Conference Paper
Topics: Computer Security . Side-channel Attack . Microarchitectural Attack . ML/DL-based Defense Techniques . Cryptography
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Seonghun Son, Debopriya Roy Dipta, and Berk Gulmezoglu. 2023. “DefWeb: Defending User Privacy against Cache-based Website Fingerprinting Attacks with Intelligent Noise Injection.” In Proceedings of the 39th Annual Computer Security Applications Conference (ACSAC '23). Association for Computing Machinery, New York, NY, USA, 379–393. Available at: https://doi.org/10.1145/3627106.3627191
Research key words: Cache-based Attack, Noise Injection, Website Fingerprinting, Generative Learning, Variational Autoencoder, Web Security
In this work, we develop a dynamic generative learning-based defense technique, DefWeb, to protect user privacy against cachebased WF attacks by injecting precise noise into the WFs. For this purpose, (i) we train generative neural networks to represent highdimensional fingerprints in a low-dimension space while creating distinct clusters for each website. (ii) Minimal noise templates are extracted in the low-dimension space to obfuscate the fingerprints efficiently. (iii) We create practical noise templates that can be added to WFs during website rendering by leveraging self-modifying code (SMC). We implement DefWeb in both simulation and real-world setups to degrade the attacker’s model accuracy. DefWeb can decrease the model accuracy to 1.1% and 28.8% in simulation and real-world setups, respectively, including Mozilla Firefox, Google Chrome, and Tor browsers. Finally, the performance overhead introduced by DefWeb is only 9.5%, which is considerably lower than previous defense techniques.
Threat Model
2. Debopriya Roy Dipta and Berk Gulmezoglu. 2022. “DF-SCA: Dynamic Frequency Side Channel Attacks are Practical”. In Proceedings of the 38th Annual Computer Security Applications Conference (ACSAC '22). Association for Computing Machinery, New York, NY, USA, 841–853. https://doi.org/10.1145/3564625.3567979
Research key words: dynamic frequency, side-channel attacks, keystroke recovery, web-site fingerprinting
We present DF-SCA, which is a software-based dynamic frequency side-channel attack on Linux and Android OS devices. We show that Dynamic Voltage and Frequency Scaling (DVFS) feature in modern systems can be utilized to perform website fingerprinting attacks for Google Chrome and Tor browsers on modern Intel, AMD, and ARM architectures. Moreover, we extract properties of keystroke patterns on frequency readings, which leads to 95% accuracy to distinguish the keystrokes from other activities on Android phones.
For details, check the following GitHub repository:
https://github.com/Diptakuet/DF-SCA-Dynamic-Frequency-Side-Channel-Attacks-are-Practical
Threat Model
3. M. S. Rahman, M. S. Hossain, E. H. Rahat, D. R. Dipta, H. M. R. Faruque and F. K. Fattah, "Efficient Hardware Implementation of 256-bit ECC Processor Over Prime Field," 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox'sBazar, Bangladesh, 2019, pp. 1-6.
Available at: doi: 10.1109/ECACE.2019.8679184.
Research key words: Elliptical curve cryptography, Jacobian coordinate, Modular multiplication, Elliptical curve point multiplication
Exchange of private information over the public domain is very much susceptible to unauthorized access, therefore this necessitates the need for a cryptosystem to ensure the protection of information against forthcoming threats. Elliptical curve cryptography (ECC) has attracted the eyes of many scientists due to its smaller key size and high-speed operation. In this paper, an efficient hardware implementation using field programmable gate array (FPGA) on elliptical curve processor (ECP) over a prime field has been proposed. Jacobian coordinate has been used to avoid modular inversion, which is regarded as the most costly operation. To minimize the area and delay in modular multiplication, an interleaved modular multiplier has been proposed. Point doubling (PD) and point addition (PA) architecture was designed with the minimum arithmetic unit using the efficient modular multiplier algorithm. Furthermore, an efficient elliptical curve point multiplication (ECPM) module has been proposed using the high-performance PD and PA architecture. It was found that the proposed ECPM module has a minimal delay and is very area efficient and thus it has immense potentiality in its application in modern day cryptosystem.
Topics: Application of ML/DL . Signal Processing . Renewable Energy
4. M. M. Rahman, D. Roy Dipta and M. M. Hasan, "Dynamic Time Warping Assisted SVM Classifier for Bangla Speech Recognition," 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1-6, doi: 10.1109/IC4ME2.2018.8465640.
Available at: https://ieeexplore.ieee.org/document/8465640
Research key words: Mel frequency cepstrum coefficient, Delta feature, Delta-Delta feature, Dynamic time warping, Support vector machine
In this paper, an automatic speech recognition system has been proposed for isolated Bangla word using Support vector machine with Dynamic time warping (DTW). For training purposes, we have collected data from 40 speakers for five different Bangla words. All the data was collected in a highly acoustic and noise-proof environment. Mel frequency cepstrum coefficients (MFCC’s) are considered as static features from the speech signal. For dynamic features, first and second derivatives of MFCC are utilized. After determining feature vectors, a modified DTW method is proposed for feature matching. Finally, for classification Support Vector Machine (SVM) with Radial basis function (RBF) is utilized. The model is tested for 12 speakers and the recognition rate that we achieved is 86.08%.
5. M. S. Haque Sunny, D. Roy Dipta, S. Hossain, H. M. Resalat Faruque and E. Hossain, "Design of a Convolutional Neural Network Based Smart Waste Disposal System," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-5,
doi: 10.1109/ICASERT.2019.8934633. Available at: https://ieeexplore.ieee.org/document/8934633
Research key words: Waste management, Convolutional Neural Network, Automated Teller Dustbin, Object detection, Object recognition.
In recent times, waste management problem has become a crucial challenge for Bangladesh, which is having a detrimental impact on the environment. This paper presents the proposition of designing a smart dustbin similar to an Automated Teller Machine (ATM) along with an intelligent embedded system, which has been dubbed as Automated Teller Dustbin (ATD). An efficient convolutional neural network (CNN) based image classifier is developed, which is able to detect and recognize any object regarded as garbage by analyzing training features. Additionally, it can also count the number of labeled objects and assign a price value to each object. The waste brought by any individual to the ATD will readily be recognized by the image classifier and the recycle value, which has been assigned for that object can be withdrawn by that individual. Therefore, a direct exchange of waste and its equivalent price is possible, which will incentivize people to use our proposed smart dustbin. After the installation cost, the operation and maintenance cost can be gained by recycling the garbage in it. A pre-trained CNN-based model ALexNet has been utilized to train and test the model with a dataset of 20 images for each of the 10 categorized objects collected from different waste management shops in Dhaka, Bangladesh. The model that has been trained for object recognition has attained an accuracy of 96%, which bears testimony to the feasibility of our proposal.
Flow chart of the non-organic waste management procedure
Different objects that have been successfully recognized by CNNbased modified AlexNet model
Training progress in terms of accuracy rate. As is evident from the graph, accuracy crosses the 80% threshold after a few iterations and then saturates to 96%, for the designed CNN model
6. F. I. Bappy, M. J. Islam, A. K. Podder, D. R. Dipta, H. M. R. Faruque, and E. Hossain, “Comparison of Different Hybrid Renewable Energy Systems With Optimized PV Configuration to Realize the Effects of Multiple Schemes,” 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, IEEE 2019. Available at: https://ieeexplore.ieee.org/document/8934824
Research key words: HOMER, NPC, HELIOSCOPE, Micro-grid, Hybrid Renewable Energy System.
Exchange of private information over the public domain is very much susceptible to unauthorized access, therefore this necessitates the need for a cryptosystem to ensure the protection of information against forthcoming threats. Elliptical curve cryptography (ECC) has attracted the eyes of many scientists due to its smaller key size and high-speed operation. In this paper, an efficient hardware implementation using field programmable gate array (FPGA) on elliptical curve processor (ECP) over a prime field has been proposed. Jacobian coordinate has been used to avoid modular inversion, which is regarded as the most costly operation. To minimize the area and delay in modular multiplication, an interleaved modular multiplier has been proposed. Point doubling (PD) and point addition (PA) architecture was designed with the minimum arithmetic unit using the efficient modular multiplier algorithm. Furthermore, an efficient elliptical curve point multiplication (ECPM) module has been proposed using the high-performance PD and PA architecture. It was found that the proposed ECPM module has a minimal delay and is very area efficient and thus it has immense potentiality in its application in modern day cryptosystem.
Proposed hybrid system configurations. (a) PV only with storage system, (b) PV with grid and storage system,
(c) PV, diesel generator with storage system, and (d) PV, diesel and grid system
PV panel installation for cases 1 and 2
PV panel installation for cases 3 and 4
