Miracle-dz/UniFAR-FTUs huggingface dataset:
Visible Forensic Investigation for Android Applications by Using Attack Scenario Reconstruction

With the widespread use of Android devices, research on their security has attracted increasing attention. However, at present, digital forensics for investigating attacks, such as social engineering attacks and phishing that target Android users, remains a challenging and time-consuming task. To help discover the existence of an attack and conduct effective investigations, we propose a top-down digital forensic tool for Android applications to reconstruct attack scenarios by considering both high-level user interface (UI) elements and low-level system events. Thus, we can explain the nature of an attack from a visual and global perspective. The tested evaluation results show that our tool can successfully reconstruct scenarios on Android devices for phishing attacks.
All Predict Wisest Decides: A Novel Ensemble Method to Detect Intrusive Traffic in IoT Networks

Internet of things (IoT) networks confront vari-ous network intrusion threats due to massively interconnected nodes that form an extensive attack surface for adversaries. Machine learning (ML)-based approaches are widely investigated to address network intrusions. It becomes further challenging to achieve promising performance for multi-class classification so to identify each attack type rather than detection of the presence of intrusion, which involves binary classification. ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, we propose an innovative ensemble learning framework, namely All Predict Wisest Decides (APWD) that builds on training of multiple ML models and testing them independently so to obtain prediction performance for all classes. For each attack category, an expert (i.e., wisest) model that performs the best F1 score, accuracy, lowest false detection rate is determined according to individual model results. The aggregation module makes decisions relying upon the wisest model determined for each class. APWD is a generic framework, and the types of MLs and the number of MLs can be customized in APWD. Experiments under a popular public dataset, NSL-KDD verify the proposed approach APWD by demonstrating that APWD boosts overall accuracy to 0.797, comparing 0.772 by XGBoost, 0.758 by RF, and 0.584 by Adaboost. Moreover, in certain attack types R2L, APWD increases F1 score by a factor of 18, from 0.022 by RF to 0.421.
Analysis and Compensation of Spatial Correlation in Data Transmission Using RIS

Reconfigurable intelligent surfaces (RIS) are currently drawing a lot of attention in the research community as a key technology for future wireless networks. In addition to boosting the signal-to-noise ratio, they can also be used to transmit data in the same way spatial modulation (SM) transmits data by mapping it to the activated antenna indices in MIMO systems. The problem of this transmission technique which we refer to as RIS-SM is that spatial correlation between elements of the RIS array strongly degrades bit error rate performance. In this paper, we analyze this degradation and introduce two techniques to compensate for spatial correlation. The first employs tile-specific phase shifts in the RIS elements and the second employs dynamic phase shifts that are specific to the RIS patterns activated by the information bits to be transmitted. The analysis and the simulation results show that the proposed techniques provide substantial performance improvements and make RIS-SM transmission reliable even in the presence of very strong spatial correlation.
Risk-sensitive REINFORCE: A Monte Carlo Policy Gradient Algorithm for Exponential Performance Criteria

Risk is an inherent component of any decision making process under uncertain conditions, and failure to consider risk may lead to significant performance degradation. We present a policy gradient theorem for the Risk-sensitive Control "exponential of integral" criteria, and propose a risk-sensitive Monte Carlo policy gradient algorithm. Our simulations, together with our theoretical analysis, show that the use of the exponential criteria with an appropriately chosen risk parameter not only results in a risk-sensitive policy, but also reduces variance during learning process and accelerates learning, which in turn results in a policy with higher expected return— that is to say, risk-sensitiveness leads to sample efficiency and improved performance.
Kullback-Leibler-Quadratic Optimal Control in a Stochastic Environment

This paper presents advances in Kullback-Leibler-Quadratic (KLQ) optimal control: a stochastic control frame-work for Markovian models. The motivation is distributed control of large networked systems. The objective function is composed of a control cost in the form of Kullback-Leibler divergence plus a quadratic cost on the sequence of marginal distributions. With this choice of objective function, the optimal probability distribution of a population of agents over a finite time horizon is shown to be an exponential tilting of the nominal probability distribution. The same is true for the controlled transition matrices that induce the optimal probability distribution.However, one limitation of the previous work is that randomness can only be introduced via the control policy; all uncontrolled processes must be modeled as deterministic to render them immutable under an exponential tilting. In this work, only the controlled dynamics are subject to tilting, allowing for more general probabilistic models.Numerical experiments are conducted in the context of power networks. The distributed control techniques described in this paper can transform a large collection of flexible loads into a ‘virtual battery’ capable of delivering the same grid services as traditional batteries. Additionally, quality of service to the load owner is guaranteed, privacy is preserved, and computation and communication requirements are reduced, relative to alternative centralized control techniques.
Dual-Masking Framework against Two-Sided Model Attacks in Federated Learning

With the popularity of AIoT (Artificial Intelligence of Things) services, we can foresee that smart end devices will generate tremendous user data at the edge. In particular, it is critical to address how to properly distill knowledge from the edge network in a communication-efficient and privacy-preserving manner. Federated learning (FL), one of the promising machine learning frameworks, ensures data privacy by allowing end devices to collaboratively train a shared model without exposing raw data to an aggregation server. However, due to its distributed nature, the framework is vulnerable to two major threats: the Model Inversion Attacks and the Model Poisoning Attacks. An abnormal aggregator or malicious end devices may probably launch these attacks in the training phase. The former leaks sensitive information by reversing the model weights to users' raw data. Still, the latter can break the model security and mislead the global model to wrong inference results. Unfortunately, the existing research has not tackled such two-sided model attacks that occurred concurrently in FL. Therefore, in this paper, we propose a dual-masking federated learning (DMFL) framework that advocates partial weights uploading in the aggregation process and applies two kinds of masks on both the end device and the aggregator sides. Based on the benchmark data for image classification, our experimental results show that the proposed DMFL framework outperforms other baselines, confirming that it can successfully preserve weights privacy and protect model security for AIoT.
Privacy-Preserving Friend Matching for Mobile Social Networks

In this paper, we propose an efficient private set intersection protocol, named LL-PSI, to enable two parties (where each party has an individual set) to obtain the intersection of their sets without leaking other information about their sets to each other. Compared with existing protocols, LL-PSI reduces the computational latency of the intersection between two sets significantly at the expense of communication costs between the parties. Based on LL-PSI, we propose a privacy-preserving friend matching scheme for mobile social networks, dubbed PAIRING. PAIRING allows users to match with those who have common interests while preserving users' private information against the semi-honest server and curious users. We analyze the security of PAIRING and conduct a comprehensive performance evaluation, which demonstrates that PAIRING is secure and efficient.
Fault Root Cause Diagnosis Method Based on Recurrent Neural Network and Granger Causality

Data-driven fault diagnosis methods are more and more important in modern industry. At the same time, the research on the cause of fault location when they occur is more and more advanced. The Granger Causality (GC) test method is difficult to analyze nonlinear signals and redundant causality when locating fault root, which limited the application of this method. To solve this problem, a fault root diagnosis method based on Recurrent Neural Network (RNN) and GC is proposed. Firstly, the Principal Component Analysis (PCA) model is established for fault detection, and the contribution plot method is used to select the variables that may fault when the fault occurred. Then, Dynamic Time Warping (DTW) is used to group fault candidate variables to reduce redundant causality. Finally, RNN is integrated with GC technology, which enabled it to deal with nonlinear signals and locate the root cause of faults. The method is applied to the simulation experiment of TE chemical process, and the experimental results show that the method can locate the fault root and identify the fault propagation path accurately.
QoS-Aware Mapping and Scheduling for Virtual Network Functions in Industrial 5G-TSN Network

Driven by the advantages of the ubiquitous connection of 5G and the determinacy of Time-Sensitive Networking (TSN), the integration of 5G and TSN is expected to provide flexible and deterministic communications for the industry. However, due to the diverse quality of service (QoS) demands of industrial applications, it is challenging to provide suitable QoS mapping across industrial 5G-TSN networks and offer dynamic services via heterogeneous infrastructures. To address this issue, we design a QoS-aware dynamic data injection scheme to realize the interconnection between 5G and TSN under edge-assisted 5G-TSN architecture. To break the tight coupling between applications and infrastructures, we focus on the virtual network function (VNF) mapping problems to facilitate the QoS provisioning for different applications leveraging the network function virtualization (NFV) technique. We first formulate it as a mixed integer linear programming (MILP) with time-sensitive constraints. Then we develop PVMS, a preemption-based two-stage heuristic algorithm for VNF mapping and scheduling in the 5G-TSN network. In particular, we dynamically map the VNFs of arrived applications by greedily searching the earliest available 5G-TSN resources. To further meet the low-latency requirements, we employ a preemption mechanism to provide no-wait transmission for higher priority applications at the cost of the preempted applications being postponed. Simulation results demonstrate that the proposed PVMS has better performance in terms of the acceptance ratio, average delay, and QoS guarantee.
Wideband Millimeter-Wave Massive MIMO Channel Training via Compressed Sensing

In this work, a compressed sensing-aided wideband MIMO-OFDM channel training framework is proposed to reduce the training overhead in slowly-varying channels with frequency- and spatial-wideband (dual-wideband) effects. To combat the beam squint effect, a set of frequency-dependent array response matrices are constructed, enabling the recovery of the sparse beamspace channel from multiple observations across OFDM subcarriers, via multiple measurement vectors (MMV). A channel training algorithm (MMV-LS-CS) is proposed to estimate slowly-varying multipath channel parameters: MMV least squares (MMV-LS) is first used to estimate the channel on the previous beam index support, followed by MMV compressed sensing (MMV-CS) on the residual to estimate the time-varying multipath components. Finally, a channel refining algorithm is proposed to estimate the gains and time delays of the dominant channel paths jointly on pilot subcarriers. Numerical results show that MMV-LS-CS achieves more accurate and robust channel estimation than the state-of-the-art approach on slowly-varying dual-wideband MIMO-OFDM: given a moderate SNR of 20 dB, our algorithm attains $\text{NMSE}=0.15$, as opposed to the state-of-the-art which attains $\text{NMSE}=0.43$ in the same configuration. Besides, MMV-LS-CS necessitates $\text{SNR} =14\ \text{dB}$ to achieve the spectral efficiency of 6 bit/s/Hz/stream, while the state-of-the-art scheme needs $\text{SNR}=17\ \text{dB}$ to attain the same spectral efficiency.
Blind Federated Learning with Low-Cost Analog-to-Digital Converters

We study federated learning over wireless channels where a massive dataset is distributed across independent workers which compute their local gradients based on their own datasets. Workers send their gradients through a multipath fading multiple access channel with orthogonal frequency division multiplexing to mitigate the frequency selectivity of the channel. We assume that there is no channel state information (CSI) at the workers, and the parameter server (PS) employs multiple antennas to align the received signals. To reduce the power consumption and hardware costs, we employ complex-valued low-resolution analog-to-digital converters (ADCs) at the receiver side, and study the effects of practical low-cost ADCs on the learning performance. Our results show that the impairments caused by low-resolution ADCs, including those of one-bit ADCs, do not prevent the convergence of the federated learning algorithm, and the multipath channel effects vanish when a sufficient number of antennas are used at the PS.
Full Duplex Relay Assisted Coded Cooperation for Next Generation Wireless Networks

Wireless networks based on Network Coded Co-operation (NCC) have shown promising results in achieving faster and reliable communication. To make the next-generation wireless systems swifter, the nodes can be equipped to work in Full-Duplex (FD) mode. In this paper, we thus model a multi source-destination NCC scenario with a relay working in FD mode (FD-NCC) and propose a decoding scheme for it. FD schemes have the benefit of an increased achievable rate. Extensive simulations carried out in MATLAB show an improved performance for FD in terms of achievable rate when compared to a Half-Duplex NCC (HD-NCC) scenario. NCC systems aided by a FD relay, also provides higher throughput thus, making it suitable for applications requiring reliable low latency communication (RLLC) in 5G/6G scenarios.
Set-Valued Model Predictive Control

Model predictive control (MPC) is a valuable tool to deal with systems that require optimal solutions and constraint satisfaction. In the case of systems with uncertainty, the formulation of predictive controllers requires models which are capable to capture system dynamics, constraints and also system uncertainty. In this work we present a formulation for a setvalued model predictive control (SVMPC) where uncertainty is represented in terms of sets. The approach presented here considers a model where the state is set-valued and dynamics are defined by a set-valued map. The cost function associated to the proposed MPC associates a real-valued cost to each set valued (or tube-based) trajectory. For this formulation, we study conditions that can yield the constrained optimal control problem associated to the set-valued MPC formulation feasible and stable, thus extending existing stability results from classic MPC to a set-based approach. Examples illustrate the results along the paper.
Towards Safer Retinal Surgery through Chance Constraint Optimization and Real-Time Geometry Estimation

Safely navigating a surgical tool to a desired location on the surface of the retina during retinal surgery relies on extreme precision and surgical skills. Damage to the delicate retinal tissue often occurs. Previous work demonstrated an approach for robot-assisted navigation in eye surgery using imitation learning and optimal control. To further enhance safety, we present a framework that combines real-time eye geometry estimation and chance-constrained optimal control to bound the probability for tissue damage during autonomous robotic navigation. A neural network is trained to predict the relative location of 3-D points on the retina with respect to the current tool-tip position through expert demonstrations. During inference, a local geometry of the retina is estimated using weighted least squares formulation based on these learned predictions and then employed as a probabilistic collision chance constraint in an optimal control framework. The proposed approach is experimentally validated using a phantom silicone eye suitable for vein cannulation testing. We statistically demonstrate that the network predictions become more accurate as the surgical tool approaches closer to the retinal surface and that the measured mean errors along the lateral directions for navigating to a given point or for a vessel-following task are below 0.1 mm. These results indicate that the proposed technique could serve as a basis to further develop robot-assisted retinal microsurgery with enhanced safety.
On Meeting a Maximum Delay Constraint

The recent applications in communications are required to meet low-latency transmission with high traffic rates and reliabilities. From the latency point of view, most of the state-of-the-art techniques consider the average latency which does not directly apply to delay-sensitive scenarios. In this paper, we propose a novel approach to tackle the scheduling problem by directly addressing the max-delay constraint; this is an NP-hard problem. Our main contributions are first, proposing the Super State Monte-Carlo Tree Search (SS-MCTS) as a version of regular MCTS modified for large-scale probabilistic environments with less computational complexity, and second, addressing the scheduling problem with maximum delay constraint on flows. Our numerical results demonstrate that the proposed approach significantly improves the packet delivery rate while meeting the maximum delay constraint in large-scale scenarios compared to the state-of-the-art technologies.
Distributed Multi-Armed Bandit over Arbitrary Undirected Graphs

This paper studies a distributed multi-armed bandit problem in a network of multiple agents, each of which can communicate only with its neighbors, where neighbor relationships are described by an undirected graph. Each agent makes a sequence of decisions on selecting an arm from a given set of candidates, yet it only has access to local samples of the reward for each action, which is an unknown random variable. All the agents share the same distribution of each arm’s reward. A distributed upper confidence bound (UCB) algorithm is proposed for the agents to cooperatively learn the best arm, which does not require any global information. It is shown that the algorithm achieves a logarithmic regret for each of the agents, even though the graph is disconnected. The derived regret implies that the proposed distributed UCB algorithm enables a faster learning for any agent in the network compared with the classical single-agent UCB algorithm, as long as the agent has at least one neighbor.
On a Non-detectable Riccati Differential Equations Arising from a Filtering Problems of Markov Jump Linear Systems

In this paper we make a further foray on Riccati equation. Besides the interest in its own right, the Riccati differential equation (RDE) studied here has an important bearing on an optimal linear filtering problem for Markov jump linear systems with partial observations of the Markov chain. A peculiar feature of this class of RDE is that controllability holds, but not detectability. Via some diagonal dominant matrices properties in conjunction with Gershgorin’s circle theorem, it is shown that this particular class of non-detectable RDE has a unique positive semidefinite solution that converges to a solution of the associated algebraic Riccati equation (ARE). Although we first consider the case with time-invariant weight of the quadratic term, it is shown that under certain conditions, extension for the time-variant case is also possible . These results has an immediate impact on the deduction of the corresponding stationary filters, and may be relevant for the optimal linear control problem. Finally, we also carry out several numerical simulations, providing a visual endorsement to the theoretical results obtained in this work.
Entropy-Driven Stochastic Policy for Fast Federated Learning in Beyond 5G Edge-RAN

Scalability and sustainability are the corner stones to unleash the potential of beyond fifth-generation (B5G) ultra-dense networks that are expected to handle massive and heterogeneous services. This implies that the transport of the underlying raw monitoring data should be minimized across the network, and urges to bring the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying users distributions, traffic profiles, and channel conditions. This makes the data collected across different points non independent and identically distributed (non-IID), which is challenging for FL tasks. To cope with this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This entropy is calculated using a clustering scheme based on a similarity matrix defined over both the features and the supervised output spaces, and is targeting classification as well as regression tasks. The FL aggregation server then uses the reported dataset entropies to devise i) an entropy-based federated averaging scheme, and ii) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to illustrate all these advantages.
Advances in System Identification: Theory and Applications

This paper describes the main features and the most recent developments of system identification in the sense of data-driven modeling of dynamical systems. A brief summary of discrete time-invariant system identification techniques is provided, from the modern work of Gilbert, Kalman and Ho and the introduction of state-space realization, to the most recent developments of the identification of discrete time-varying and nonlinear systems. Important concepts of state-space realization, controllability and observability for linear systems are introduced along with more advanced methods to identify nonlinear dynamics. Numerical examples of varying complexity are considered to demonstrate the capability of the different approaches presented in this paper.