are still loopholes in the protection of privacy data, especially in location-based services. The attacker can reconstruct the location relationship network among the correlation about the environment information, identity information, and other sensing data provided by mobile users. Moreover, in the edge environment, this kind of attack is more accurate and more threatening to the location privacy information. To solve this problem, we propose a location privacy protection scheme (ELPPS) for a mobile crowd-sensing network in the edge environment, to protect the position correlation weight between sensing users through differential privacy. We use the grid anonymous algorithm to confuse the location information in order to reduce the computing cost of edge nodes. The experiment results show that the proposed framework can effectively protect the location information of the sensing users without reducing the availability of the sensing task results, and has a low delay.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"232 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":"116324476","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}
Pub Date : 2020-12-01DOI: 10.1109/TrustCom50675.2020.00189
Syed Ghazanfar Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad, G. Shah, K. Zafar
In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.
{"title":"IoT-Sphere: A Framework To Secure IoT Devices From Becoming Attack Target And Attack Source","authors":"Syed Ghazanfar Abbas, M. Husnain, U. U. Fayyaz, F. Shahzad, G. Shah, K. Zafar","doi":"10.1109/TrustCom50675.2020.00189","DOIUrl":"https://doi.org/10.1109/TrustCom50675.2020.00189","url":null,"abstract":"In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.","PeriodicalId":221956,"journal":{"name":"2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)","volume":"27 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":"127802465","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：