1.
Leonardo Babun, Amit Kumar Sikder, Abbas Acar, A. Selcuk Uluagac
The Truth Shall Set Thee Free: Enabling Practical Forensic Capabilities in Smart Environments Conference Paper
In the Proceedings of the 30th Network and Distributed System Security Symposium (NDSS), 2022.
Abstract | Links | BibTeX | Tags: Forensics, IoT Security
@conference{babun2022truth,
title = {The Truth Shall Set Thee Free: Enabling Practical Forensic Capabilities in Smart Environments},
author = {Leonardo Babun and Amit Kumar Sikder and Abbas Acar and A. Selcuk Uluagac},
url = {https://www.ndss-symposium.org/wp-content/uploads/2022-133-paper.pdf},
year = {2022},
date = {2022-04-01},
urldate = {2022-04-01},
booktitle = {In the Proceedings of the 30th Network and Distributed System Security Symposium (NDSS)},
abstract = {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 percent 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 (ie, the cloud servers).},
keywords = {Forensics, IoT Security},
pubstate = {published},
tppubtype = {conference}
}
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 percent 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 (ie, the cloud servers).
2.
Michael Thompson, Suat Mercan, Mumin Cebe, Kemal Akkaya, Arif Selcuk Uluagac
Cost-efficient IoT Forensics Framework with Blockchain Patent
US Patent, 2021.
Abstract | Links | BibTeX | Tags: Forensics, IoT Security
@patent{Thompson2021CostEfficient,
title = {Cost-efficient IoT Forensics Framework with Blockchain},
author = {Michael Thompson and Suat Mercan and Mumin Cebe and Kemal Akkaya and Arif Selcuk Uluagac},
url = {https://patentscope.wipo.int/search/en/detail.jsf?docId=US336296346},
year = {2021},
date = {2021-09-21},
urldate = {2021-09-21},
booktitle = {US Patent},
pages = {332–333},
publisher = {Association for Computing Machinery},
series = {WiSec '19},
abstract = {A cost-effective and reliable digital forensics framework is provided by exploiting multiple blockchain networks in two levels. The selected data collected from sensors on a boat is sent to a remote company database and calculated hash of the data is saved in two blockchain platforms in the first level. Hash of each block is retrieved and inserted onto a Merkle tree on a periodic basis to be stored on another blockchain in the second level which is used to detect any error in the first level blockchains. A secure platform is created with the combination of several blockchains.},
howpublished = {US Patent},
keywords = {Forensics, IoT Security},
pubstate = {published},
tppubtype = {patent}
}
A cost-effective and reliable digital forensics framework is provided by exploiting multiple blockchain networks in two levels. The selected data collected from sensors on a boat is sent to a remote company database and calculated hash of the data is saved in two blockchain platforms in the first level. Hash of each block is retrieved and inserted onto a Merkle tree on a periodic basis to be stored on another blockchain in the second level which is used to detect any error in the first level blockchains. A secure platform is created with the combination of several blockchains.
3.
Leonardo Babun, Amit K. Sikder, Abbas Acar, A. Selcuk Uluagac
A Digital Forensics Framework for Smart Settings: Poster Conference Paper
In the Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), 2019.
Abstract | Links | BibTeX | Tags: Forensics, IoT Security
@conference{1Babun2019DFFramework,
title = {A Digital Forensics Framework for Smart Settings: Poster},
author = {Leonardo Babun and Amit K. Sikder and Abbas Acar and A. Selcuk Uluagac},
url = {https://doi.org/10.1145/3317549.3326317},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
publisher = {In the Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks (WiSec)},
abstract = {Users utilize IoT devices and sensors in a co-operative manner to enable the concept of a smart environment. This integration generate data with high forensic value. Nonetheless, current smart app programming platforms do not provide any digital forensics capability to identify, trace, store, and analyze the data produced in these settings. To overcome these limitations, in this poster, we present our ongoing work to introduce a novel digital forensic framework for a smart environment.},
keywords = {Forensics, IoT Security},
pubstate = {published},
tppubtype = {conference}
}
Users utilize IoT devices and sensors in a co-operative manner to enable the concept of a smart environment. This integration generate data with high forensic value. Nonetheless, current smart app programming platforms do not provide any digital forensics capability to identify, trace, store, and analyze the data produced in these settings. To overcome these limitations, in this poster, we present our ongoing work to introduce a novel digital forensic framework for a smart environment.
4.
Mumin Cebe, Enes Erdin, Kemal Akkaya, Hidayet Aksu, Selcuk Uluagac
Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles Journal Article
IEEE Communications Magazine, 2018.
Abstract | Links | BibTeX | Tags: Blockchain Security, Forensics, IoT Security
@article{cebe2018block4forensic,
title = {Block4forensic: An integrated lightweight blockchain framework for forensics applications of connected vehicles},
author = {Mumin Cebe and Enes Erdin and Kemal Akkaya and Hidayet Aksu and Selcuk Uluagac},
url = {https://ieeexplore.ieee.org/document/8493118},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {IEEE Communications Magazine},
publisher = {IEEE},
abstract = {Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.},
keywords = {Blockchain Security, Forensics, IoT Security},
pubstate = {published},
tppubtype = {article}
}
Today's vehicles are becoming cyber-physical systems that not only communicate with other vehicles but also gather various information from hundreds of sensors within them. These developments help create smart and connected (e.g., self-driving) vehicles that will introduce significant information to drivers, manufacturers, insurance companies, and maintenance service providers for various applications. One such application that is becoming crucial with the introduction of self-driving cars is forensic analysis of traffic accidents. The utilization of vehicle-related data can be instrumental in post-accident scenarios to discover the faulty party, particularly for self-driving vehicles. With the opportunity of being able to access various information in cars, we propose a permissioned blockchain framework among the various elements involved to manage the collected vehicle-related data. Specifically, we first integrate vehicular public key infrastructure (VPKI) to the proposed blockchain to provide membership establishment and privacy. Next, we design a fragmented ledger that will store detailed data related to vehicles such as maintenance information/ history, car diagnosis reports, and so on. The proposed forensic framework enables trustless, traceable, and privacy-aware post-accident analysis with minimal storage and processing overhead.
Citations: 8413
h-index: 44
i10-index: 107
