Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly...
We consider the problem of data poisoning attack detection in a federated learning (FL) setup with differential privacy (DP). Local DP in FL ensures that privacy leakage caused by shared gradients is controlled by adding randomness to the process. We...
Cognitive Radio Network (CRN) is an emerging technology used to solve spectrum shortage problems in wireless communications. In CRN, unlicensed secondary users (SUs) and licensed primary users (PUs) use spectrum resources at the same time by avoiding...
Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighti...
The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces...
This work studies the power of adversarial attacks against machine learning algorithms that use differentially private mechanisms as their weapon. In our setting, the adversary aims to modify the content of a statistical dataset via insertion of addi...
Distributed Denial of Service (DDoS) has developed multiple variants, one of which is Distributed Reflective Denial of Service (DRDoS). With the increasing number of Internet of Things (IoT) devices, the threat of DRDoS attack is growing, and the dam...
With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result o...
Adversarial example generation techniques for neural network models have exploded in recent years. In the adversarial attack scheme for image recognition models, it is challenging to achieve a high attack success rate with very few pixel modification...
Deep neural networks (DNNs) have been employed in continuous-variable quantum key distribution (CV-QKD) systems as attacking detection portions of defense countermeasures. However, the vulnerability of DNNs leaves security loopholes for hacking attac...
Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses...
In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’...
Temperature sensors play an important role in modern monitoring and control applications. When more and more sensors are integrated into internet-connected systems, the integrity and security of sensors become a concern and cannot be ignored anymore....
This study introduces a deep-learning-based framework for detecting adversarial attacks in CT image segmentation within medical imaging. The proposed methodology includes analyzing features from various layers, particularly focusing on the first laye...
The Deterministic Network (DetNet) is becoming a major feature for 5G and 6G networks to cope with the issue that conventional IT infrastructure cannot efficiently handle latency-sensitive data. The DetNet applies flow virtualization to satisfy time-...
Distributed denial-of-service (DDoS) attack is one of the major threats to the web server. The rapid increase of DDoS attacks on the Internet has clearly pointed out the limitations in current intrusion detection systems or intrusion prevention syste...
This paper presents a study about hardware attacking and clock signal vulnerability. It considers a particular type of attack on the clock signal in the I2C protocol, and proposes the design of a new sensor for detecting and defending against this ty...
Multi-modal anti-spoofing aims to differentiate live users from spoofing attacks using multiple biometric modalities during model training. While existing anti-spoofing methods often incorporate just one biometric modality, the effectiveness of attac...
Federated learning has emerged as a privacy-preserving paradigm that collaboratively trains a model without sharing raw data. However, federated learning remains highly vulnerable to abnormal or malicious participants. Differential privacy techniques...
The increasing use of Internet of Things (IoT) devices has led to a rise in Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on these networks. These attacks can have severe consequences, resulting in the unavailability of cri...
Internet Protocol version 6 (IPv6) and its core protocol, Internet Control Message Protocol version 6 (ICMPv6), need to be secured from attacks, such as Denial of Service (DoS) and Distributed DoS (DDoS), in order to be reliable for deployment. Sever...
Federated object detection over multi-site remote-sensing imagery faces two coupled risks that are not fully addressed by existing methods. Existing federated object-detection studies mainly focus on distributed training, communication efficiency, an...
The adversarial attack is crucial to improving the robustness of deep learning models; they help improve the interpretability of deep learning and also increase the security of the models in real-world applications. However, existing attack algorithm...
Recent research has revealed that subtle imperceptible perturbations can deceive well-trained neural network models, leading to inaccurate outcomes. These instances, known as adversarial examples, pose significant threats to the secure application of...
An SQL Injection Attack (SQLIA) is a major cyber security threat to Web services, and its different stages can cause different levels of damage to an information system. Attackers can construct complex and diverse SQLIA statements, which often cause...
The threat posed by adversarial examples (AEs) to deep learning applications has garnered significant attention from the academic community. In response, various defense strategies have been proposed, including adversarial example detection. A range...
This study aims to explore the optimal power allocation problem under Distributed Denial of Service (DDoS) attack in wireless communication networks. The Starkberg Equilibrium (SE) framework is employed to analyze the strategic interactions between d...
Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers c...
The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhol...
As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing s...
Low-rate Denial-of-Service (LDoS) attacks exploit periodic traffic pulses to trigger congestion while maintaining a low average rate, making them highly stealthy and difficult to distinguish from legitimate bursty traffic using threshold-based or sim...
Analyzing network traffic activities is imperative in network security to detect attack patterns. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identify...
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for...
Unmanned Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage duri...
With the rise of DDoS attacks, several machine learning-based attack detection models have been used to mitigate malicious behavioral attacks. Understanding how machine learning models work is not trivial. This is particularly true for complex and no...
The Internet of Things (IoT) system’s ever-expanding attack surface calls for a new intrusion detection system (IDS). These systems may include thousands of wireless devices that need to be protected from cyberattacks. Recent research efforts u...
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...
The adoption of deep learning-based solutions practically pervades all the diverse areas of our everyday life, showing improved performances with respect to other classical systems. Since many applications deal with sensible data and procedures, a st...
Cross-site scripting attacks represent one of the major security threats facing web applications, with Stored XSS attacks becoming the predominant form. Compared to reflected XSS, stored XSS attack payloads exhibit temporal and spatial asynchrony bet...
Today, with the emergence of the Internet of Things (IoT) in smart home technology, new security issues have arisen, including device diversity, data privacy concerns, and susceptibility to advanced, complex attacks. Traditional Intrusion Detection S...
In recent years, smart grid security has gained considerable attention. Numerous studies have proposed techniques to detect cyber-attacks using sensor data; however, limited attention has been given to distinguishing cyber intrusions from physical fa...
The way we connect with the physical world has completely changed because of the advancement of the Internet of Things (IoT). However, there are several difficulties associated with this change. A significant advancement has been the emergence of int...
The Automatic Identification System (AIS) is a fundamental component of modern Maritime Situational Awareness (MSA) systems, supporting navigation safety, vessel traffic management, and maritime surveillance. However, the absence of authentication, i...
Fingerprints have been used for decades to verify the identity of an individual for various security reasons. Attackers have developed many approaches to deceive a fingerprint verification system, ranging from the sensor level, where gummy fingers ar...
Compressed-TinyML pipelines apply distillation, quantization and pruning to fit lightweight anomaly detectors onto resource-constrained sensor nodes. Such frameworks are routinely called privacy-aware on the strength of on-device inference or noisy f...
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...
In machine learning (ML), adversarial attack (targeted or untargeted) in the presence of noise disturbs the model prediction. This research suggests that adversarial perturbations on pictures lead to noise in the features constructed by any networks....
Due to the emergence of online society, a representative user authentication method that is password authentication has been a key topic. However, in this authentication method, various attack techniques have emerged to steal passwords input from the...
The covert nature of DNS covert channels makes them a widely utilized method for data exfiltration by malicious attackers. In response to this challenge, the present study proposes a detection methodology for DNS covert channels that employs a Deep B...
In this study, the effects of titanium dioxide (TiO2) nanoparticles on the sulfate attack resistance of ordinary Portland cement (OPC) and slag-blended mortars were investigated. OPC and slag-blended mortars (OPC:Slag = 50:50) were made with water to...
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