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Filters: Author is Valente, Junia  [Clear All Filters]
2018-02-27
Valente, Junia, Cardenas, Alvaro A..  2017.  Security & Privacy in Smart Toys. Proceedings of the 2017 Workshop on Internet of Things Security and Privacy. :19–24.

We analyze the security practices of three smart toys that communicate with children through voice commands. We show the general communication architecture, and some general security and privacy practices by each of the devices. Then we focus on the analysis of one particular toy, and show how attackers can decrypt communications to and from a target device, and perhaps more worryingly, the attackers can also inject audio into the toy so the children listens to any arbitrary audio file the attacker sends to the toy. This last attack raises new safety concerns that manufacturers of smart toys should prevent.

2017-04-03
Urbina, David I., Giraldo, Jairo A., Cardenas, Alvaro A., Tippenhauer, Nils Ole, Valente, Junia, Faisal, Mustafa, Ruths, Justin, Candell, Richard, Sandberg, Henrik.  2016.  Limiting the Impact of Stealthy Attacks on Industrial Control Systems. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1092–1105.

While attacks on information systems have for most practical purposes binary outcomes (information was manipulated/eavesdropped, or not), attacks manipulating the sensor or control signals of Industrial Control Systems (ICS) can be tuned by the attacker to cause a continuous spectrum in damages. Attackers that want to remain undetected can attempt to hide their manipulation of the system by following closely the expected behavior of the system, while injecting just enough false information at each time step to achieve their goals. In this work, we study if attack-detection can limit the impact of such stealthy attacks. We start with a comprehensive review of related work on attack detection schemes in the security and control systems community. We then show that many of those works use detection schemes that are not limiting the impact of stealthy attacks. We propose a new metric to measure the impact of stealthy attacks and how they relate to our selection on an upper bound on false alarms. We finally show that the impact of such attacks can be mitigated in several cases by the proper combination and configuration of detection schemes. We demonstrate the effectiveness of our algorithms through simulations and experiments using real ICS testbeds and real ICS systems.