Remotely Exploiting AT Command Attacks on ZigBee Networks

Vaccari, Ivan; Cambiaso, Enrico; Aiello, Maurizio

doi:10.1155/2017/1723658

Cited by 22 publications

(13 citation statements)

References 36 publications

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“…Concerning ZigBee attacks, ad-hoc security testing tools are proposed by [60], while [61] introduces the KillerBee framework, able to execute a wide range of attacks against ZigBee networks. Also, [62] introduces an innovative attack to ZigBee sensor devices, exploiting remote AT commands. Concerning the tools proposed in [60], due to hardware limitations, we do not adopted the proposed software for our tests.…”

Section: Internet Of Things Network May Adopt Different Communicatiomentioning

confidence: 99%

Evaluating Security of Low-Power Internet of Things Networks

al.¹

2019

IJCDS

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Internet of Things (IoT) is one of the most prominent technologies on the Internet. Simple objects gain the ability to store, process and exchange information among themselves or with external entities, by observing and controlling the environment. Thanks to the rapid development of this innovation, IoT opens possibilities to a huge number of objects and applications that promise to improve our daily life. The main scenarios of the development of IoT are home automation/domestic and Industrial IoT. According to such scenarios, several applications could be implemented: from smart thermostats, light bulbs, refrigerators, ovens, door window sensors to volumetric, flow, heat and connected data processing devices. Since this is a new phenomenon, it has not yet been studied and analyzed for its entirety, also due to the lack of a definitive standard that can provide an overview of these devices. The objective of this paper is to implement different well-known attacks against IoT networks, by adopting the ZigBee communication protocol to analyze devices and network security. For our aim, we have considered different scenarios involving an attacker aiming to dismantle the IoT network (jamming, flooding DoS), retrieve sensitive information (sniffing, brute force password crack) and to actively communicate on the network to impersonate legitimate nodes (replay). Such exploitation provides us the ability to analyze the effects of attacks designed to target common wireless networks, when they are perpetrated against IoT environments. Obtained results prove that IoT devices and networks (often embedded in sensitive environments such as hospitals or critical infrastructures) are vulnerable to several attacks.

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Section: Internet Of Things Network May Adopt Different Communicatiomentioning

confidence: 99%

Evaluating Security of Low-Power Internet of Things Networks

al.¹

2019

IJCDS

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“…The ZigBee protocol was created with security considerations in mind, but consumer demand for cheap devices with long life expectancy often caused vendors to sacrifice security, which led to poor implementation of the protocol [49]; this, in turn, led to major security issues such as data compromising or information sniffing. [41]. For example, Vaccari et al [41] focused on the security aspects of the ZigBee protocol.…”

Section: Short Range Communication Protocolsmentioning

confidence: 99%

“…[41]. For example, Vaccari et al [41] focused on the security aspects of the ZigBee protocol. The study identified important security issues and presented an attack on the protocol which enabled the attacker to compromise the data transferring in the network.…”

Section: Short Range Communication Protocolsmentioning

confidence: 99%

“…With respect to hardening IoT security, most prior research focuses on the security of individual IoT devices [20,32,47], the security of an IoT protocol [24,33,41,43,49], or the the security of a network that consists solely of IoT devices [12,16,39,46] (see Section 3.1 for more details). To the best our knowledge, there is no previous related research aimed at identifying the optimal (security risk-wise) deployment of devices within the physical space.…”

Section: Introductionmentioning

confidence: 99%

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Deployment optimization of IoT devices through attack graph analysis

Agmon

Shabtai

Puzis

2019

Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks

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The Internet of things (IoT) has become an integral part of our life at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional opportunities for the lateral movement of an attacker within an organization. Thus, the type and location of IoT devices may significantly change the level of network security of the organizational network. In this paper, we quantify the level of network security based on an augmented attack graph analysis that accounts for the physical location of IoT devices and their communication capabilities. We use the depth-first branch and bound (DFBnB) heuristic search algorithm to solve two optimization problems: Full Deployment with Minimal Risk (FDMR) and Maximal Utility without Risk Deterioration (MURD). An admissible heuristic is proposed to accelerate the search. The proposed method is evaluated using a real network with simulated deployment of IoT devices. The results demonstrate (1) the contribution of the augmented attack graphs to quantifying the impact of IoT devices deployed within the organization on security, and (2) the effectiveness of the optimized IoT deployment. CCS CONCEPTS• Security and privacy → Distributed systems security; Mobile and wireless security.

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“…Unmanaged devices have been the cause of well known attacks [27] and its security vulnerabilities are being unceasingly disclosed [28]. The IoT devices are exposing vulnerabilities from the network stack to the software stack [29]. A dysfunctional device can cause the failure of a whole IoT-based system.…”

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confidence: 99%

Collective Attestation for Manageable IoT Environments

Lee

2018

Applied Sciences

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The swarm attestation methods have been proposed to detect illegitimate modifications in a large network efficiently. However, they do not provide the scalable identification of detected devices, which is critical to keep a swarm network trustworthy in the practical uses. In this paper, we propose a lightweight attestation method with efficient scalable identification of target devices. The proposed approach, called CAMIE (Collective Attestation for Manageable IoT Environments), combines binary-embedded tag generation and regional reports to build the region-based attestation result. During the attestation process, the swarm network generates merged region summaries. An authorized verifier can infer the individual device information from the regional attestation results. In this way, CAMIE can achieve the scalable identification of compromised devices with significantly less overhead. Memory (ROM) and Memory Protection Unit (MPU), to build a minimal hardware trust anchor [14][15][16][17]. The swarm attestation [18][19][20][21][22][23][24][25] is based on this approach. It efficiently checks whether a large number of devices are in good software configurations (without any modifications). The methods for swarm attestation have mainly focused on detection of integrity violation of devices with efficiency [18,19], DoS-attack resiliency [22], verifiable aggregation [21], physical attacks [20,23], execution trace [24] or device property [25].However, in the real-world situations, IoT swarms are being faced with more hostile environments than that assumed in the previous approaches. We have already observed the result of the outbreak of large-scale malware (e.g., Mirai Botnet [2]), where the phenomenon follows the malware-infection epidemic model [26]. It showed that how fast a group of locally connected devices can be infected at the first stage. The previous methods already provide a way to identify compromised devices during attestation process [19,21]. Unfortunately, they are not scalable for such large-scale infections. The overhead caused by indicating compromised devices individually overwhelms the attestation process.In the view of the network operators, the efficient maintenance is a key to keep a swarm network trustworthy. However, the maintenance supports in the attestation process have not yet been paid attention to enough. Outdated devices are dangerous. Unmanaged devices have been the cause of well known attacks [27] and its security vulnerabilities are being unceasingly disclosed [28]. The IoT devices are exposing vulnerabilities from the network stack to the software stack [29]. A dysfunctional device can cause the failure of a whole IoT-based system. Furthermore, although each device has limited capabilities, recent studies [30,31] show that the compromised devices can be organized into a mobile botnet conducting more sophisticated attacks. However, more than 200,000 devices in 2017 still require a software patch against the Heartbleed attack that occurred in 2014 [32]. Therefore, in this paper, we pro...

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Remotely Exploiting AT Command Attacks on ZigBee Networks

Cited by 22 publications

References 36 publications

Evaluating Security of Low-Power Internet of Things Networks

Evaluating Security of Low-Power Internet of Things Networks

Deployment optimization of IoT devices through attack graph analysis

Collective Attestation for Manageable IoT Environments

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