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Seyit Camtepe

Publications and source records attributed to Seyit Camtepe.

At least 55 records · Page 3Linked to original sources
arXiv2023

Radio Signal Classification by Adversarially Robust Quantum Machine Learning

Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Researchers have underscored the high susceptibility of ML algorithms for radio signal classification to adversarial attacks. Such vulnerability could result in severe consequences, including misinterpretation of critical messages, interception of classified information, or disruption of communication channels. Recent advancements in quantum computing have revolutionized theories and implementations of computation, bringing the unprecedented development of Quantum Machine Learning (QML). It is shown that quantum variational classifiers (QVCs) provide notably enhanced robustness against classical adversarial attacks in image classification. However, no research has yet explored whether QML can similarly mitigate adversarial threats in the context of radio signal classification. This work applies QVCs to radio signal classification and studies their robustness to various adversarial attacks. We also propose the novel application of the approximate amplitude encoding (AAE) technique to encode radio signal data efficiently. Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack. However, the converse is not true. QVCs primarily resist the attacks generated on CNNs. Overall, with comprehensive simulations, our results shed new light on the growing field of QML by bridging knowledge gaps in QAML in radio signal classification and uncovering the advantages of applying QML methods in practical applications.

quant-ph↗
arXiv2023

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this work, we propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors when confronted with adversarial attacks. The framework introduces the concept of Accrued Malicious Magnitude (AMM) to identify which malware features could be manipulated to maximize the likelihood of evading detection. We then use this framework to test several state-of-the-art malware detectors' abilities to detect manipulated malware. We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i.e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i.e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.

cs.CR↗
arXiv2023

Access-based Lightweight Physical Layer Authentication for the Internet of Things Devices

Physical-layer authentication is a popular alternative to the conventional key-based authentication for internet of things (IoT) devices due to their limited computational capacity and battery power. However, this approach has limitations due to poor robustness under channel fluctuations, reconciliation overhead, and no clear safeguard distance to ensure the secrecy of the generated authentication keys. In this regard, we propose a novel, secure, and lightweight continuous authentication scheme for IoT device authentication. Our scheme utilizes the inherent properties of the IoT devices' transmission model as its source for seed generation and device authentication. Specifically, our proposed scheme provides continuous authentication by checking the access time slots and spreading sequences of the IoT devices instead of repeatedly generating and verifying shared keys. Due to this, access to a coherent key is not required in our proposed scheme, resulting in the concealment of the seed information from attackers. Our proposed authentication scheme for IoT devices demonstrates improved performance compared to the benchmark schemes relying on physical channels. Our empirical results find a near threefold decrease in the misdetection rate of illegitimate devices and close to zero false alarm rate in various system settings with varied numbers of active devices up to 200 and signal-to-noise ratio from 0 dB to 25 dB. Our proposed authentication scheme also has a lower computational complexity of at least half the computational cost of the benchmark schemes based on support vector machine and binary hypothesis testing in our studies. This further corroborates the practicality of our scheme for IoT deployments.

cs.CR↗
arXiv2023

Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks

Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single type of perturbation while various attack methods might be adopted to perform stronger adversarial attacks against the deployed model in real-world scenarios, e.g., $\ell_2$ or $\ell_\infty$. Defending against various attacks can be a challenging problem since multi-perturbation adversarial training and its variants only achieve suboptimal robustness trade-offs, due to the theoretical limit to multi-perturbation robustness for a single model. Besides, it is impractical to deploy large models in some storage-efficient scenarios. To settle down these drawbacks, in this paper we propose a novel multi-perturbation adversarial training framework, parameter-saving adversarial training (PSAT), to reinforce multi-perturbation robustness with an advantageous side effect of saving parameters, which leverages hypernetworks to train specialized models against a single perturbation and aggregate these specialized models to defend against multiple perturbations. Eventually, we extensively evaluate and compare our proposed method with state-of-the-art single/multi-perturbation robust methods against various latest attack methods on different datasets, showing the robustness superiority and parameter efficiency of our proposed method, e.g., for the CIFAR-10 dataset with ResNet-50 as the backbone, PSAT saves approximately 80\% of parameters with achieving the state-of-the-art robustness trade-off accuracy.

cs.CV↗
arXiv2023

Stealthy Physical Masked Face Recognition Attack via Adversarial Style Optimization

Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking various face accessories into consideration, like glasses, hats, and masks. In the COVID-19 pandemic era, wearing face masks is one of the most effective ways to defend against the novel coronavirus. However, DNNs are known to be vulnerable to adversarial examples with a small but elaborated perturbation. Thus, a facial mask with adversarial perturbations may pose a great threat to the widely used deep learning-based FR models. In this paper, we consider a challenging adversarial setting: targeted attack against FR models. We propose a new stealthy physical masked FR attack via adversarial style optimization. Specifically, we train an adversarial style mask generator that hides adversarial perturbations inside style masks. Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner. We simultaneously optimize the generator and the style selection for generating strong and stealthy adversarial style masks. We evaluated the effectiveness and transferability of our proposed method via extensive white-box and black-box digital experiments. Furthermore, we also conducted physical attack experiments against local FR models and online platforms.

cs.CV↗
arXiv2023

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.

cs.LG↗
arXiv2023

SplITS: Split Input-to-State Mapping for Effective Firmware Fuzzing

Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost firmware in emulators and attempt to facilitate inputs from a diversity of methods (interrupt driven, status polling) and a plethora of devices (such as modems and GPS units). Despite recent progress to tackle peripheral input generation challenges in rehosting, a firmware's expectation of multi-byte magic values supplied from peripheral inputs for string operations still pose a significant roadblock. We solve the impediment posed by multi-byte magic strings in monolithic firmware. We propose feedback mechanisms for input-to-state mapping and retaining seeds for targeted replacement mutations with an efficient method to solve multi-byte comparisons. The feedback allows an efficient search over a combinatorial solution-space. We evaluate our prototype implementation, SplITS, with a diverse set of 21 real-world monolithic firmware binaries used in prior works, and 3 new binaries from popular open source projects. SplITS automatically solves 497% more multi-byte magic strings guarding further execution to uncover new code and bugs compared to state-of-the-art. In 11 of the 12 real-world firmware binaries with string comparisons, including those extensively analyzed by prior works, SplITS outperformed, statistically significantly. We observed up to 161% increase in blocks covered and discovered 6 new bugs that remained guarded by string comparisons. Significantly, deep and difficult to reproduce bugs guarded by comparisons, identified in prior work, were found consistently. To facilitate future research in the field, we release SplITS, the new firmware data sets, and bug analysis at https://github.com/SplITS-Fuzzer

cs.CR↗
arXiv2023

Data-Driven Intelligence can Revolutionize Today's Cybersecurity World: A Position Paper

As cyber threats evolve and grow progressively more sophisticated, cyber security is becoming a more significant concern in today's digital era. Traditional security measures tend to be insufficient to defend against these persistent and dynamic threats because they are mainly intuitional. One of the most promising ways to handle this ongoing problem is utilizing the potential of data-driven intelligence, by leveraging AI and machine learning techniques. It can improve operational efficiency and saves response times by automating repetitive operations, enabling real-time threat detection, and facilitating incident response. In addition, it augments human expertise with insightful information, predictive analytics, and enhanced decision-making, enabling them to better understand and address evolving problems. Thus, data-driven intelligence could significantly improve real-world cybersecurity solutions in a wide range of application areas like critical infrastructure, smart cities, digital twin, industrial control systems and so on. In this position paper, we argue that data-driven intelligence can revolutionize the realm of cybersecurity, offering not only large-scale task automation but also assist human experts for better situation awareness and decision-making in real-world scenarios.

cs.CR↗
arXiv2023

Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network

We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode their transmitted data. We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame. Exploiting the temporal correlation, we propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the MUD problem. The BiLSTM network creates a pattern of the device activation history using forward and reverse pass LSTMs, whereas the attention mechanism provides essential context to the device activation points. By doing so, a hierarchical pathway is followed for detecting active devices in a grant-free scenario. Then, by utilising the complex spreading sequences, blind data detection for the estimated active devices is performed. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.

eess.SP↗
arXiv2023

Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods

DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or network. Distinguishing between legitimate traffic and malicious traffic is a challenging task. It is possible to classify legitimate traffic and malicious traffic and analysis the network traffic by using machine learning and deep learning techniques. However, an inter-model explanation implemented to classify a traffic flow whether is benign or malicious is an important investigation of the inner working theory of the model to increase the trustworthiness of the model. Explainable Artificial Intelligence (XAI) can explain the decision-making of the machine learning models that can be classified and identify DDoS traffic. In this context, we proposed a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the classifier model. To address this concern, we first adopt feature selection techniques to select the top 20 important features based on feature importance techniques (e.g., XGB-based SHAP feature importance). Following that, the Multi-layer Perceptron Network (MLP) part of our proposed model uses the optimized features of the DDoS attack dataset as inputs to classify legitimate and malicious traffic. We perform extensive experiments with all features and selected features. The evaluation results show that the model performance with selected features achieves above 99\% accuracy. Finally, to provide interpretability, XAI can be adopted to explain the model performance between the prediction results and features based on global and local explanations by SHAP, which can better explain the results achieved by our proposed framework.

cs.CR↗
arXiv2023

Task-Aware Meta Learning-based Siamese Neural Network for Classifying Obfuscated Malware

Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware families when such obfuscated malware samples are present in the training dataset, resulting in high false-positive rates. To address this issue, we propose a novel task-aware few-shot-learning-based Siamese Neural Network that is resilient against the presence of malware variants affected by such control flow obfuscation techniques. Using the average entropy features of each malware family as inputs, in addition to the image features, our model generates the parameters for the feature layers, to more accurately adjust the feature embedding for different malware families, each of which has obfuscated malware variants. In addition, our proposed method can classify malware classes, even if there are only one or a few training samples available. Our model utilizes few-shot learning with the extracted features of a pre-trained network (e.g., VGG-16), to avoid the bias typically associated with a model trained with a limited number of training samples. Our proposed approach is highly effective in recognizing unique malware signatures, thus correctly classifying malware samples that belong to the same malware family, even in the presence of obfuscated malware variants. Our experimental results, validated by N-way on N-shot learning, show that our model is highly effective in classification accuracy, exceeding a rate \textgreater 91\%, compared to other similar methods.

cs.CR↗
arXiv2023

OptimShare: A Unified Framework for Privacy Preserving Data Sharing -- Towards the Practical Utility of Data with Privacy

Tabular data sharing serves as a common method for data exchange. However, sharing sensitive information without adequate privacy protection can compromise individual privacy. Thus, ensuring privacy-preserving data sharing is crucial. Differential privacy (DP) is regarded as the gold standard in data privacy. Despite this, current DP methods tend to generate privacy-preserving tabular datasets that often suffer from limited practical utility due to heavy perturbation and disregard for the tables' utility dynamics. Besides, there has not been much research on selective attribute release, particularly in the context of controlled partially perturbed data sharing. This has significant implications for scenarios such as cross-agency data sharing in real-world situations. We introduce OptimShare: a utility-focused, multi-criteria solution designed to perturb input datasets selectively optimized for specific real-world applications. OptimShare combines the principles of differential privacy, fuzzy logic, and probability theory to establish an integrated tool for privacy-preserving data sharing. Empirical assessments confirm that OptimShare successfully strikes a balance between better data utility and robust privacy, effectively serving various real-world problem scenarios.

cs.CR↗
arXiv2023

Reconstruction-based LSTM-Autoencoder for Anomaly-based DDoS Attack Detection over Multivariate Time-Series Data

A Distributed Denial-of-service (DDoS) attack is a malicious attempt to disrupt the regular traffic of a targeted server, service, or network by sending a flood of traffic to overwhelm the target or its surrounding infrastructure. As technology improves, new attacks have been developed by hackers. Traditional statistical and shallow machine learning techniques can detect superficial anomalies based on shallow data and feature selection, however, these approaches cannot detect unseen DDoS attacks. In this context, we propose a reconstruction-based anomaly detection model named LSTM-Autoencoder (LSTM-AE) which combines two deep learning-based models for detecting DDoS attack anomalies. The proposed structure of long short-term memory (LSTM) networks provides units that work with each other to learn the long short-term correlation of data within a time series sequence. Autoencoders are used to identify the optimal threshold based on the reconstruction error rates evaluated on each sample across all time-series sequences. As such, a combination model LSTM-AE can not only learn delicate sub-pattern differences in attacks and benign traffic flows, but also minimize reconstructed benign traffic to obtain a lower range reconstruction error, with attacks presenting a larger reconstruction error. In this research, we trained and evaluated our proposed LSTM-AE model on reflection-based DDoS attacks (DNS, LDAP, and SNMP). The results of our experiments demonstrate that our method performs better than other state-of-the-art methods, especially for LDAP attacks, with an accuracy of over 99.

cs.CR↗
arXiv2023

Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework

To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable systems is desirable. Threat intelligence is crucial for guiding Security Operations Center (SOC) analysts' focus toward specific system activity and provides the primary contextual foundation for interpreting security alerts. This paper explores novel approaches for improving incident response triage operations, including dealing with attacks and zero-day malware. This solution for rapid prioritization of different malware have been raised to formulate fast response plans to minimize socioeconomic damage from the massive growth of malware attacks in recent years, it can also be extended to other incident response. We propose a malware triage approach that can rapidly classify and prioritize different malware classes to address this concern. We utilize a pre-trained ResNet18 network based on Siamese Neural Network (SNN) to reduce the biases in weights and parameters. Furthermore, our approach incorporates external task memory to retain the task information of previously encountered examples. This helps to transfer experience to new samples and reduces computational costs, without requiring backpropagation on external memory. Evaluation results indicate that the classification aspect of our proposed method surpasses other similar classification techniques in terms of performance. This new triage strategy based on task memory with meta-learning evaluates the level of similarity matching across malware classes to identify any risky and unknown malware (e.g., zero-day attacks) so that a defense of those that support critical functionality can be conducted.

cs.CR↗
arXiv2023

Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps

The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world's most marginalized children. However, while legislatures around the world have enacted regulations to protect children's online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20,195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children's apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children's privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers.

cs.CR↗
arXiv2023

On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Radio frequency fingerprint identification (RFFI) is a lightweight device authentication technique particularly desirable for power-constrained devices, e.g., the Internet of things (IoT) devices. Similar to biometric fingerprinting, RFFI exploits the intrinsic and unique hardware impairments resulting from manufacturing, such as power amplifier (PA) nonlinearity, to develop methods for device detection and classification. Due to the nature of wireless transmission, received signals are volatile when communication environments change. The resulting radio frequency fingerprints (RFFs) are distorted, leading to low device detection and classification accuracy. We propose a PA nonlinearity quotient and transfer learning classifier to design the environment-robust RFFI method. Firstly, we formalized and demonstrated that the PA nonlinearity quotient is independent of environmental changes. Secondly, we implemented transfer learning on a base classifier generated by data collected in an anechoic chamber, further improving device authentication and reducing disk and memory storage requirements. Extensive experiments, including indoor and outdoor settings, were carried out using LoRa devices. It is corroborated that the proposed PA nonlinearity quotient and transfer learning classifier significantly improved device detection and device classification accuracy. For example, the classification accuracy was improved by 33.3% and 34.5% under indoor and outdoor settings, respectively, compared to conventional deep learning and spectrogram-based classifiers.

eess.SP↗
arXiv2023

An Additive Instance-Wise Approach to Multi-class Model Interpretation

Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner. The process is thus inefficient and susceptible to poorly-conditioned samples. Meanwhile, many selection-based methods directly optimize local feature distributions in an instance-wise training framework, thereby being capable of leveraging global information from other inputs. However, they can only interpret single-class predictions and many suffer from inconsistency across different settings, due to a strict reliance on a pre-defined number of features selected. This work exploits the strengths of both methods and proposes a framework for learning local explanations simultaneously for multiple target classes. Our model explainer significantly outperforms additive and instance-wise counterparts on faithfulness with more compact and comprehensible explanations. We also demonstrate the capacity to select stable and important features through extensive experiments on various data sets and black-box model architectures.

cs.LG↗
arXiv2022

PublicCheck: Public Integrity Verification for Services of Run-time Deep Models

Existing integrity verification approaches for deep models are designed for private verification (i.e., assuming the service provider is honest, with white-box access to model parameters). However, private verification approaches do not allow model users to verify the model at run-time. Instead, they must trust the service provider, who may tamper with the verification results. In contrast, a public verification approach that considers the possibility of dishonest service providers can benefit a wider range of users. In this paper, we propose PublicCheck, a practical public integrity verification solution for services of run-time deep models. PublicCheck considers dishonest service providers, and overcomes public verification challenges of being lightweight, providing anti-counterfeiting protection, and having fingerprinting samples that appear smooth. To capture and fingerprint the inherent prediction behaviors of a run-time model, PublicCheck generates smoothly transformed and augmented encysted samples that are enclosed around the model's decision boundary while ensuring that the verification queries are indistinguishable from normal queries. PublicCheck is also applicable when knowledge of the target model is limited (e.g., with no knowledge of gradients or model parameters). A thorough evaluation of PublicCheck demonstrates the strong capability for model integrity breach detection (100% detection accuracy with less than 10 black-box API queries) against various model integrity attacks and model compression attacks. PublicCheck also demonstrates the smooth appearance, feasibility, and efficiency of generating a plethora of encysted samples for fingerprinting.

cs.CR↗