Leonardo Babun (Florida International University), Amit Kumar Sikder (Florida International University), Abbas Acar (Florida International University), Selcuk Uluagac (Florida International University)

In smart environments such as smart homes and offices, the interaction between devices, users, and apps generate abundant data. Such data contain valuable forensic information about events and activities occurring in the smart environment. Nonetheless, current smart platforms do not provide any digital forensic capability to identify, trace, store, and analyze the data produced in these environments. To fill this gap, in this paper, we introduce VeritaS, a novel and practical digital forensic capability for the smart environment. VeritaS has two main components: Collector and Analyzer. The Collector implements mechanisms to automatically collect forensically-relevant data from the smart environment. Then, in the event of a forensic investigation, the Analyzer uses a First Order Markov Chain model to extract valuable and usable forensic information from the collected data. VeritaS then uses the forensic information to infer activities and behaviors from users, devices, and apps that violate the security policies defined for the environment. We implemented and tested VeritaS in a realistic smart office environment with 22 smart devices and sensors that generated 84209 forensically-valuable incidents. The evaluation shows that VeritaS achieves over 95% of accuracy in inferring different anomalous activities and forensic behaviors within the smart environment. Finally, VeritaS is extremely lightweight, yielding no overhead on the devices and minimal overhead in the backend resources (i.e., the cloud servers).

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 55 ) ) ) [post__not_in] => Array ( [0] => 8479 ) )

WIP: Infrastructure-Aided Defense for Autonomous Driving Systems: Opportunities and...

Yunpeng Luo (UC Irvine), Ningfei Wang (UC Irvine), Bo Yu (PerceptIn), Shaoshan Liu (PerceptIn) and Qi Alfred Chen (UC Irvine)

Read More

ROV-MI: Large-Scale, Accurate and Efficient Measurement of ROV Deployment

Wenqi Chen (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Chenxin Duan (Tsinghua University), Xia Yin (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University)

Read More

First, Fuzz the Mutants

Alex Groce (Northern Arizona Univerisity), Goutamkumar Kalburgi (Northern Arizona Univerisity), Claire Le Goues (Carnegie Mellon University), Kush Jain (Carnegie Mellon University), Rahul Gopinath (Saarland University)

Read More

Usability of Cryptocurrency Wallets Providing CoinJoin Transactions

Simin Ghesmati (Uni Wien, SBA Research), Walid Fdhila (Uni Wien, SBA Research), Edgar Weippl (Uni Wien, SBA Research)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)