Transformer-based models are driving a significant revolution in the field of machine learning at the moment. Among these innovations, vision transformers (ViTs) stand out for their application of transformer architectures to vision-related tasks. By...
The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial attack method called the Multi-Stage Dual-Perturbation Attack (MSDPA), s...
Machine learning (ML) techniques have significantly enhanced decision support systems to render them more accurate, efficient, and faster. ML classifiers in securing networks, on the other hand, face a disproportionate risk from the sophisticated adv...
Machine learning systems, particularly in the domain of image recognition, are susceptible to adversarial perturbations applied to input data. These perturbations, while imperceptible to humans, have the capacity to easily deceive deep learning class...
Concerns about cybersecurity and attack methods have risen in the information age. Many techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs), that help achieve security goals, such as detecting malicious attacks...
Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negati...
The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these pertur...
Natural language processing (NLP) models based on deep neural networks (DNNs) are vulnerable to backdoor attacks. Existing backdoor defense methods have limited effectiveness and coverage scenarios. We propose a textual backdoor defense method based...
Deep learning models are vulnerable to backdoor attacks. The success rate of textual backdoor attacks based on data poisoning in existing research is as high as 100%. In order to enhance the natural language processing model’s defense against backdoo...
Due to the rapid growth in artificial intelligence (AI) and deep learning (DL) approaches, the security and robustness of the deployed algorithms need to be guaranteed. The security susceptibility of the DL algorithms to adversarial examples has been...
Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversaria...
Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vu...
The convergence of ubiquitous connectivity, large-scale data generation, and rapid advancements in machine learning is transforming the field of cybersecurity. The widespread adoption of interconnected systems including Internet of Things devices, mo...
Machine learning is being applied to continuous-variable quantum key distribution (CVQKD) systems as defense countermeasures for attack classification. However, recent studies have demonstrated that most of these detection networks are not immune to...
In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great...
Adversarial attacks represent a critical issue that prevents the reliable integration of machine learning methods into cyber defense systems. Past work has shown that even proficient detectors are highly affected just by small perturbations to malici...
DDoS (distributed denial of service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread...
Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model...
GPS spoofing remains a critical threat in the use of autonomous vehicles. Machine-learning-based detection systems, particularly support vector machines (SVMs), demonstrate high accuracy in their defense against conventional spoofing attacks. However...
Cybersecurity attacks pose a significant threat to the security of network systems through intrusions and illegal communications. Measuring the vulnerability of cybersecurity is crucial for refining the overall system security to further mitigate pot...
With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there...
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...
The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical ris...
Adversarial patch attacks have emerged as a powerful and practical threat to machine learning models in vision-based tasks. Unlike traditional perturbation-based adversarial attacks, which often require imperceptible changes to the entire input, patc...
The rise of IoT devices has led to significant advancements but also new security challenges. This paper assesses the performance of various machine learning (ML) models—Decision Trees, Naïve Bayes, Support Vector Machines (SVMs), and a de...
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples t...
Artificial Intelligence (AI) security research is promising and highly valuable in the current decade. In particular, deep neural network (DNN) security is receiving increased attention. Although DNNs have recently emerged as a prominent tool for add...
Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the M...
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are...
With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like a...
An intrusion detection system (IDS) is an effective tool for securing networks and a dependable technique for improving a user’s internet security. It informs the administration whenever strange conduct occurs. An IDS fundamentally depends on t...
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...
Adversarial machine learning (AML) is a class of data manipulation techniques that cause alterations in the behavior of artificial intelligence (AI) systems while going unnoticed by humans. These alterations can cause serious vulnerabilities to missi...
Internet of Things (IoT) technologies serve as a backbone of cutting-edge intelligent systems. Machine Learning (ML) paradigms have been adopted within IoT environments to exploit their capabilities to mine complex patterns. Despite the reported prom...
Rapid advancements in connected and autonomous vehicles (CAVs) are fueled by breakthroughs in machine learning, yet they encounter significant risks from adversarial attacks. This study explores the vulnerabilities of machine learning-based intrusion...
Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine learning (ML) tec...
Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the oth...
Federated learning offers a powerful approach for training models across decentralized datasets, enabling the creation of machine learning models that respect data privacy. However, federated learning faces significant challenges due to its vulnerabi...
Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have prov...
In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more....
In today’s digital era, cyberattacks are rapidly evolving, rendering traditional security mechanisms increasingly inadequate. The adoption of AI-based Network Intrusion Detection Systems (NIDS) has emerged as a promising solution, due to their...
Machine learning (ML) has greatly improved intrusion detection in enterprise networks. However, ML models remain vulnerable to adversarial attacks, where small input changes cause misclassification. This study evaluates the robustness of a Random For...
Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious network traffic, several IDS based on machine learning (ML) method...
Intrusion Detection Systems (IDS) are essential components in preventing malicious traffic from penetrating networks and systems. Recently, these systems have been enhancing their detection ability using machine learning algorithms. This development...
Machine learning (ML) promises advances in automation and threat detection for the future generations of communication networks. However, new threats are introduced, as adversaries target ML systems with malicious data. Adversarial attacks on tree-ba...
Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL....
Federated learning (FL) is an emerging machine learning technique where machine learning models are trained in a decentralized manner. The main advantage of this approach is the data privacy it provides because the data are not processed in a central...
Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities re...
Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the...
Recent advances in machine learning have enabled highly effective ciphertext-based cryptographic algorithm identification, posing a potential threat to encrypted communication. Inspired by adversarial example techniques, we present CSPM (Class-Specif...
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