no code implementations • 2 Apr 2024 • Sreenitha Kasarapu, Sanket Shukla, Rakibul Hassan, Avesta Sasan, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Furthermore, such constraints limit the detection of emerging malware samples due to the lack of sufficient malware samples required for efficient training.
1 code implementation • 21 Dec 2023 • Yu-Zheng Lin, Muntasir Mamun, Muhtasim Alam Chowdhury, Shuyu Cai, Mingyu Zhu, Banafsheh Saber Latibari, Kevin Immanuel Gubbi, Najmeh Nazari Bavarsad, Arjun Caputo, Avesta Sasan, Houman Homayoun, Setareh Rafatirad, Pratik Satam, Soheil Salehi
The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners.
no code implementations • 1 Oct 2023 • Ali Karkehabadi, Houman Homayoun, Avesta Sasan
Saliency-Guided Training (SGT) methods try to highlight the prominent features in the model's training based on the output to alleviate this problem.
no code implementations • 4 Apr 2023 • Jialin Liu, Ning Miao, Chongzhou Fang, Houman Homayoun, Han Wang
In particular, we first identify the vulnerability of DTW for ECG classification, i. e., the correlation between warping path choice and prediction results.
no code implementations • 13 Sep 2022 • Rakibul Hassan, Gaurav Kolhe, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Logic obfuscation is introduced as a pivotal defense against multiple hardware threats on Integrated Circuits (ICs), including reverse engineering (RE) and intellectual property (IP) theft.
no code implementations • 7 Apr 2022 • Ali Mirzaeian, Zhi Tian, Sai Manoj P D, Banafsheh S. Latibari, Ioannis Savidis, Houman Homayoun, Avesta Sasan
We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid.
no code implementations • 29 Jun 2020 • Ali Mirzaeian, Sai Manoj, Ashkan Vakil, Houman Homayoun, Avesta Sasan
Deep convolutional neural networks have shown high efficiency in computer visions and other applications.
no code implementations • 26 Jun 2020 • Ali Mirzaeian, Jana Kosecka, Houman Homayoun, Tinoosh Mohsenin, Avesta Sasan
This paper proposes an ensemble learning model that is resistant to adversarial attacks.
1 code implementation • 22 Mar 2020 • Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges.
no code implementations • 16 Jan 2020 • Farnaz Behnia, Ali Mirzaeian, Mohammad Sabokrou, Sai Manoj, Tinoosh Mohsenin, Khaled N. Khasawneh, Liang Zhao, Houman Homayoun, Avesta Sasan
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity.
no code implementations • 14 Oct 2019 • Ali Mirzaeian, Houman Homayoun, Avesta Sasan
In this paper, we first propose the design of Temporal-Carry-deferring MAC (TCD-MAC) and illustrate how our proposed solution can gain significant energy and performance benefit when utilized to process a stream of input data.
no code implementations • 1 Oct 2019 • Ali Mirzaeian, Houman Homayoun, Avesta Sasan
In this paper, we present NESTA, a specialized Neural engine that significantly accelerates the computation of convolution layers in a deep convolutional neural network, while reducing the computational energy.
no code implementations • 22 Aug 2019 • Yuyang Gao, Lingfei Wu, Houman Homayoun, Liang Zhao
In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.
no code implementations • 29 Jul 2019 • Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, Setareh Rafatirad
HLS tools offer a plethora of techniques to optimize designs for both area and performance, but resource usage and timing reports of HLS tools mostly deviate from the post-implementation results.
no code implementations • 7 Jul 2019 • Mahdi Pedram, Seyed Ali Rokni, Marjan Nourollahi, Houman Homayoun, Hassan Ghasemzadeh
We propose to transform the activity recognition problem from a multi-class classification problem to a hierarchical model of binary decisions using cascading online binary classifiers.
no code implementations • 14 Feb 2019 • Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Sai Manoj P. D., Houman Homayoun, Liang Zhao, Chang-Tien Lu
Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates.
no code implementations • 30 Apr 2018 • Shervin Roshanisefat, Harshith K. Thirumala, Kris Gaj, Houman Homayoun, Avesta Sasan
In this paper, we investigate the strength of six different SAT solvers in attacking various obfuscation schemes.
Cryptography and Security
no code implementations • 30 Apr 2018 • Hadi Mardani Kamali, Kimia Zamiri Azar, Kris Gaj, Houman Homayoun, Avesta Sasan
In this work, we propose LUT-Lock, a novel Look-Up-Table-based netlist obfuscation algorithm, for protecting the intellectual property that is mapped to an FPGA bitstream or an ASIC netlist.
Cryptography and Security