Reviewerly/exHarmony huggingface dataset:
GNN-Based Energy-Efficient Anomaly Detection for IoT Multivariate Time-Series Data

Anomaly detection is an important topic in the Internet of Things (IoT). Recently, some anomaly detection methods based on graph neural networks (GNNs) have gained much attention. However, such methods require a large amount of sensory data for inference, which leads to high energy consumption for data transmission and can hardly be applied in IoT scenarios. This paper proposes a subgraph-based anomaly detection strategy as an energy-efficient anomaly detection method. To accomplish this task, we use graph structure generation to divide subgraphs by feature similarity and reduce energy consumption for data transmission. To validate the effectiveness of our mechanism, we use real-world IoT multivariate time-series data for modelling. The results show that our scheme is more energy efficient and has higher precision compared to other methods in anomaly detection.
Encrypted Voice Traffic Fingerprinting: An Adaptive Network Traffic Feature Encoding Model

Recent studies show that the traffic fingerprints constructed by encrypted voice traffic features will uncover users' activities. Current researches have focused on using machine learning models to build traffic fingerprints, however, they pay less attention to the distribution of traffic data. We note that encrypted voice traffic data is bimodal distributed, which is different from other encrypted traffic following Gaussian distribution. It means that present encoding model may destroy the distribution characteristics of encrypted voice traffic data, leading to a decrease in classification accuracy. To ameliorate this issue, our study proposes the combination of Discrete Fourier Transform and Stacked Autoencoder as traffic feature encoding model. The former is able to map the encrypted voice traffic features from bimodal distribution to frequency-domain space while keeping the trend of its features. And the latter can further improve features utilization in classification model. Finally, we achieved 96.08% accuracy on Amazon Echo dataset and 99.53% accuracy on Google Home dataset, which get the SOTA(state of the art) results on both encrypted voice traffic datasets.
Depersonalized Federated Learning: Tackling Statistical Heterogeneity by Alternating Stochastic Gradient Descent

Federated learning (FL), which has gained increasing attention recently, enables distributed devices to train a common machine learning (ML) model for intelligent inference cooperatively without data sharing.However, problems in practical networks, such as non-independent-and-identicallydistributed (non-iid) raw data and limited bandwidth, give rise to slow and unstable convergence of the FL training process.To address these issues, we propose a new FL method that can significantly mitigate statistical heterogeneity through the depersonalization mechanism.Particularly, we decouple the global and local optimization objectives by alternating stochastic gradient descent, thus reducing the accumulated variance in local update phases to accelerate the FL convergence.Then we analyze the proposed method in detail to show the proposed method converging at a sublinear speed in the general non-convex setting.Finally, numerical results are conducted with experiments on public datasets to verify the effectiveness of our proposed method.
FLORAS: Differentially Private Wireless Federated Learning Using Orthogonal Sequences

We propose a novel private-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for wireless federated learning (FL) systems. From the communication design perspective, FLORAS eliminates the requirement of channel state information at the transmitters (CSIT) by leveraging the properties of orthogonal sequences. From the privacy perspective, we prove that FLORAS can offer pure differential privacy (DP) guarantee, and explicitly characterize the achievable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -DP level as a function of the FLORAS parameter configuration. A novel FL convergence bound is derived which, combined with the pure DP guarantee, allows for a smooth tradeoff between convergence rate and DP guarantee levels. Experiments based on real-world datasets not only corroborate the theoretical findings but also empirically demonstrate the communication and privacy advantages of FLORAS over state-of-the-art AirComp methods.
Data Flow-driven and Attention Mechanism-enabled Smart Contract Vulnerability Detection for Secure and Green Blockchain-based Service Networks

In recent years, applying smart contract to Blockchain-based Service Networks (BSNs) has been considered as one of the most promising solution to boost the integration and adoption of Blockchain in big businesses. However, smart contract are especially vulnerable to attack due to poor coding. Although many existing vulnerability detection tools are restricted by rigorous rules that are defined by the experts in advance, these tools are observed to have a high false positive rate in practice. Thus we propose a vulnerability detection framework for smart contract based on the attention mechanism and data flow. The code of smart contract is transformed to a data flow according to the abstract syntax tree that is built from the code. The data flow we built with smart contract code could represent the relationships of code semantic logic. Source code, data flow, and the tags of smart contract code are used as datasets to mask processing. Then, we construct a bidirectional multi-layer transformer architecture based on the attention mechanism to train our dataset. After training, we can get the label of whether there is a vulnerability in the final smart contract. Finally, the model we proposed reaches state-of-the-art results in the practical experiments of smart contract vulnerability detection with 92.54%, 81.79%, and 86.84% in the results Accuracy, Recall, and F1 <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">score</inf> , respectively.
Interference-Aware Constellation Design for Z-Interference Channels with Imperfect CSI

A deep autoencoder (DAE)-based end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper.The design is for imperfect channel state information (CSI) where both estimation and quantization errors exist.The proposed structure jointly optimizes the encoders and decoders to generate interferenceaware constellations that adapt their shape to the interference intensity in order to minimize the bit error rate.A normalization layer is designed to guarantee an average power constraint in the DAE while allowing the architecture to generate constellations with nonuniform shapes.This brings further shaping gain compared to standard uniform constellations such as quadrature amplitude modulation.The performance of the DAE-ZIC is compared with two conventional methods, i.e., standard and rotated constellations.The proposed structure significantly enhances the performance of the ZIC.Simulation results confirm bit error rate reduction in all interference regimes (weak, moderate, and strong).At a signal-to-noise ratio of 20dB, the improvements reach about two orders of magnitude when only quantization error exists, indicating that the DAE-ZIC is highly robust to the interference compared to the conventional methods.
FAST: Fidelity-Adjustable Semantic Transmission Over Heterogeneous Wireless Networks

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.
Enabling AI Quality Control via Feature Hierarchical Edge Inference

With the rise of edge computing, various AI services are expected to be available at a mobile side through the inference based on deep neural network (DNN) operated at the network edge, called edge inference (EI). On the other hand, the resulting AI quality (e.g., mean average precision in objective detection) has been regarded as a given factor, and AI quality control has yet to be explored despite its importance in addressing the diverse demands of different users. This work aims at tackling the issue by proposing a feature hierarchical EI (FHEI), comprising feature network and inference network deployed at an edge server and corresponding mobile, respectively. Specifically, feature network is designed based on feature hierarchy, a one-directional feature dependency with a different scale. A higher scale feature requires more computation and communication loads while it provides a better AI quality. The tradeoff enables FHEI to control AI quality gradually w.r t, communication and computation loads, leading to deriving a near-to-optimal solution to maximize multi-user AI quality under the constraints of uplink & downlink transmissions and edge server and mobile computation capabilities. It is verified by extensive simulations that the proposed joint communication-and-computation control on FHEI architecture always outperforms several benchmarks by differentiating each user's AI quality depending on the communication and computation conditions.
On Using Hyperledger Fabric Over Networks: Ordering Phase Improvements

Blockchain is increasingly being used in various research disciplines, such as Internet of Things (IoT), Software Defined Networking (SDN), logistics, etc. Hyperledger Fabric is a popular enterprise-grade blockchain framework that falls into the category of the permissioned blockchains, making it particularly effective at ensuring transparency for secure communication among peers, such as the SDN controllers of an IoT network. One of the most prominent aspects of Hyperledger Fabric is its three-phase transaction flow architecture, which consists of the execution, ordering and validation phases. The ordering phase involves communication between the client and the ordering service, as the latter is responsible for the transaction assembly and distribution. This study reconstructs the ordering phase and proposes a mechanism for faster communication between the client and the ordering service. While adjusting various blockchain parameters, a performance analysis is carried out to examine the blockchain behavior with the applied mechanism. The experiments reveal performance improvements on various metrics when compared to the existing ordering phase. Notably, the proposed mechanism can be easily integrated in a future Hyperledger Fabric release.
Cyber Resilience Measurement Through Logical Attack Graph Analysis

To improve resilience, it is crucial to quantify or measure it. Measurement techniques usually base their measure on critical functionality, which is unfortunately not mission-centric. Also, methods of measurement over time can not tackle the fact that a system may have different consecutive missions at different intervals of time. We propose a method to measure the cyber-resilience of any complex network by analyzing how the business process varies against adversity effort. Both efforts of the attacker and the impact on the business process are obtained by leveraging the vulnerabilities CVSS score of attack paths extracted from a generated attack graph. We finally obtain a numerical value for cyber resilience by calculating the area under the curve of business process against attacker effort. Experimentation shows that the proposed framework suits the absorption, recovery, and adaptation abilities of cyber resilience. This also helps designers to analyze which type of vulnerabilities leads to the worst resilience case, thereby making critical decisions to improve cyber resilience.
TriFSS: Secure Trigonometric Function Evaluation via Function Secret Sharing

Trigonometric functions are crucial non-linear operations used in scientific computation and complex machine learning models. However, existing secure computing frameworks either lack support for these operations, or suffer from undesirable performance bottleneck. In this paper, we present an efficient and precise fixed-point framework called TriFSS for securely evaluating trigonometric functions. Specifically, we first design new building blocks based on advanced Function Secret Sharing techniques, achieving reduced communication and computation overhead. Second, with these efficient components, we propose a general evaluation process for these functions, in which periodic properties are fully exploited for better performance. Moreover, we implement the TriFSS framework and conduct extensive experiments. The experimental results show that our protocols achieve at least 23x less communication overhead and 2.8x less latency than the state-of-the-art frameworks, while only resulting in 1 ULP error, which is comparable to floating-point based works.
BotCS: A Lightweight Model for Large-Scale Twitter Bot Detection Comparable to GNN-Based Models

Social bot detection methods using graph neural networks (GNNs) are thriving, but the structural complexity of GNN also brings more training costs on large-scale data and interpretability concerns. In this paper, we propose a social bot detection method, BotCS, which utilizes both the attribute and the structural features of the social graph at a smaller computational cost than GNN-based detection methods. BotCS makes a base prediction with a simple multilayer perceptron classifier (MLP) and then propagates the classification residuals of the training set to other nodes for further correction. Then, it smooths the corrected prediction by label propagation. With little end-to-end training, this course is low-cost and scalable. We analyze the local interaction pattern between bots and human users, and designed the corresponding residual propagation and smoothing rules from the local perspective, which ensures the interpretability of BotCS. Experimental results show that BotCS achieves similar detection results to state-of-the-art methods with one or two orders of magnitude fewer parameters.
Making Watermark Survive Model Extraction Attacks in Graph Neural Networks

Collecting graph data is costly and well-trained graph neural networks (GNNs) are viewed as intellectual property. To make better use of GNNs, they are used to provide cloud-based services. However, models on cloud-based services may be leaked under model extraction attacks. Adversaries can extract an imitation model by simply querying the GNNs on the cloud-based services. To protect GNNs, watermarks are embedded in the models. However, the watermarks can be removed by the model extraction attacks. To address this issue, we propose adding a watermark that cannot be ignored by queries from the model extraction attacks. Concretely, we add the soft nearest neighbor loss to the loss function of the watermark embedding process to merge the distributions for the normal tasks and watermarks. We also observe that the watermark brings a performance loss to GNNs and propose an optimization method to maintain the model performance. We evaluate our method on multiple real-world datasets to demonstrate the superiority of the method.
Comparative Study of Machine Learning Techniques for Detecting GPS Spoofing Attacks on Mission Critical Military IoT Devices

In military Internet of Things (IoT) devices, which rely on accurate GPS information for navigation, communication, and other critical functions, GPS spoofing has become a growing threat. A method based on artificial intelligence has been proposed for detecting GPS spoofing attacks on military IoT devices. Using Python programming, we built a synthetic GPS dataset to address this issue. We evaluated the selected machine learning models on a synthetic dataset of GPS spoofing attacks. The results of our work indicate that LightXGBoost outperforms traditional algorithms, such as SVM and KNN, in terms of accuracy, precision, and F1 Score. XGBoost, on the other hand, provides good results in terms of detection time and memory consumption. Additionally, we have demonstrated that synthetic datasets can be useful for detecting spoofing attacks on IoT devices using XGBoost and LightXGBoost.
On Augmented Intelligence and Performance Anomaly Detection in Unlabeled OpenWiFi Data

Performance degradation of OpenWiFi traffic is a significant problem while provisioning service to a dense area consisting of thousands of clients operating at the same time. Among various vulnerabilities of poor performance in Wireless Local Area Networks (WLANs), deviation of a traffic pattern from normal indicates probable presence of anomaly or outlier. Adoption of machine learning algorithms including unsupervised and supervised models, the complexity of detection of a anomalous traffic along with respective root cause is untangled with augmented machine learning to involve domain knowledge input. In this paper, to cope with unlabeled data in an OpenWiFi setting, the following systematic work flow is proposed to augment machine learning-based anomaly detection. First, a combination of two unsupervised clustering algorithms is used to segregate the anomalies from normal distribution of traffic. The anomalous instances are confirmed via domain knowledge input. Next, supervised models are trained to detect anomalies in a different domain (Wireless Sensor Networks) with labeled data. Following upon feature extraction to obtain the same number of dimensions in the OpenWiFi data as the labeled dataset, trained supervised classifiers are used to detect anomalies in the OpenWiFi system. Furthermore, the impact of oversampling methods have also been investigated. Through numerical results we show the impact of the proposed supervised models in terms of decision-making metrics and the shift in performances from the standpoint of imbalanced distribution of rare-class classification problem.
Verifiable and Privacy-Preserving Cooperative Federated Learning in UAV-Assisted Vehicular Networks

Federated learning (FL) is a promising distributed learning paradigm, which enables devices to collaboratively train an AI model without exposing participants' private data. However, FL is vulnerable to various attacks and thus remains exposed to privacy issues. For example, malicious parties can launch attacks to recover sensitive and private training data from the shared parameters. Leakage of privacy data can cause serious damage to data providers. Furthermore, user anonymity and data verification in FL also need to be considered. To tackle these problems, in this paper, a verifiable and privacy-preserving cooperative FL (VPPFL) scheme is proposed in UAV-assisted vehicular networks (UVNs). Specifically, to preserve the identity privacy of vehicles, elliptic curve cryptosystem (ECC) is used to generate pseudonyms for vehicles. To preserve the data privacy, Paillier homomorphic encryption algorithm is utilized to encrypt the updates of vehicles, whereby UAVs directly perform global aggregations on encrypted updates instead of raw ones. Additionally, pseudonym-based signature mechanism is presented for vehicles to generate verifiable signatures, so as to ensure the authenticity and validity of uploaded local model updates. Besides, to sufficiently use the multi-source data, multiple UAVs share the local updates packets with each other to execute global aggregation. Finally, simulations are carried out to demonstrate that the proposed scheme can achieve high accuracy and verification with providing strict privacy protection.
Post-Quantum Forward-Secure Signatures with Hardware-Support for Internet of Things

Digital signatures provide scalable authentication with non-repudiation and therefore are vital tools for the Internet of Things (IoT). IoT applications harbor vast quantities of low-end devices that are expected to operate for long periods with a risk of compromise. Hence, IoT needs post-quantum cryptography (PQC) that respects the resource limitations of low-end devices while offering compromise resiliency (e.g., forward-security). However, as seen in NIST PQC efforts, quantum-safe signatures are extremely costly for low-end IoT. These costs become prohibitive when forward security is considered. We propose a highly lightweight post-quantum digital signature called HArdware-Supported Efficient Signature (HASES) that meets the stringent requirements of resource-limited signers (processor, memory, bandwidth) with forward security. HASES transforms a key-evolving one-time hash-based signature into a polynomially unbounded one by introducing a public key oracle via secure enclaves. The signer is non-interactive and only generates a few hashes per signature. Unlike existing hardware-supported alternatives, HASES does not require a secure-hardware on the signer, which is infeasible for low-end IoT. HASES also does not assume non-colluding servers that permit scalable verification. We proved that HASES is secure and implemented it on the commodity hardware and the 8-bit AVR ATmega2560 microcontroller. Our experiments confirm that HASES is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$271\times\ \mathbf{and}\ 34\times$</tex> faster than (forward-secure) XMSS and (plain) Dilithium. HASES is more than twice and magnitude more energy-efficient than (forward-secure) ANT and (plain) BLISS, respectively, on an 8-bit device. We open-source HASES for public testing and adaptation.
A Framework for Reactive Jamming Detection via Causal Inference

In this work, we propose a novel framework for reactive jammer detection based on spectrum patrolling, blind source separation (BSS), and causal inference. The methodology is based on a spectrum patrol that performs jamming detection without demodulating the received signals; therefore, it can manage scenarios with legitimate users belonging to different networks and adopting completely different network protocols. Furthermore, because of the wireless medium, over-the-air signals captured by the patrols are mixed; consequently, BSS is used to separate traffic patterns. The proposed causal inference-based approach (namely all-versus-one transfer entropy (AvOTE)) reaches a remarkably high probability of detection (i.e., 0.95) with low false alarm probability (i.e., 0.05) even in high shadowing regimes. Moreover, to fully understand the impact of different parameters on the performance, we study the impact of the signal-to-jammer ratio (SJR) and the jammer attack strategy on the detection performance in different shadowing regimes.