26 citations · 86 across the 11 of their papers we have counts for
12 papers
Deep Defence on Wheels: A Dual Intrusion Detection System Architecture for Comprehensive In-Vehicle Network Security
Shashwat Khandelwal, Shanker Shreejith
Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cyberattacks. Initial research focu…
FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs
Shashwat Khandelwal, Jakoba Petri-Koenig, Thomas B. Preußer +2
Recurrent neural networks (RNNs), particularly LSTMs, are effective for time-series tasks like sentiment analysis and short-term stock prediction. However, their computational comp…
FAV-NSS: An HIL Framework for Accelerating Validation of Automotive Network Security Strategies
Changhong Li, Shashwat Khandelwal, Shreejith Shanker
Complex electronic control unit (ECU) architectures, software models and in-vehicle networks are consistently improving safety and comfort functions in modern vehicles. However, th…
SecCAN: An Extended CAN Controller with Embedded Intrusion Detection
Shashwat Khandelwal, Shreejith Shanker
Recent research has highlighted the vulnerability of in-vehicle network protocols such as controller area networks (CAN) and proposed machine learning-based intrusion detection sys…
An Energy-Efficient Artefact Detection Accelerator on FPGAs for Hyper-Spectral Satellite Imagery
Cornell Castelino, Shashwat Khandelwal, Shanker Shreejith +1
Hyper-Spectral Imaging (HSI) is a crucial technique for analysing remote sensing data acquired from Earth observation satellites. The rich spatial and spectral information obtained…
Accelerating local laplacian filters on FPGAs
Shashwat Khandelwal, Ziaul Choudhury, Shashwat Shrivastava +1
Images when processed using various enhancement techniques often lead to edge degradation and other unwanted artifacts such as halos. These artifacts pose a major problem for photo…
Quantised Neural Network Accelerators for Low-Power IDS in Automotive Networks
Shashwat Khandelwal, Anneliese Walsh, Shanker Shreejith
In this paper, we explore low-power custom quantised Multi-Layer Perceptrons (MLPs) as an Intrusion Detection System (IDS) for automotive controller area network (CAN). We utilise…
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN
Shashwat Khandelwal, Shreejith Shanker
Recent years have seen an exponential rise in complex software-driven functionality in vehicles, leading to a rising number of electronic control units (ECUs), network capabilities…
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
Shashwat Khandelwal, Shreejith Shanker
Vehicles today comprise intelligent systems like connected autonomous driving and advanced driving assistance systems (ADAS) to enhance the driving experience, which is enabled thr…
Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs
Shashwat Khandelwal, Shreejith Shanker
Increasing automation in vehicles enabled by increased connectivity to the outside world has exposed vulnerabilities in previously siloed automotive networks like controller area n…
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
Shashwat Khandelwal, Shreejith Shanker
Rising connectivity in vehicles is enabling new capabilities like connected autonomous driving and advanced driver assistance systems (ADAS) for improving the safety and reliabilit…
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN
Shashwat Khandelwal, Eashan Wadhwa, Shreejith Shanker
Rising complexity of in-vehicle electronics is enabling new capabilities like autonomous driving and active safety. However, rising automation also increases risk of security threa…