Search Results (9)

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques. Full article

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model’s functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices’ malfunction, services’ interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement. Full article

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 ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well. Full article

Profiting from the powerful feature extraction and representation capabilities of deep learning (DL), aerial image semantic segmentation based on deep neural networks (DNNs) has achieved remarkable success in recent years. Nevertheless, the security and robustness of DNNs deserve attention when dealing with safety-critical earth observation tasks. As a typical attack pattern in adversarial machine learning (AML), backdoor attacks intend to embed hidden triggers in DNNs by poisoning training data. The attacked DNNs behave normally on benign samples, but when the hidden trigger is activated, its prediction is modified to a specified target label. In this article, we systematically assess the threat of backdoor attacks to aerial image semantic segmentation tasks. To defend against backdoor attacks and maintain better semantic segmentation accuracy, we construct a novel robust generative adversarial network (RFGAN). Motivated by the sensitivity of human visual systems to global and edge information in images, RFGAN designs the robust global feature extractor (RobGF) and the robust edge feature extractor (RobEF) that force DNNs to learn global and edge features. Then, RFGAN uses robust global and edge features as guidance to obtain benign samples by the constructed generator, and the discriminator to obtain semantic segmentation results. Our method is the first attempt to address the backdoor threat to aerial image semantic segmentation by constructing the robust DNNs model architecture. Extensive experiments on real-world scenes aerial image benchmark datasets demonstrate that the constructed RFGAN can effectively defend against backdoor attacks and achieve better semantic segmentation results compared with the existing state-of-the-art methods. Full article

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 promising results, ML-based solutions exhibit several security vulnerabilities and threats. Specifically, Adversarial Machine Learning (AML) attacks can drastically impact the performance of ML models. It also represents a promising research field that typically promotes novel techniques to generate and/or defend against Adversarial Examples (AE) attacks. In this work, a comprehensive survey on AML attack and defense techniques is conducted for the years 2018–2022. The article investigates the employment of AML techniques to enhance intrusion detection performance within the IoT context. Additionally, it depicts relevant challenges that researchers aim to overcome to implement proper IoT-based security solutions. Thus, this survey aims to contribute to the literature by investigating the application of AML concepts within the IoT context. An extensive review of the current research trends of AML within IoT networks is presented. A conclusion is reached where several findings are reported including a shortage of defense mechanisms investigations, a lack of tailored IoT-based solutions, and the applicability of the existing mechanisms in both attack and defense scenarios. Full article

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 before they enter the system and classifying them as malicious activities. However, the IDS approaches have shortcomings in misclassifying novel attacks or adapting to emerging environments, affecting their accuracy and increasing false alarms. To solve this problem, researchers have recommended using machine learning approaches as engines for IDSs to increase their efficacy. Machine-learning techniques are supposed to automatically detect the main distinctions between normal and malicious data, even novel attacks, with high accuracy. However, carefully designed adversarial input perturbations during the training or testing phases can significantly affect their predictions and classifications. Adversarial machine learning (AML) poses many cybersecurity threats in numerous sectors that use machine-learning-based classification systems, such as deceiving IDS to misclassify network packets. Thus, this paper presents a survey of adversarial machine-learning strategies and defenses. It starts by highlighting various types of adversarial attacks that can affect the IDS and then presents the defense strategies to decrease or eliminate the influence of these attacks. Finally, the gaps in the existing literature and future research directions are presented. Full article

The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications. Full article

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 mission-critical AI-enabled applications. This work introduces an AI architecture augmented with adversarial examples and defense algorithms to safeguard, secure, and make more reliable AI systems. This can be conducted by robustifying deep neural network (DNN) classifiers and explicitly focusing on the specific case of convolutional neural networks (CNNs) used in non-trivial manufacturing environments prone to noise, vibrations, and errors when capturing and transferring data. The proposed architecture enables the imitation of the interplay between the attacker and a defender based on the deployment and cross-evaluation of adversarial and defense strategies. The AI architecture enables (i) the creation and usage of adversarial examples in the training process, which robustify the accuracy of CNNs, (ii) the evaluation of defense algorithms to recover the classifiers’ accuracy, and (iii) the provision of a multiclass discriminator to distinguish and report on non-attacked and attacked data. The experimental results show promising results in a hybrid solution combining the defense algorithms and the multiclass discriminator in an effort to revitalize the attacked base models and robustify the DNN classifiers. The proposed architecture is ratified in the context of a real manufacturing environment utilizing datasets stemming from the actual production lines. Full article

Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on adversarial risk analysis. Full article