TY - GEN
T1 - A self-learning framework to detect the intruded integrated circuits
AU - Lodhi, F. K.
AU - Abbasi, I.
AU - Khalid, F.
AU - Hasan, O.
AU - Awwad, F.
AU - Hasan, S. R.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/29
Y1 - 2016/7/29
N2 - Globalization trends in integrated circuit (IC) design using deep submicron (DSM) technologies are leading to increased vulnerability of ICs against malicious intrusions. These malicious intrusions are referred as hardware Trojans. One way to address this threat is to utilize unique electrical signatures of ICs. However, this technique requires analyzing extensive sensor data to detect the intruded integrated circuits. In order to overcome this limitation, we propose to combine the signature extraction mechanism with machine learning algorithms to develop a self-learning framework that can detect the intruded integrated circuits. The proposed approach applies the lazy, eager or probabilistic learners to generate self-learning prediction model based on the electrical signatures. In order to validate this framework, we applied it on a recently proposed signature based hardware Trojan detection technique. The cross validation comparison of these learner shows that eager learners are able to detect the intrusion with 96% accuracy and also require less amount of memory and processing power compared to other machine learning techniques.
AB - Globalization trends in integrated circuit (IC) design using deep submicron (DSM) technologies are leading to increased vulnerability of ICs against malicious intrusions. These malicious intrusions are referred as hardware Trojans. One way to address this threat is to utilize unique electrical signatures of ICs. However, this technique requires analyzing extensive sensor data to detect the intruded integrated circuits. In order to overcome this limitation, we propose to combine the signature extraction mechanism with machine learning algorithms to develop a self-learning framework that can detect the intruded integrated circuits. The proposed approach applies the lazy, eager or probabilistic learners to generate self-learning prediction model based on the electrical signatures. In order to validate this framework, we applied it on a recently proposed signature based hardware Trojan detection technique. The cross validation comparison of these learner shows that eager learners are able to detect the intrusion with 96% accuracy and also require less amount of memory and processing power compared to other machine learning techniques.
KW - Asynchronous pipeline
KW - Hardware Trojan
KW - MSMA
KW - Machine Learning
KW - Rapid Miner Studio
UR - http://www.scopus.com/inward/record.url?scp=84983405281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84983405281&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2016.7538895
DO - 10.1109/ISCAS.2016.7538895
M3 - Conference contribution
AN - SCOPUS:84983405281
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
SP - 1702
EP - 1705
BT - ISCAS 2016 - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Symposium on Circuits and Systems, ISCAS 2016
Y2 - 22 May 2016 through 25 May 2016
ER -