TY - GEN
T1 - Bluetooth Intrusion Detection System (BIDS)
AU - Satam, Pratik
AU - Satam, Shalaka
AU - Hariri, Salim
N1 - Funding Information:
ACKNOWLEDGMENT This work is partly suoptedrbypthe Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA9550-1-1-8 0427, Natioal nSciene Fcodution an(NSF) research poecrtsj NSF-1624668 and SES-131, and4Thom6son3Reut1ers in the framework of the Partner University Fund (PUF) project (PUF is a proamgof thre French Embassy in the United States and the FACE Fonutiondandais supted bpyAmo ericarndoos and rn the French geronmenvt).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - With the rapid deployment of IOT devices, Bluetooth networks, which form Personal Area Networks(PAN), have become the wireless network of choice for small range/indoor communications networks. Bluetooth is widely used to deliver audio streams (e.g.: Bluetooth headphones, Music systems in cars), connecting peripherals devices to more powerful devices (e.g.: keyboards to computers), connecting wearable technology like smart watches, heart monitors and fitness trackers. It's imperative that Bluetooth networks (like other wireless networks) are secure against cyberattacks such as Man In The Middle Attacks(MITM), Denial of Service attacks(DoS), etc. Moreover, Bluetooth is used heavily in mobile devices/ sensors, and consequently they become sensitive to battery utilization attacks; this type of attacks requires the Bluetooth devices to be secure against different battery draining attacks. As a part of this paper we present an anomaly-based intrusion detection system for Bluetooth networks; Bluetooth IDS (BIDS). The BIDS use an n-gram based approach to characterize the normal behavior of the Bluetooth protocol. Smoothing techniques like Jelinek-Mercer smoothing was used to improve the machine learning algorithm used for detecting abnormal Bluetooth operations. Machine learning algorithms like C4.5, AdaBoostMl, SVM, Naïve Bayes, RIPPER, Bagging were used to build the behavior models for the Bluetooth protocol. The developed models had high accuracy with precision up to 99.6% and recall up to 99.6%.
AB - With the rapid deployment of IOT devices, Bluetooth networks, which form Personal Area Networks(PAN), have become the wireless network of choice for small range/indoor communications networks. Bluetooth is widely used to deliver audio streams (e.g.: Bluetooth headphones, Music systems in cars), connecting peripherals devices to more powerful devices (e.g.: keyboards to computers), connecting wearable technology like smart watches, heart monitors and fitness trackers. It's imperative that Bluetooth networks (like other wireless networks) are secure against cyberattacks such as Man In The Middle Attacks(MITM), Denial of Service attacks(DoS), etc. Moreover, Bluetooth is used heavily in mobile devices/ sensors, and consequently they become sensitive to battery utilization attacks; this type of attacks requires the Bluetooth devices to be secure against different battery draining attacks. As a part of this paper we present an anomaly-based intrusion detection system for Bluetooth networks; Bluetooth IDS (BIDS). The BIDS use an n-gram based approach to characterize the normal behavior of the Bluetooth protocol. Smoothing techniques like Jelinek-Mercer smoothing was used to improve the machine learning algorithm used for detecting abnormal Bluetooth operations. Machine learning algorithms like C4.5, AdaBoostMl, SVM, Naïve Bayes, RIPPER, Bagging were used to build the behavior models for the Bluetooth protocol. The developed models had high accuracy with precision up to 99.6% and recall up to 99.6%.
KW - Anomaly based intrusion detection system
KW - Bluetooth Security
KW - IEEE 802.15.1
KW - Internet of Things(IoT)
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85061896623&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061896623&partnerID=8YFLogxK
U2 - 10.1109/AICCSA.2018.8612809
DO - 10.1109/AICCSA.2018.8612809
M3 - Conference contribution
AN - SCOPUS:85061896623
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018
PB - IEEE Computer Society
T2 - 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018
Y2 - 28 October 2018 through 1 November 2018
ER -