Protecting sensitive biomedical data is a critical security challenge in decentralized healthcare environments without a trusted authority. Electroencephalography (EEG) signals constitute highly sensitive biometric data used for neurological disorder diagnosis, making privacy-preserving analysis essential. Local differential privacy (LDP) addresses this challenge by perturbing data at the source; however, selecting an appropriate privacy budget ε remains nontrivial, as it directly governs the privacy–utility trade-off. This work proposes a sensitivity-aware privacy budget selection framework for EEG-based analysis under LDP, leveraging multi-criteria decision making to jointly consider privacy level, data accuracy, computational cost, and data sensitivity. Unlike existing approaches that rely on a single global privacy budget, the proposed framework performs participant-specific privacy budget selection, accounting for differences in data sensitivity across participating data sources. Privacy budgets are adaptively selected using TOPSIS and Fuzzy TOPSIS, enabling sensitivity-aware calibration without modifying the underlying LDP mechanism. Experiments conducted on the University of Bonn EEG dataset employ time-domain, frequency-domain, nonlinear, and wavelet features, with eight machine learning classifiers evaluated on privacy-preserving data against a noise-free baseline. The results show that Fuzzy TOPSIS consistently selects smaller privacy budgets, achieving stronger privacy guarantees while maintaining a favorable balance between privacy protection and classification performance. Performance remains stable across classifiers, and the additional computational overhead is modest. Overall, the findings demonstrate that sensitivity-aware privacy budget selection enables effective privacy–utility trade-offs for EEG analysis, offering a scalable and ethically grounded solution for privacy-preserving biomedical signal processing in decentralized healthcare systems.
The implementation of IDSs for intrusion detection based on machine learning has been the subject of extensive research to address the growing threats posed by network intrusions. Nonetheless, there are several problems that must be resolved. Poor detection rates are generated for unknown threats because they are challenging to handle when they do not occur in the training set. IDSs can have a high risk of false positives. Since different models learn data features from different angles, our work introduces a hybrid intrusion detection system (IDS) that combines Random Forest (RF) and Autoencoder (AE). This study implements the combination of RF and AE intrusion detection systems. The hybrid model has two distinct operational phases. We employ the RF classifier's probability output in the first phase for analysis. The system determines, in its first phase, whether the examined sample constitutes an attack. The probability output becomes an essential element for identification. To identify unknown attacks, adding a second Autoencoder helps reduce false positives in the second stage. We specifically remove some samples from one attack class from the training set to simulate an unidentified attack in trials. Our proposed method shows a high detection rate when compared to other baselines. Furthermore, an AE detection module integrated into the system helps decrease the false detection rate. replace word moreover.
The evolution of malware continues to provide a challenge to existing detection methods, particularly those based on static analysis and signature-based heuristics. This paper investigates a memory-forensics-driven methodology for malware detection that includes feature refinement, model explainability, efficiency analysis, adversarial robustness testing, and family-disjoint validation. Using the CIC MalMem-2022 dataset, we examined 55 memory-resident features and reduced them to 13 using mutual information and SHapley Additive exPlanations-guided feature refinement (SHAP-GFR). We tested five classifiers: RF, XGBoost, (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP), one-dimensional CNN (1D CNN), and a CNN-LSTM hybrid using a 70
Partially manipulated images pose a growing threat to the reliability of online content. The rapid spread of diffusion-based inpainting tools has made the creation of such manipulations increasingly easy to perform. As a result, the multimedia forensics community is disadvantaged compared to the attackers, as developing effective localization techniques often requires the creation of large datasets, a resource-intensive process due to the necessary human effort. In this paper, we present Beyond the Brush++ (BtB++), a fully automated pipeline for generating large-scale datasets of realistic inpainted images. Our experiments demonstrate that BtB++ is both flexible and easily integrates different models and configurations, offering the adaptability required to address evolving models and application scenarios. Moreover, an automatic filtering mechanism ensures quality control by discarding low-quality generated images. To provide an initial assessment of the proposed filtering strategy, we also conducted a small-scale human evaluation, studying the alignment between human perceptual judgments and the automatic metrics used for filtering.
The popularity of drones, unmanned aerial vehicles (UAVs), and industrial-level cyber-physical systems has deepened external threats caused by network-based cyber-attacks. Such environments have a problem of dynamic traffic behavior, temporal dependencies, class imbalance and the existence of various type of attacks, such as denial of service, injection, replay attacks, scanning, and man in the middle attacks. This paper presents an effective and justifiable multi-class attack detection model in a heterogeneous environment that can be used as an intrusion detector. Three benchmark datasets, Drone IDS, UAVIDS-2025, and ICSCASD_MPLC were evaluated comprehensively with ensemble-based machine learning models (Random Forest, Extra trees, AdaBoost, XGBoost, and CatBoost) and those with deep learning architecture (ANN, CNN, RNN, LSTM, and ResNet). Within the framework of many preprocessing steps, the accuracy, macro-averaged precision, recall, F1-score, Matthews Correlation Coefficient, Cohen’s Kappa, log loss, and ROC-AUC were used to evaluate the models. According to experimental findings, Random Forest is more effective than other ensemble models, with macro F1-scores of 0.99964, 0.99844, and 0.99994 on Drone IDS, UAVIDS-2025, and ICSCASD_MPLC datasets, respectively, with nearly perfect ROC-AUC indicators. Compared to other deep learning methods, LSTM is best at learning patterns of attack over time, ANN is well-performing with minimal computing costs, and RNN is well-performing in generalizing on industrial traffic. The validity of statistical significance of results is tested with Friedman and Wilcoxon signed-rank tests with Holm correction, bootstrap confidence intervals, and McNemar test. Also, explainable tools of AI, including SHAP and LIME, provide both local and global explanations, which are both intuitive, as well as ablation testing demonstrates that a small set of flow-based and temporal features are capable of sustaining close-optimal performance. In general, the framework proposed provides real-time and safety–critical deployments with intrusion detection algorithms that are accurate, interpretable, and validated statistically.
Designing novel and effective distortion functions for spatial image steganography has become increasingly challenging. The controversial pixels prior (CPP) rule mitigates this by fusing existing distortion functions rather than constructing new ones, but it is limited to functions with comparable security performance. We propose G-CPP, a generalized fusion framework based on the grading of controversial pixels. G-CPP assigns embedding priorities according to pixel conflict levels, enabling more effective utilization of high-potential embedding locations. Furthermore, we introduce a customized scheduling strategy for the G-CPP and cost-spreading rules, tailored to the intrinsic properties of different distortion-function combinations, thereby enhancing statistical undetectability against both conventional and deep learning–based steganalyzers. G-CPP preserves the advantages of CPP while extending its applicability to functions with significantly divergent security levels. Experiments on multiple benchmark datasets show that G-CPP consistently outperforms the conventional CPP rule, achieving superior security performance across diverse function combinations.
Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text-guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non-semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine-tuning IFL methods on FR images and generative super-resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine-tuning improves localization on FR images, evaluation with random non-semantic masks reveals object bias. Furthermore, generative super-resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI-based image enhancements. TGIF2 is available at https://github.com/IDLabMedia/tgif-dataset.
This research explores the enhancement of cybersecurity systems by integrating emotion-based techniques with conventional brain-mapping measurements. By incorporating biosignals such as electroencephalography (EEG) and speech analysis, this approach allows for a more comprehensive evaluation of emotional and cognitive states, improving the reliability and robustness of lie detection. Despite the lack of external proof from such evaluations, the proposed system represents a significant advancement over conventional methods, offering deeper insights into user intentions and emotion levels. This paper presents a method for multimodal emotion detection using physiological and behavioral signals—such as EEG and speech expressions; however, several challenges arise in integrating these modalities effectively. These include the complexity of synchronizing multiple data sources, applying appropriate fusion techniques, and using advanced representation learning to extract meaningful emotional cues. Overcoming these issues is crucial to fully realizing the potential of this enhanced cybersecurity framework. The proposed model was evaluated using the newly introduced EAV and PME4 datasets, both well suited for multimodal emotion analysis owing to their synchronized EEG and speech expressions. The modality-specific weighted feature-fusing strategy was utilized to combine the features extracted from the DCCA network, and the fused features were then used to train the suggested ANN classifier. The proposed model achieved performances of 68.95
This research examines the feasibility and effectiveness of detecting ransomware attacks in quasi real-time by leveraging AI-based monitoring of centralized file operations. As ransomware continues to evolve in speed and complexity, traditional endpoint protection mechanisms often fall short, especially in environments with limited client-side defense. The goal is to determine whether lightweight, server-side monitoring combined with machine learning can provide a quasi real-time and accurate detection mechanism without relying on client instrumentation. A virtualized SME (small- and medium-sized enterprise) infrastructure was developed, simulating realistic user behavior through automated file operations and randomly triggered attacks by ransomware samples (Ryuk, NotPetya, Lockbit, Teslacrypt, and WannaCry). A nanosecond-scale time-stamped logging mechanism was implemented using Fluentbit and InfluxDB to track file creation, renaming, and deletion events. Five classic and ensemble machine learning models (Random Forest, Decision Tree, SVM, AdaBoost, XGBoost) were trained and optimized using supervised learning on aggregated file operation sequences using one-second intervals. The comparative evaluation of the models showed that all five achieved reliable detection performance, but XGBoost outperformed the others with a sensitivity of 91.87
Broken access control (BAC) remains the most critical security risk (i.e., OWASP Top 10). Although BAC is commonly tested with dynamic white-box techniques, their effectiveness hinge on the strength of the underlying test cases; weak test cases leave exploitable risks on the software. Mutation testing is widely used and has been empirically shown to be highly sensitive and reliable for evaluating test case quality. Though it is also used in software security, it remains limited for testing BAC. This study aims to improve security test cases quality for two BAC vulnerabilities: Improper Pathname Limitation (IPL) and Cross-Site Request Forgery (CSRF). We introduce 15 novel mutation operators, systematically formulated through data flow analysis to understand the nature of those vulnerabilities. The proposed operator groups, including file access check and CSRF-token related mutation operators, to simulate realistic and possible semantic fallacies that lead to security vulnerabilities. The approach was evaluated using the Quality of mutant set Coverage (QCo) and measuring the test cases improvement using Mutation Score Indicator (MSI) on 29 security test cases. Experimental results show that all operators, implemented as infectious PHP extension, achieved QCo above 85%, while test case quality improved in CSRF from 5 to 12 test cases and in IPL from 8 to 17 test cases on a PHP-based dummy project, whereas on DVWA from 6 to 8 test cases for CSRF and from 4 to 7 test cases for IPL. These findings indicate that the proposed mutation operators substantially enable developers in strengthening security test cases to reveal BAC vulnerabilities.
Distributed systems are widely used in modern environments such as cloud platforms, Internet of Things (IoT) networks, smart grids, and blockchain-based systems. These platforms often require digital signatures to maintain trust between devices or users. When many signatures are shared at once, the size of the communication grows, and processing takes more time. This becomes a problem in applications where quick message verification is important. Classical digital signature schemes, such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA), are not safe against quantum attacks. Post-quantum cryptography offers better protection, but it often increases communication size and computation cost. This paper introduces a quantum-resistant model that supports signature aggregation and constant-time verification. The method is based on lattice-based techniques, using structures similar to CRYSTALS-Dilithium. By combining several signatures into a single aggregated form, the model reduces the time needed for verification and the overall communication overhead. The design is suitable for real-time applications, offering strong accuracy and faster message handling. Simulation results show that the proposed method performs better than recent approaches in multiple areas. The model achieves a low authentication time, high verification accuracy, reduced message size, and improved throughput. These benefits help meet the needs of distributed systems where speed, accuracy, and security must work together. The architecture is designed to be flexible for use in real-world deployments. Future directions include support for multi-party signing, adaptive quantum-safe policies, and integration with quantum co-processors. This research supports the development of secure and efficient communication frameworks that remain effective even in the presence of future quantum threats.
Privacy regulations and ethical concerns have encouraged the use of privacy-friendly synthetic data for the training of facial analysis systems. However, the automated generation of images depicting the same synthetic subject in different environmental scenarios remains challenging, as identity-related features may not be accurately preserved. This is a severe issue for the training of differential morphing attack detection (MAD) algorithms, where subtle differences in facial features can indicate morphing attacks. This work introduces IDSwapMAD as a new way for generating privacy-friendly training data for differential MAD methods. In detail, a generative adversarial network is employed to generate synthetic facial images of which the faces are swapped with pairs of real reference and probe images containing variations that mimic a border control scenario. In this way, style-related properties of the reference and probe images are retained, while identity-related features are replaced. It is shown that the proposed IDSwapMAD technique is an effective and privacy-friendly strategy for training differential MAD methods, whose detection performance is on par with a state-of-the-art MAD method trained on real data.
The growing use of Internet of Things (IoT) devices has increased the need for secure and reliable communication protocols, with MQTT being one of the most widely used. However, MQTT brokers are vulnerable to denial-of-service (DoS) attacks, which can disrupt data flow in IoT systems. This research compares three MQTT brokers, Mosquitto, HiveMQ, and EMQX, to evaluate their resilience against DoS attacks, specifically the MQTT publish flooding attack. Using a testing environment replicating real-world IoT scenarios, the brokers were subjected to simulated attacks, and their performance was measured based on five key metrics: latency, packet loss, throughput, CPU usage, and recovery time. The results show varying levels of resilience among the brokers. Mosquitto demonstrated quick recovery but suffered from high latency and moderate packet loss. HiveMQ maintained low latency but experienced high packet loss and failed to recover without manual intervention. EMQX balanced performance with the lowest packet loss and a reasonable recovery time, making it the most robust among the three. These findings provide insights into selecting suitable MQTT brokers for IoT deployments, emphasizing the importance of considering factors such as latency, message reliability, and recovery capability when dealing with potential DoS attacks.
Speaker recognition is the task of identifying or verifying a person's identity using their voice. This problem involves challenges like variations in speech due to emotional states, health conditions, heterogeneity of microphone models, different environments and background noise. Accurate speaker recognition is critical for security, personalization, and forensic applications. Applying a CNN with Monte Carlo dropout can enhance Speaker Recognition by enabling robust uncertainty-aware predictions, making the presented architecture particularly effective for smaller, noisy datasets without the need for large-scale pre-training. This approach helps mitigate overfitting and improves generalization, making it effective in handling diverse speech patterns. The designed deep learning model showcases superior performance in multiple dimensions, achieving a peak validation accuracy of 93.27% for speaker recognition on a specific dataset recorded in the wild by phone, and 0.030 of EER, showing competitive performance with respect to state-of-the-art baselines.
We establish the randomized distributed function computation (RDFC) framework, in which a sender transmits just enough information for a receiver to generate a randomized function of the input data. Describing RDFC as a form of semantic communication, which can be essentially seen as a generalized remote-source-coding problem, we show that security and privacy constraints naturally fit this model, as they generally require a randomization step. Using strong coordination metrics, we ensure (local differential) privacy for every input sequence and prove that such guarantees can be met even when no common randomness is shared between the transmitter and receiver. This work provides lower bounds on Wyner’s common information (WCI), which is the communication cost when common randomness is absent, and proposes numerical techniques to evaluate the other corner point of the RDFC rate region for continuous-alphabet random variables with unlimited shared randomness. Experiments illustrate that a sufficient amount of common randomness can reduce the semantic communication rate by up to two orders of magnitude compared to the WCI point, while RDFC without any shared randomness still outperforms lossless transmission by a large margin. A finite blocklength analysis further confirms that the privacy parameter gap between the asymptotic and non-asymptotic RDFC methods closes exponentially fast with input length. Our results position RDFC as an energy-efficient semantic communication strategy for privacy-aware distributed computation systems.
Exponentially expanding domain name system (DNS) over HTTPS (DoH) has significantly increased privacy but has also quietly masked malicious activities, rendering traditional threat detection systems meaningless. Existing deep learning-powered systems are unable to detect fleeting micro-temporal abnormalities in encrypted streams, are too costly for real-time operation, and are still vulnerable to adversarial attacks. To overcome these complex issues, this work proposes a new architecture—Neuromorphic Quantum Adversarial Learning (NQAL)—a bio-inspired, zero-knowledge-supported detection mechanism combining spiking neural networks (SNNs), quantum noise injection (QNI), and federated swarm intelligence to immunize, rather than detect, DoH-based attacks. The method relies on a neuromorphic model employing Dynamic Spiking Graph Attention (DSGAT) and Spike-Timing-Dependent Plasticity (STDP) to encode encrypted traffic as dynamic spike trains to enable ultra-fast, energy-efficient inference on processors such as Intel Loihi and BrainChip Akida. Quantum adversarial noise, emulated through stochastic perturbations created from quantum random walks, is injected during training to build gradient-obfuscating robustness. A threat immunization engine powered by adversarial GANs and quantum perturbations to simulate zero-day anomalies for preconditioning the model. Zero-knowledge verification is guaranteed through zk-SNARKs for privacy-preserving confirmation of anomalies without decrypting packets. Empirical studies confirm that NQAL achieves 99.18 <1 ms latency, and 10x less energy consumption than GPU-based models, while also being robust to both classical and quantum adversarial attacks. Feasibility, novelty, and decentralization of the system amount to a paradigm shift from existing architectures—hence, making NQAL a resilient frontier in encrypted traffic immunization.
Advanced persistent threat (APT) attribution is a key defense strategy that can effectively safeguard the security of critical assets and systems. Cyber threat intelligence (CTI) contains rich information about APT groups that can be leveraged for attribution. However, most existing studies focus on a single feature from different perspectives, neglecting the multi-level mining and combined features of CTI, which limits the depth and accuracy of attribution analysis and may even lead to misleading conclusions. To overcome these limitations, we propose a multi-level feature Dempster–Shafer joint (MLDSJ) attribution method for APT groups based on threat intelligence. Specifically, we extract multi-level features such as attack patterns, textual information, and graph topology from CTI reports to construct feature vectors. Subsequently, we classify the three types of features separately using simple machine learning models. Finally, we introduce Dempster–Shafer (DS) evidence theory and apply the Dempster combination rule to integrate the three feature types and determine the final attribution. Experimental results show that our method outperforms the baseline in classification, achieving an accuracy of 89.9
Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in terms of performance but require more data and time. To tackle these challenges, we present EnLeM, an ensemble learning model designed specifically for phishing website detection. EnLeM brings together three well-known machine learning classifiers—decision tree, random forest, and k-nearest neighbor—using a hard voting mechanism, and further strengthens efficiency with Mutual Information–based feature selection. When tested on the UCI phishing dataset, EnLeM delivered strong results, reaching 97.21
Anonymization of graph data is fundamental to preserving users’ privacy while publishing social network datasets. The strongest privacy guarantees against any structural attacks provide three well-known methods: k-automorphism, k-isomorphism and k-symmetry. These methods have been proposed independently and are often considered distinct, although certain relationships between them have been noted. This paper presents a comprehensive theoretical analysis of the relationships between these methods. A refined definition of k-automorphism is introduced, formalizing conditions implicitly assumed in practical algorithms. Using this enhanced definition, it is formally proved that k-symmetry and k-automorphism are equivalent. Additionally, the relationship between these two methods and k-isomorphism is analyzed. A novel proof demonstrates that a k-automorphic graph necessarily contains k isomorphic subgraphs. The practical relevance of the provided theoretical results is shown by comparing existing anonymization algorithms. This work contributes to a deeper mathematical understanding of privacy guarantees in graph-structured data, supporting the design of anonymization methods in network security.
Having a strong password is vital in maintaining secure access to private or sensitive data. However, strong passwords require good memorization skills, placing a significant burden on human memory and cognitive capacity. Using additional authentication measures, such as token-based access, reduces the need for overly complex passwords while maintaining a high level of security. However, using additional measures introduces additional user interaction during the log-in process. In this work, we propose a password hardening scheme that provides a location based authentication mechanism. We use the information contained within the local WiFi environment to strengthen a user’s password. With our method, the requirements on the user password remain at a reasonable level, while keeping extra user involvement to a minimum. We achieve this by generating a cryptographic key from WiFi beacon frames, which we combine with the user password using a key derivation function. Furthermore, we conduct an analysis to assess the stability of local WiFi environments to determine the practicality of our proposed password hardening scheme.
