Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods...
The rapid proliferation of ransomware variants necessitates more effective detection mechanisms, as traditional signature-based methods are increasingly inadequate. These conventional methods rely on manual feature extraction and matching, which are...
This paper aims at explaining the lessons learned from the chemical attacks that took place in 2013 in the Syrian military conflict, especially the sarin attacks on the Ghouta area of Damascus on August 21. Despite the limitations the UN Mission fo...
Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or
This study presents a novel defense framework to fortify Under-Display Camera (UDC) image restoration models against adversarial attacks, a previously underexplored vulnerability in this domain. Our research initially conducts an in-depth robustness...
In the field of behavioral detection, deep learning has been extensively utilized. For example, deep learning models have been utilized to detect and classify malware. Deep learning, however, has vulnerabilities that can be exploited with crafted inp...
Improving network performance has become an active area of research in light of recent advances in Internet of Things sensor networks. This performance depends on configurable parameters at the transmitting node, including the contention window (CW),...
Deep learning models for power quality (PQ) disturbance classification remain critically vulnerable to adversarial perturbations, with classification performance degrading severely under white-box attacks. Existing defenses address individual models...
Automated insulator inspection systems face critical challenges from small object sizes, complex backgrounds, and vulnerability to adversarial attacks, a security concern largely unaddressed in safety-critical power infrastructure. We introduce Faste...
High-performance geopolymer concrete (HPGC) is an eco-friendly type of concrete that is traditionally made of slag, silica fume (SF), and quartz sand. Recycling industrial waste in HPGC presents an eco-friendly approach for maximizing sustainability...
Blackberries are very perishable with a limited shelf life due to a high metabolic activity and susceptibility to mechanical damage and microbial attack. The effect of edible coatings (EC) based on candelilla wax (CW) and guar gum (GG) on the quality...
Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against...
This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wag...
Audio deepfake and vocoder fingerprint detectors are increasingly used to identify synthetic speech and attribute it to its generating model. However, their robustness against adversarial perturbations remains unclear across attack algorithms, pertur...
Mobile Edge Computing (MEC) is a key enabler of 5G/6G services, but multi-base-station deployment enlarges the attack surface and motivates edge-native intrusion detection systems (IDSs). Existing MEC-based IDSs are mainly single-node or centralized,...
Constructed wetlands (CWs), increasingly adopted as nature-based solutions (NBS) for wastewater treatment, require a rigorous assessment of the durability and structural performance of the materials used in their supporting systems. In contrast to th...
Deep neural network has been widely used in pattern recognition and speech processing, but its vulnerability to adversarial attacks also proverbially demonstrated. These attacks perform unstructured pixel-wise perturbation to fool the classifier, whi...
Kolmogorov–Arnold Networks (KANs) are a novel class of neural network architectures based on the Kolmogorov–Arnold representation theorem, which has demonstrated potential advantages in accuracy and interpretability over Multilayer Percep...
With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent stud...
To improve the robustness of intrusion detection systems constructed using deep learning models, a method based on an auxiliary adversarial training WGAN (AuxAtWGAN) is proposed from the defender’s perspective. First, one-dimensional traffic da...
We propose RobustQuote, a novel defense framework designed to enhance the adversarial robustness of vision transformers. The core idea is to leverage trusted reference images drawn from a dynamically changing pool unknown to the attacker as contextua...
The ViTs model has been widely used since it was proposed, and its performance on large-scale datasets has surpassed that of CNN models. In order to deploy the ViTs model safely in practical application scenarios, its robustness needs to be investiga...
Fine-grained recognition has many applications in many fields and aims to identify targets from subcategories. This is a highly challenging task due to the minor differences between subcategories. Both modal missing and adversarial sample attacks are...
Multi-label deep learning models are widely used in real-world applications where predictions depend on the joint presence of several semantically correlated labels. However, existing adversarial attacks largely overlook these inter-label dependencie...
The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to thei...
Vision-sensor-based intelligent perception systems are increasingly used in safety-critical scenarios such as autonomous driving, edge surveillance, and Internet-of-Things (IoT) platforms. The vulnerability of deep neural networks to adversarial exam...
Cloud-based intrusion detection systems (IDSs) increasingly rely on deep learning classifiers to identify malicious traffic; however, this reliance exposes them to adversarial evasion attacks in which adversaries craft near-imperceptible perturbation...
Adversarial examples are one of the most intriguing topics in modern deep learning. Imperceptible perturbations to the input can fool robust models. In relation to this problem, attack and defense methods are being developed almost on a daily basis....
Vision Transformers (ViTs) have demonstrated strong performance in hyperspectral image (HSI) classification; however, their robustness is highly sensitive to patch size. This study investigates the impact of spatial patch size on clean accuracy and a...
Protection of the water system is paramount due to the negative consequences of contaminated water on the public health. Water resources are one of the critical infrastructures that must be preserved from deliberate and accidental attacks. Water qual...
The CSMA/CA algorithm uses the binary backoff mechanism to solve the multi-user channel access problem, but this mechanism is vulnerable to jamming attacks. Existing research uses channel-hopping to avoid jamming, but this method fails when the chann...
With the in-depth integration of deep learning and side-channel analysis (SCA) technology, the security threats faced by embedded devices based on the Internet of Things (IoT) have become increasingly prominent. By building a neural network model as...
Aluminum alloys are extensively used to manufacture mechanical components. However, when exposed to alkaline environments, like lubricants, refrigerants, or detergents, they can be corroded, reducing their durability. For this reason, the aim of this...
Insights into motor cortex remodeling may enable the development of more effective rehabilitation strategies during the acute phase. We aim to assess the affected and unaffected motor/premotor/somatosensory cortex resting state functional connectivit...