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Rwitam Bandyopadhyay

dblp:385/6714 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cyber-physical and IoT security · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cyber-physical and IoT security › robot security
robotic vehicle security
0.812024
A Systematic Study of Physical Sensor Attack Hardness · SP 2024
Embedded and real-time systems
cyber-physical system platforms
0.212024
A Systematic Study of Physical Sensor Attack Hardness · SP 2024

Methods — techniques the papers use, named apart from their topics

prerequisite analysis · 1.5attack parameter tuning · 1.5
YearPublicationVenuePosition
2024 A Systematic Study of Physical Sensor Attack Hardness
abstract
Physical sensor attacks against robotic vehicles (RV) have become a serious concern due to their prevalence and potential physical threat. However, RV software developers often do not deploy appropriate countermeasures. This hesitance stems from their belief that attackers face substantial challenges when conducting sensor attacks, e.g., nullifying sensor redundancy in hardware and circumventing sensor filters in software. Yet, we discover that attackers can overcome the challenges by fulfilling specific prerequisites and finely tuning attack parameters. The misconceptions that the developers have arisen from a lack of study regarding the level of difficulty attackers face in successfully achieving their attack goals, which we call "attack hardness".In this paper, we examine the hardness of 12 well-known sensor attacks. We first identify the prerequisites required to conduct the attacks successfully. We then quantify the hardness of each attack as how frequent the prerequisites enabling a specific attack are in the real world. To automate this analysis, we introduce RVPROBER, an attack prerequisite analysis framework. RVPROBER discovered that the 12 sensor attacks require, on average, 4.4 prerequisites, highlighting that previous literature has often missed important details required to perform these attacks. By satisfying the identified prerequisites and tuning attack parameters, we increased the number of successful attacks from 6 to 11. Moreover, our analysis showed that an average of 57.08% of actual RV users are vulnerable to sensor attacks. Finally, starting from the identified prerequisites, we analyzed the reasons behind the success of each attack and found previously-unknown root causes, such as design flaws in the RV software’s fail-safe logic.
Hyungsub Kim, Rwitam Bandyopadhyay, Muslum Ozgur Ozmen, Z. Berkay Celik, Antonio Bianchi, Yongdae Kim, Dongyan Xu
SP2