Reviewerly/exHarmony huggingface dataset:
The Capability-Security Trade-Off of Blockchain for Data Sharing at the Network Edge

Blokchain is a promising technology to enable distributed and reliable data sharing at the network edge. The high security in blockchain is undoubtedly a critical factor for the network to handle important data item. On the other hand, according to the trilemma in blockchain, an overemphasis on distributed security will lead to poor transaction-processing capability, which limits the application of blockchain in data sharing scenarios with high-throughput and low-latency requirements. To enable demand-oriented distributed services, this paper investigates the relationship between capability and security in blockchain from the perspective of block propagation and forking problem. First, a Markov chain is introduced to analyze the gossiping-based block propagation among edge servers, which aims to derive block propagation delay and forking probability. Then, we study the impact of forking on blockchain capability and security metrics, in terms of transaction throughput, confirmation delay, fault tolerance, and the probability of malicious modification. The analytical results show that with the adjustment of block generation rate, transaction throughput improves at the sacrifice of fault tolerance, and vice versa. Meanwhile, the decline in security can be offset by adjusting confirmation threshold, at the cost of increasing confirmation delay.
Learning Variable-Rate Codes for CSI Feedback

We observe that current Deep Learning (DL)-based Channel State Information (CSI) encoder and decoder architectures achieve a distortion which is highly channel-dependent. To exploit this, we propose a novel learning-based variable-rate coding scheme to reduce overheads associated with CSI feedback. To that end, we propose an architecture which combines (a) training an efficient predictor for the distortion rate tradeoffs achievable for a given channel, and (b) optimization of a decision logic which allocates rates based on the predicted distortion. We evaluate our approach on various wireless channel datasets including the 3GPP 3D channel model and COST2100 with Massive MIMO channel model, and show significant potential reductions of up to 20% in the CSI feedback overhead.
Misbehavior Detection in Vehicular Networks: An Ensemble Learning Approach

Emerging vehicle-to-everything (V2X) systems call for a diverse set of novel mechanisms to address vulnerabilities and security breaches. In this context, misbehavior detection approaches aim to detect malicious behavior of rogue V2X entities and possible attacks that may originate from them. In this paper, we introduce a data-driven ensemble framework which jointly leverages clustering and reinforcement learning to detect misbehaviors in unlabeled vehicular data. A rigorous detection assessment using an open-source dataset reveals meaningful performance trends for various attacks. In particular, while the majority of attacks can be effectively detected, detection may be curtailed for certain misbehavior types due to partly inaccurate clustering and erratic activity of the attacker over time. Performance comparison against benchmark detectors reveals the robustness of our approach in the presence of potentially inconsistent or mislabeled training data. The real-time detection capabilities of our framework are also explored in an effort to evaluate its practical feasibility in mission-critical V2X scenarios.
Frame Synchronization for Quantum Key Distribution Systems

In this work, a reliable and low computational õ complexity frame synchronization method for quantum key distribution (QKD) systems is proposed. The proposed method is based on the computation of the quantum bit error rate (QBER) and requires no more hardware than the one used for qubit coding and decoding. In a initial stage, pre-shared and received sequences are shifted until a minimum QBER value is reached. In a second stage, to confirm that the frames are correctly positioned, and to ensure that during key transmission a correct association of each received bit is made with the corresponding bit sent by the transmitter, a pre-determined number of inverted frames is sent, rising the QBER value to maximum values. As the number of inverted frames is a pre-agreed number, after detecting the first inverted frame it is possible to determine exactly when the secret key transmission will start. We use a polarization-encoding QKD testbed to assess the overall performance of the method. With the proposed frame synchronization method, the synchronization is achieved after roughly 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> clock cycles, despite the concerns imposed by the low probability detection inherent to the QKD systems.
A novel aggregation method to promote safety security for poisoning attacks in Federated Learning

Federated Learning enables devices to collaboratively learn a shared prediction model while keeping all the training data on the local device and promoting clients' privacy. Vanilla federated learning models are susceptible to model poisoning attacks, where malicious nodes can inject fake models weight in order to deviate the global model to its objective. With this attack, malicious nodes intend to generate inaccurate global models or even to generate global models that make wrong inferences. This work proposes a new similarity function for federated learning applications to tackle the model poisoning vulnerability. Our method uses a new security aggregation proposal for local models based on the quantification of the heterogeneity of the data. In addition, this quantifier benefits from theoretical results found in models that use the auxiliary space proposed in our model. To assess the general behavior of our method, we evaluate our proposal in a federated scenario under non-iid data where all local models are honest, where we outperformed the vanilla model by 52.84% and 58.88% on two real-world dataset, respectively. In addition, our proposal reached 81.79% and 73.92% of F1-Score in a model poisoning experiment and outperformed the other state-of-the-art methods by 8.60% and 5.54% respectively.
Communication-Efficient Federated Learning in Channel Constrained Internet of Things

Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As the most widely-used FL framework,Jederated averaging (FedAvg) suffers an expensive communication cost especially when there are large amounts of devices involving the FL process. Moreover, when considering asynchronous FL, the slowest device becomes the bottleneck for the cask effect and determines the overall latency. In this work, we propose a communication-efficient federated learning framework with partial model aggregation (CE-FedPA) algorithm to utilize compression strategy and weighted device selection, which can significantly reduce the size of uploaded data and decrease the communication time. We perform a series of experiments on the MNIST/CIFAR-10 datasets, in both lID and non-lID data settings. We compare the communication time of different aggregation schemes, in terms of iteration rounds and target accuracy. Simulation results demonstrate that the uploading time of the proposed scheme is up to 4.3 times shorter than other existing ones. Experiments on an end - to-end FL framework also verify the communication efficiency of CE-FedPA in a real-world setting.
MMOS: Multi-Staged Mutation Operator Scheduling for Deep Learning Library Testing

The rapid development of deep learning (DL) technology has made deep learning libraries such as Tensor Flow widely used in practice. However, the complexity of DL libraries inevitably leads to multiple vulnerabilities. Recently, research on DL library testing generally designs a variety of mutation operators to generate new models from the perspective of model mutation. However, we find these research efforts do not consider that different mutation operators have different vulnerability mining efficiencies, thus treating all mutation operators equally can lead to inefficiency. Based on the above observation, we design a novel mutation operator scheduling strategy to improve the efficiency of vulnerability mining in the DL library, including the multi-staged mutation operator selection strategy and mutation operator energy allocation strategy. To evaluate the efficiency of our work, we implement a prototype called MMOS. The results show that MMOS finds six more crash bugs, two more NaN bugs and one more inconsistency bug in four widely used DL libraries including TensorFlow, Theano, CNTK and MXNet compared with the random strategy. Among them, MMOS finds 5 previously unknown vulnerabilities in MXNet and 1 in CNTK. Moreover, MMOS outperforms LEMON in vulnerability mining of DL libraries. In total, MMOS finds 13 more vulnerabilities than LEMON.
Private Protocol Traffic Identification Based on Sequence Statistical Fingerprint

With more attention paid to communication security and user privacy, there has been a dramatic rise in private protocols, which brings great challenges to traditional protocol identification. The increasingly complex design of private pro-tocols has led to the loss of effectiveness of features used for traditional identification. Besides, some features are redundant and also lose sight of the particularity of private protocols, which could not be guaranteed to be the optimal identification solution. In this paper, we propose a method to obtain the Sequence Sta-tistical Fingerprint(Seq-SF) which is proprietary for each private protocol and can be used for identifying private protocols from promiscuous traffic. We extend statistical features associated with protocol transmission to make the sequence feature be integrated into it. Then, we adopt a feature selection algorithm based on mutual information ranking, which can evaluate the pertinence between features and protocols to guarantee the effectiveness and optimality of Seq-SF. The results of experiments on the real-world dataset covering 13 private protocols indicate that Seq-SF achieves an ideal performance(99.41% accuracy, 97.56% precision, 95.18% recall, and 0.20% FPR on average) and has good robustness.
A Privacy-preserving Data Transmission Protocol with Constant Interactions in E-health

In recent years, to improve the quality of medical services in e-health systems, various types of sensors supporting collection and online/offline consultation have appeared in life; effectively facilitating doctors' disease prediction and consultation. However, data in e-health systems come from a wide range of sources and are mostly related to patient privacy. Therefore, how to ensure patient privacy and data confidentiality in data transmission is considered serious issues. In addition, the storage volume of cloud servers continues to grow, and how to guarantee that servers can quickly respond to requests has become a pressing problem. To this end, a privacy-preserving data trans-mission protocol is proposed, which only needs constant times interactions to complete the batching requests. In particular, a lightweight OTnk protocol is designed, employing the idea of matrix transformation, which effectively reduces the number of interactions while protecting the privacy of both communicating parties. The security and performance analysis indicate that the proposed protocol can be instantiated in e-health with high security and efficiency.
A New Implementation of Federated Learning for Privacy and Security Enhancement

Motivated by the ever-increasing concerns on per-sonal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parame-ter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning

Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from similar tasks and thus has also been intensively studied. However, due to the limited radio spectrum, the communication efficiency of federated learning via wireless links is critical since some tasks may require thousands of Terabytes of uplink payload. In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches. We first introduce the system design in which the extracted features and outputs are uploaded instead of parameter updates, and then determine the required payload with this approach and provide comparisons with the existing approaches. Subsequently, we analyze the random shuffling scheme that preserves the clients' privacy. Finally, we evaluate the performance of the proposed learning scheme via experiments on an image classification task to show its effectiveness.
How Tezos blockchain can meet IoT?

Nowadays, blockchain, revolutionary technology with high security and decentralization is widely applied in many industry segments including healthcare, cryptocurrency and so on. Despite its benefit, it is a challenge for blockchain to effectively operate in large-scale networks without compromising security and decentralization properties due to low throughput. Therefore, in this paper, we consider sharding-based blockchain as a solution to overcome this limitation. The objective is to split the network into different shards to process transactions in parallel and reduce quorum size to reach consensus quickly. However, existing sharding-based blockchain networks implement a consensus algorithm for both main and sub-shards, so it is ineffective with different purposes (e.g., to improve security, reduce resource consumption, etc.). Hence, we propose a system with heterogeneous consensus algorithms in sharding-based blockchain to meet different requirements. Experimental results show that the proposed approach improves up to 54 percent of throughout in comparison with benchmarks.
Combining Blind Equalization and Automatic Modulation Classification in a Loop Structure

The process of demodulating an unknown wireless communication signal without knowledge of the channel state and modulation type is called the dual-blind demodulation problem. We focus on solving the dual-blind demodulation problem in the presence of multipath fading and noise. Traditional blind receivers have different functional blocks to preprocess the incoming signal and recognize the modulation type. However, the independence among these blocks may limit performance. In this paper, we propose a dual-mode equalizer combined with a modulation classifier with a loop evaluation structure, called the automatic modulation classification (AMC) loop. We also introduce an AMC algorithm with a novel cluster-to-constellation (c2c) distance. Via experiments, we show that the proposed loop structure achieves better demodulation and classification accu-racy in a dual-blind scenario than (i) the traditional approach without the loop structure, and (ii) loop structures with alternate AMC algorithms. We also show that the proposed novel AMC-Loop structure with an AMC based on c2c distance is crucial for good performance via an ablation study.
Model-based Federated Reinforcement Distillation

Reinforcement learning (RL) is a framework for learning highly rewarding policies through interactions with the environment. The more the agent knows about the environment, the more easily it learns. Therefore, exploration is often performed using multiple agents. However, information gathered by edge devices is not always available to all devices, including the server. Federated learning (FL) is a framework for collaborative learning while preserving the privacy of the training data. Most of the FL methods exchange the trained model parameters instead of the private data. In contrast, some FL methods incorporate knowledge distillation. These methods are known to be superior to parameter exchange methods in terms of communication efficiency. In this study, we apply the idea of distillation-based FL to RL problems. We highlight some difficulties unique to RL and give a solution to them by introducing an environment model. We call this newly proposed method model-based federated reinforcement distillation (Model-based FRD). As with existing distillation-based FL methods, our proposed method achieves high communication efficiency. Our experimental results show that our method reduces communication costs by approximately 821 times.
FLaaS6G: Federated Learning as a Service in 6G Using Distributed Data Management Architecture

AI/ML is envisioned to play an essential role in 6G mobile communication systems. The privacy-preserving capabil-ities of Federated Learning (FL) make it promising in vertical applications; however, the central server-based system and lack of trusted data management limit its widespread use. To effectively support FL as a service from a network architecture perspective, this work provides a comprehensive design including three key features: First, the network architecture enables transparent and traceable data management based on Distributed Ledger Technology (DLT) platform, and realizes distributed and off-chain data storage by adopting Distributed Data Storage Entity (DDSE). Second, the central aggregator of an FL service is decoupled from the data management scheme mentioned above, and is decentralized through smart contracts for aggregator selection among a set of aggregator candidates, with the selected aggregator subsequently responsible for client selection and model aggregation. Third, a completed set of procedures for FL services operations is defined. A simulation system is developed to verify the feasibility of the proposed architecture and to study the impact of introducing the data management mechanisms on the overall performance overhead. The results show that the impact is related to the FL settings, with a worst-case time overhead of 15% observed in selected test cases, i.e., 15% of the total time spent on the interactions with the DLT platform and DDSE.
Shapley Explainer - An Interpretation Method for GNNs Used in SDN

Graph neural networks (GNNs) have been widely applied in software-defined network (SDN) for better network modeling and performance prediction. However, the black-box characteristic of deep learning makes the GNNs hard to interpret, such interpretability issue hinders the wide use of GNNs. In this paper, we propose Shapley Explainer, that provides fair importance scores to the input nodes of a GNN within an appropriate computation cost, thereby providing a valid and reasonable interpretation of graph neural network on software defined network. The proposed method derives the importance ranking of topological nodes by combining shapley values with a soft discrete mask matrix. We apply Shapley Explainer to RouteNet model, a GNN model that provides intelligent predictions of SDN network performance metrics. The experimental results show that Shapley Explainer can provide effective interpretations for RouteNet. It also verifies that the RouteNet model can correctly learn the relationship between features, which can provide a better understanding of the prediction process of RouteNet, promoting the application of GNN-based SDN systems in engineering practice.
Non-cooperative Learning for Robust Spectrum Sharing in Connected Vehicles with Malicious Agents

Multi-agent reinforcement learning (MARL) has pre-viously been employed for efficient spectrum sharing among co-operative connected vehicles. However, we show in this paper that existing MARL models are not robust against non-cooperative or malicious agents (vehicles) whose spectrum selection strategy may cause congestion and reduce the spectrum utilization. For example, a selfish (non-cooperative) agent aims to only maximize its own spectrum utilization, irrespective of the overall system efficiency and spectrum availability to others. We investigate and analyze the MARL-based spectrum sharing problem in connected vehicles including vehicles (agents) with selfish or sabotage strategies. We then develop a theoretical framework to consider the selfish agent, and study various adversarial scenarios (including attacks with disruptive goals) via simulations. Our robust MARL approach where “robust” agents are trained to be prepared for selfish agents in testing phase achieves more resiliency in the presence of a selfish agent and even a sabotage one; achieving 6.7%~20% and 50.7% ~ 138% higher unicast throughput and broadcast delivery success rate over regular benign agents, respectively.
Energy Level Spoofing Attacks and Countermeasures in Blockchain-enabled IoT

The Internet of Things (IoT) ecosystem is witnessing widespread deployments for emerging applications in diverse domains such as remote sensing, smart homes, and industry 4.0. There is also a growing need to secure such deployments against malicious IoT devices to sustain normal network operations. Since the IoT deployments encompass geographically distributed nodes, blockchain technology, which inherently offers distributed trust in such scenarios, is gaining popularity in providing a secure and trusted IoT deployment. In this paper, we present a use case in which an IoT deployment is retrofitted with a blockchain. The use of blockchain prevents malicious nodes from falsifying information about their energy levels. We first present attack scenarios where IoT nodes can spoof energy while joining or being a part of the network. We then build a defense strategy and evaluate its performance under various attack scenarios. Our results indicate that the IoT deployment is robust under the proposed defense strategy which can detect if a node is spoofing its energy levels over 75% of the time.