Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk

Agarwal, Sharad; Oser, Pascal; Lueders, Stefan

doi:10.3390/s19194107

Cited by 20 publications

(22 citation statements)

References 9 publications

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“…The term 10 6 in Equation ( 4) just simplifies Equation (3) by converting time (or) latency (i.e., time for one PM) from microseconds to seconds. The time (or) latency mentioned in column 6 of Tables 4 and 5 is calculated by using Equation (5). time (or) latency = required clock cycles (CCs) operational clock f requency (in MHz)…”

Section: Performance Metricmentioning

confidence: 99%

“…Due to this enormous communication of IoT devices through a cloud, they are subject to malicious attacks [4]. Security concerns arise, and therefore various threats and attacks may occur as data owners have no control over the data management [5]. Consequently, the importance of data security and the availability of limited resources provoke us to explore recent low-complexity cryptographic schemes [6].…”

Section: Introductionmentioning

confidence: 99%

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A Low-Complexity Edward-Curve Point Multiplication Architecture

et al. 2021

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The Binary Edwards Curves (BEC) are becoming more and more important, as compared to other forms of elliptic curves, thanks to their faster operations and resistance against side channel attacks. This work provides a low-complexity architecture for point multiplication computations using BEC over GF(2233). There are three major contributions in this article. The first contribution is the reduction of instruction-level complexity for unified point addition and point doubling laws by eliminating multiple operations in a single instruction format. The second contribution is the optimization of hardware resources by minimizing the number of required storage elements. Finally, the third contribution is to reduce the number of required clock cycles by incorporating a 32-bit finite field digit-parallel multiplier in the datapath. As a result, the achieved throughput over area ratio over GF(2233) on Virtex-4, Virtex-5, Virtex-6 and Virtex-7 Xilinx FPGA (Field Programmable Gate Array) devices are 2.29, 19.49, 21.5 and 20.82, respectively. Furthermore, on the Virtex-7 device, the required computation time for one point multiplication operation is 18 µs, while the power consumption is 266 mW. This reveals that the proposed architecture is best suited for those applications where the optimization of both area and throughput parameters are required at the same time.

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Section: Performance Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Edward-Curve Point Multiplication Architecture

et al. 2021

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“…The attacks met on this layer include physical cloning (counterfeiting) [ 9 , 21 , 22 ], malware, spyware [ 9 ] and physical attacks (intentional damage to the device, theft [ 9 ] especially for those deployed to work autonomously [ 23 ]). Agarwal et al [ 24 ] developed two tools to detect IoT devices in heterogeneous networks and to retrieve device-related data (manufacturer, model, firmware version) and they concluded that many IoT devices manufacturers do not secure the devices at all. When referring to IoT devices, one cannot implement the standard heavyweight, complex techniques [ 19 ] to secure the devices belonging to the layer.…”

Section: Security In the Internet Of Thingsmentioning

confidence: 99%

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications

Vulpe

Crăciunescu

Drăgulinescu

et al. 2021

Sensors

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Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

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“…The use of scanners to find devices of a certain type is illustrated in [12] where researchers show how Shodan, Masscan and Nmap can be used to find specific IoT models that are vulnerable. Similarly, researchers analyzed the network of CERN, trying to identify IoT devices using web-scraping [1]. They identified all devices and profiled them, first by scanning their open ports, and then by scraping their web interface when they had one.…”

Section: Related Workmentioning

confidence: 99%

NURSE: eNd-UseR IoT malware detection tool for Smart homEs

d'Estalenx

Gañán

2021

Proceedings of the 11th International Conference on the Internet of Things

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IoT devices keep entering our homes with the promise of delivering more services and enhancing user experience; however, these new devices also carry along an alarming number of vulnerabilities and security issues. In most cases, the users of these devices are completely unaware of the security risks that connecting these devices entail. Current tools do not provide users with essential security information such as whether a device is infected with malware. Traditional techniques to detect malware infections were not meant to be used by the end-user and current malware removal tools and security software cannot handle the heterogeneity of IoT devices. In this paper, we design, develop, and evaluate a tool, called NURSE, to fill this information gap, i.e., enabling end-users to detect IoT-malware infections in their home networks. NURSE follows a modular approach to analyze IoT traffic as captured by means of an ARP spoofing technique which does not require any network modification or specific hardware. Thus, NURSE provides zeroconfiguration IoT traffic analysis within everybody's reach. After testing NURSE in 83 different IoT network scenarios with a wide variety of IoT device types, results show that NURSE identifies malware-infected IoT devices with high-accuracy (86.7%) using device network behaviour and contacted destinations. CCS CONCEPTS• Security and privacy → Intrusion/anomaly detection and malware mitigation; Usability in security and privacy.

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Detecting IoT Devices and How They Put Large Heterogeneous Networks at Security Risk

Cited by 20 publications

References 9 publications

A Low-Complexity Edward-Curve Point Multiplication Architecture

A Low-Complexity Edward-Curve Point Multiplication Architecture

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications

NURSE: eNd-UseR IoT malware detection tool for Smart homEs

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