A Novel 6LoWPAN-ND Extension to Enhance Privacy in IEEE 802.15.4 Networks

Brilli, Luca; Pecorella, Tommaso; Pierucci, Laura; Fantacci, Romano

doi:10.1109/glocom.2016.7841523

Cited by 14 publications

(7 citation statements)

References 13 publications

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“…The adopted countermeasure will have total cost less or equal to the damage cost: in our example, when an attack is detected, we can change the MAC-16 (MAC short) [19,22] address of our devices and, if a new attack is perform, we can modify the routing algorithm tree with a complete network reconfiguration.…”

Section: Resultsmentioning

confidence: 99%

“…As an example, if in a Fog/IoT network there are several temperature sensors and only one is under attack, we can evaluate if we can accept the risk that the attack propagates and simply apply a low-cost countermeasure by discarding data from the device under attack or by isolating the node and by reauthenticating it. Instead, if the device is a central node which routes data towards the FU, we need, e.g., to apply network reconfiguration with higher time and energy costs [19]. Therefore, the countermeasure must be correlated to the attack according to the assessed risk outcomes.…”

Section: Attack Tree and Attack Modelsmentioning

confidence: 99%

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False Data Detection for Fog and Internet of Things Networks

Fantacci

Nizzi

Pecorella

et al. 2019

Sensors

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The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities, and processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.

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Section: Resultsmentioning

confidence: 99%

Section: Attack Tree and Attack Modelsmentioning

confidence: 99%

False Data Detection for Fog and Internet of Things Networks

Fantacci

Nizzi

Pecorella

et al. 2019

Sensors

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“…The authors in [24] provided a method based on an exten-sion of IPv6 over Low power Wireless Personal Area Network -Neighbor Discovery (6LoWPAN-ND) to change both L2 and L3 addresses, while maintaining the header compression (as well as all other functionalities of the 6LoWPAN) and a small routing table. The technique presented in this paper, with respect to [24], maintains all the original benefits, reducing the network overhead to zero for small to medium size networks. Moreover, the active sessions and routing path management during the address renew are greatly simplified.…”

Section: Related Workmentioning

confidence: 99%

“…The address reconfiguration approach suggested in [24] is to start the reconfiguration from the tree leafs. However, this solution requires an exact topology knowledge and an additional coordination among the nodes.…”

Section: Use Case: Applying Asha To Lr-wpanmentioning

confidence: 99%

IoT Security via Address Shuffling: The Easy Way

Nizzi

Pecorella

Esposito

et al. 2019

IEEE Internet Things J.

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Securing Internet of Things (IoT) devices and protecting their applications from privacy leaks is a challenge, due to their weak (computational and storage) capabilities, and their proximity with sensitive data. Considering the resourceconstrains of such devices, their long lifetime, and the intermittent connections, classical security approaches are often too difficult or impractical to apply. Moving Target Defense is an established technique whose goal is to lower the attack surface to malicious users by constantly modifying device footprint. Changing the address to an IoT device without privacy leaks is, however, a nontrivial task. In this paper, we propose a novel method to perform a network-wide (IP and MAC) address shuffling procedure, called Address Shuffling Algorithm with HMAC (AShA), which is simple to implement, and whose network overhead is minimal. To demonstrate its effectiveness, we analyze our approach via theoretical analysis and simulations. Our analysis shows how AShA parameters can be adapted to various network sizes while our simulations results show how AShA can be used to successfully perform a global collision-free address renewal on networks of more than 2000 nodes using 16-bit addresses.

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“…A gateway architecture is chosen as the most appropriate for resource-constrained devices and for high system availability. In [16] the authors propose a cross layer method to overcome the traceability of the user in smart home networks 6LoWPAN . In this case, the IEEE 802.15.4 standards [17] foresees to cipher the payload but leaves out the headers containing the layer 2 addresses of the source and destination of the packets.…”

Section: Related Workmentioning

confidence: 99%

“Network Sentiment” Framework to Improve Security and Privacy for Smart Home

2018

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A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalls and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and on going threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g., the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT sensors, Wi-Fi and Ethernet connections.

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A Novel 6LoWPAN-ND Extension to Enhance Privacy in IEEE 802.15.4 Networks

Cited by 14 publications

References 13 publications

False Data Detection for Fog and Internet of Things Networks

False Data Detection for Fog and Internet of Things Networks

IoT Security via Address Shuffling: The Easy Way

“Network Sentiment” Framework to Improve Security and Privacy for Smart Home

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