Md Hasan Shahriar

Md Hasan Shahriar, Yang Xiao, Pablo Moriano et al.

Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control. With the expansion of advanced connectivity features in automobiles and the elevated risks of internal system exposure, the CAN bus is increasingly prone to intrusions and injection attacks. As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them. However, advanced attackers can inject false data to the signal/semantic level, while looking innocuous by the pattern/frequency of the CAN messages. The rule-based IDS, as well as the anomaly-based IDS, are built merely on the sequence of CAN messages IDs or just the binary payload data and are less effective in detecting such attacks. Therefore, to detect such intelligent attacks, we propose CANShield, a deep learning-based signal-level intrusion detection framework for the CAN bus. CANShield consists of three modules: a data preprocessing module that handles the high-dimensional CAN data stream at the signal level and parses them into time series suitable for a deep learning model; a data analyzer module consisting of multiple deep autoencoder (AE) networks, each analyzing the time-series data from a different temporal scale and granularity, and finally an attack detection module that uses an ensemble method to make the final decision. Evaluation results on two high-fidelity signal-based CAN attack datasets show the high accuracy and responsiveness of CANShield in detecting advanced intrusion attacks.

Md Hasan Shahriar, Ning Wang, Naren Ramakrishnan et al.

The exponential adoption of machine learning (ML) is propelling the world into a future of distributed and intelligent automation and data-driven solutions. However, the proliferation of malicious data manipulation attacks against ML, namely adversarial and backdoor attacks, jeopardizes its reliability in safety-critical applications. The existing detection methods are attack-specific and built upon some strong assumptions, limiting them in diverse practical scenarios. Thus, motivated by the need for a more robust, unified, and attack-agnostic defense mechanism, we first investigate the shared traits of adversarial and backdoor attacks. Based on our observation, we propose NoiSec, a reconstruction-based intrusion detection system that brings a novel perspective by shifting focus from the reconstructed input to the reconstruction noise itself, which is the foundational root cause of such malicious data alterations. NoiSec disentangles the noise from the test input, extracts the underlying features from the noise, and leverages them to recognize systematic malicious manipulation. Our comprehensive evaluation of NoiSec demonstrates its high effectiveness across various datasets, including basic objects, natural scenes, traffic signs, medical images, spectrogram-based audio data, and wireless sensing against five state-of-the-art adversarial attacks and three backdoor attacks under challenging evaluation conditions. NoiSec demonstrates strong detection performance in both white-box and black-box adversarial attack scenarios, significantly outperforming the closest baseline models, particularly in an adaptive attack setting. We will provide the code for future baseline comparison. Our code and artifacts are publicly available at https://github.com/shahriar0651/NoiSec.

Md Hasan Shahriar, Md Mohaimin Al Barat, Harshavardhan Sundar et al.

Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on precise temporal synchronization exposes it to new vulnerabilities. In this paper, we introduce DejaVu, an attack that exploits the in-vehicular network and induces delays across sensor streams to create subtle temporal misalignments, severely degrading downstream MMF-based perception tasks. Our comprehensive attack analysis across different models and datasets reveals the sensors' task-specific imbalanced sensitivities: object detection is overly dependent on LiDAR inputs, while object tracking is highly reliant on the camera inputs. Consequently, with a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%. We further demonstrated two attack scenarios using an automotive Ethernet testbed for hardware-in-the-loop validation and the Autoware stack for end-to-end AD simulation, demonstrating the feasibility of the DejaVu attack and its severe impact, such as collisions and phantom braking.

Nur Imtiazul Haque, Mohammad Ashiqur Rahman, Md Hasan Shahriar et al.

Smart healthcare systems (SHSs) are providing fast and efficient disease treatment leveraging wireless body sensor networks (WBSNs) and implantable medical devices (IMDs)-based internet of medical things (IoMT). In addition, IoMT-based SHSs are enabling automated medication, allowing communication among myriad healthcare sensor devices. However, adversaries can launch various attacks on the communication network and the hardware/firmware to introduce false data or cause data unavailability to the automatic medication system endangering the patient's life. In this paper, we propose SHChecker, a novel threat analysis framework that integrates machine learning and formal analysis capabilities to identify potential attacks and corresponding effects on an IoMT-based SHS. Our framework can provide us with all potential attack vectors, each representing a set of sensor measurements to be altered, for an SHS given a specific set of attack attributes, allowing us to realize the system's resiliency, thus the insight to enhance the robustness of the model. We implement SHChecker on a synthetic and a real dataset, which affirms that our framework can reveal potential attack vectors in an IoMT system. This is a novel effort to formally analyze supervised and unsupervised machine learning models for black-box SHS threat analysis.

Nur Imtiazul Haque, Md Hasan Shahriar, Md Golam Dastgir et al.

Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point. Cyberattacks are the principal threats confronting the usage and advancement of the state-of-the-art systems. The advancement of SG has added a wide range of technologies, equipment, and tools to make the system more reliable, efficient, and cost-effective. Despite attaining these goals, the threat space for the adversarial attacks has also been expanded because of the extensive implementation of the cyber networks. Due to the promising computational and reasoning capability, machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators, respectively. In this paper, we perform a comprehensive summary of cyberattacks generation, detection, and mitigation schemes by reviewing state-of-the-art research in the SG domain. Additionally, we have summarized the current research in a structured way using tabular format. We also present the shortcomings of the existing works and possible future research direction based on our investigation.

Md Hasan Shahriar, Nur Imtiazul Haque, Mohammad Ashiqur Rahman et al.

The boundaries of cyber-physical systems (CPS) and the Internet of Things (IoT) are converging together day by day to introduce a common platform on hybrid systems. Moreover, the combination of artificial intelligence (AI) with CPS creates a new dimension of technological advancement. All these connectivity and dependability are creating massive space for the attackers to launch cyber attacks. To defend against these attacks, intrusion detection system (IDS) has been widely used. However, emerging CPS technologies suffer from imbalanced and missing sample data, which makes the training of IDS difficult. In this paper, we propose a generative adversarial network (GAN) based intrusion detection system (G-IDS), where GAN generates synthetic samples, and IDS gets trained on them along with the original ones. G-IDS also fixes the difficulties of imbalanced or missing data problems. We model a network security dataset for an emerging CPS using NSL KDD-99 dataset and evaluate our proposed model's performance using different metrics. We find that our proposed G-IDS model performs much better in attack detection and model stabilization during the training process than a standalone IDS.

Mohammad Ashiqur Rahman, Md Hasan Shahriar, Mohamadsaleh Jafari et al.

Contingency Analysis (CA) is a core component of the Energy Management System (EMS) in the power grid. The goal of CA is to operate the power system in a secure manner by analyzing the system subject to a contingency (e.g., the outage of a transmission line or a power generator) to determine the setpoints that will allow system operation without violation of constraints. The analysis in CA is conducted based on the output from State Estimation (SE), another core EMS module. However, it is also shown that an adversary can alter certain power measurements to corrupt the system states estimated by SE without being detected. Such a corrupted estimation can severely skew the results of the contingency analysis as it will provide a fake model to deal with. In this research, we formally model necessary interdependency relationships and systematically analyze these novel attacks on the contingency analysis. In particular, this research focuses on Security Constrained Optimal Power Flow (SCOPF) that finds out the optimal economic dispatches considering a single line failure (based on the $n - 1$ contingency analysis) and transmission line capacities. The proposed model is implemented and solved to find out potential threat vectors (i.e., a set of measurements to be altered) that can evade CA so that the system will face overloading situation on one or more transmission lines when some specific contingencies happen. We demonstrate our formal model on an IEEE 14 bus system-based case study and verify the results with a standard PowerWorld model. We further evaluate the model with respect to various attacks and grid characteristics.