Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Window Based Group-Behavior Supported Method for Accurate Detection of Anomalous Users

Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns. Most existing approaches typically build models by reconstructing single-day and individual-user behaviors. However, without capturing long-term signals and group-correlation signals, the models cannot identify low-signal yet long-lasting threats, and will wrongly report many normal users as anomalies on busy days, which, in turn, lead to high false positive rate. In this paper, we propose ACOBE, an Anomaly detection method based on COmpound BEhavior, which takes into consideration long-term patterns and group behaviors. ACOBE leverages a novel behavior representation and an ensemble of deep autoencoders and produces an ordered investigation list. Our evaluation shows that ACOBE outperforms prior work by a large margin in terms of precision and recall, and our case study demonstrates that ACOBE is applicable in practice for cyberattack detection.
A Novel 0–5 KPA Piezoresistive Pressure Sensor Based on Peninsula Structure Diaphragm

This paper first reports a new peninsula structure diaphragm (NPSD) piezoresistive pressure sensor, which has high sensitivity and linearity at the same time. By creating stress concentration areas and increasing the stiffness of the film, within the pressure range of 0–5 kPa, the sensitivity of the sensor reaches 22.7 mV/kPa while the nonlinearity is reduced to 0.11% FSO (full-scale output). Compared with our previous work, the sensitivity is increased by 23.4% while the nonlinearity is reduced by 69%. When the ambient temperature rises to 60°C, the sensor still has a high sensitivity of 21.5 mV/kPa.
Fog Node Placement in IoT Scenarios with Stringent QoS Requirements: Experimental Evaluation

Leveraging the Internet of Things (IoT) in intensive domains, such as in the Industrial Internet of Things (IIoT) or Internet of Medical Things (IoMT), provides automation and sensing solutions for complex environments through the interconnection of different sensors and actuators. However, these scenarios usually demand to meet stringent Quality of Service (QoS) requirements to work properly. Fog computing, a paradigm that brings computation and storage closer to the edge, and Software-Defined Networking (SDN), a networking paradigm that enables for network scalability and flexibility, can be combined. To do so, fog nodes that integrate both, computation resources and SDN capabilities, are leveraged to meet these stringent needs. Clearly, the placement of such fog nodes plays a key role in the achieved QoS. In this paper, an optimal fog node placement formulation is evaluated in an emulated fog and SDN environment. Results show that an optimal fog node placement can achieve a reduction of up to 59% in the network latency with a minimal jitter compared with other well-known placement methods.
4-Meth­oxy­benzyl­ammonium nitrate

In the title salt, C8H12NO+·NO3−, the 4-meth­oxy­benzyl­ammonium cation lies in the mirror plane m of space group Pnma and is thus planar by symmetry. The nitrate anion is also planar by symmetry, with an N O group in the mirror plane and one O atom in a general position. The dihedral angle between the benzene ring and the planar nitrate anion is constrained to be exactly 90°, because of the relative special positions for both ions. In the crystal, the cations are connected to the anions by C—H⋯O, C—H⋯N, N—H⋯N and N—H⋯O hydrogen bonds. Further, the crystal structure also features two C—H⋯π inter­actions involving the benzene ring of the cation, forming a three-dimensional network.
NOMA Spectral Efficiency Maximization with Improper Gaussian Signaling and SIC Imperfection

The paper studies the downlink of non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). In an effort to overcome the SIC imperfection problem, we propose to adopt improper Gaussian signaling (IGS) for the signal transmission of the NOMA users. In particular, we jointly optimize the IGS circularity coefficient and transmit power to maximize the spectral efficiency subject to minimum Quality-of-Service (QoS) per each user and total power budget. Simulations results show the effectiveness of IGS to compensate for the SIC imperfections compared with proper Gaussian signaling (PGS)-based NOMA systems.
A Self-Learning Approach for Detecting Intrusions in Healthcare Systems

The rapid evolution of the Internet of Medical Things (IoMT) introduces the healthcare ecosystem into a new reality consisting of smart medical devices and applications that provide multiple benefits, such as remote medical assistance, timely administration of medication and real-time monitoring. However, despite the valuable advantages, this new reality increases the cybersecurity and privacy concerns since vulnerable IoMT devices can access and handle autonomously patients’ data. Furthermore, the continuous evolution of cyberattacks, malware and zero-day vulnerabilities require the development of the appropriate countermeasures. In the light of the aforementioned remarks, in this paper, we present an Intrusion Detection and Prevention System (IDPS), which can protect the healthcare communications that rely on the Hypertext Transfer Protocol (HTTP) and the Modbus/Transmission Control Protocol (TCP). HTTP is commonly adopted by conventional healthcare-related services, such as web-based Electronic Health Record (EHR) applications, while Modbus/TCP is an industrial protocol adopted by IoMT. Although the Machine Learning (ML) and Deep Learning (DL) methods have already demonstrated their efficacy in detecting intrusions, the rarely available intrusion detection datasets (especially in the healthcare sector) complicate their global application. The main contribution of this work lies in the fact that an active learning approach is modelled and adopted in order to re-train dynamically the supervised classifiers behind the proposed IDPS. The evaluation analysis demonstrates the efficiency of this work against HTTP and Modbus/TCP cyberattacks, showing also how the entire accuracy is increased in the various re-training phases.
SnF Scheduling of Multicast Transfers Across Inter-Datacenter Optical Networks

Traffic fluctuations make conventional multicast tree difficult to fully utilize the residual bandwidth in the inter-datacenter networks. In this paper, we introduce datacenter storage into the provisioning process of multicast transfers. A scheduling method, named Store-and-Forward-Tree (SnFTree), is proposed for multicast transfers in the software-defined inter-datacenter optical network. By leveraging temporary storage at intermediate sites, a multicast tree can be segmented into time-independent subtrees when provisioning an entire light-tree fails. Benefiting from the broadcast nature of optical multicast, intermediate sites can provide cut-through routing and temporary storage simultaneously with negligible processing delay. By using load-balancing tree computation, traffic can be balanced across the network efficiently. Simulations show that SnFTree provides extra provisioning flexibility and hence outperforms the state-of-the-art scheduling methods in terms of blocking probability and the number of OpenFlow Agent updates.
Flexible Smart Acoustic Wave Patches for Effective Detection and Elimination of Surface Condensation

In this paper, flexible ZnO/ Al Lamb wave device was used to detect and eliminate surface condensations, commonly occurring in chemical process, agriculture, automobile and pipelines. The flexible and smart patch based on acoustic wave devices can identify dew formation through the change of frequency spectrum and then actuate to remove the condensation through acousto-thermal effect. To distinguish the differences between environmental interference (temperature and humidity) and the real condensation, a specified 3D space was built to quantify the degree of distortion of the transmission spectra. For droplets uniformly distributed on the surface of the device with an input power of ∼0.6 W, the evaporation time was significantly reduced to ∼ 1/13 of the natural evaporation time.
Voltage Stabilization in Current Inverters with Fully Controllable Switches

The paper describes a current inverter scheme, based on fully controllable switches with a low modulation frequency of the output curve. There are two ways to regulate the output voltage in the proposed system. The first way is changing of the input current pulses amplitude of the inverter, the second – changing of their duration. This allowed proposing of three methods for stabilization of the output voltage. The realization possibility of the parallel operation of three output-joined current inverters with automatic regulation of the output voltage and load distribution between inverter modules is considered. The obtained results analysis allows recommending of the studied inverter and considered stabilization methods in combined microgrid systems with a common AC bus.
Tapping Bandwidth Widening of CMOS-MEMS Vibro-Impacting Resonators Based on Double-Sided Stopper Structures

A CMOS-MEMS based vibro-impacting resonator with double sided stopper structures has been demonstrated that exhibits a wider tapping bandwidth up to 2.3× compared to the conventional single sided counterpart. In particular, a folded-beam capacitive-comb transduced resonator fabricated using a $0.35-\mu \mathrm{m}$ CMOS process is driven impacts the stopper structure on either side of the shuttle with an air gap of $0.6\ \mu \mathrm{m}$ and results in a flat region in the frequency transmission response, where the tapping occurs, around the resonance frequency of 125 kHz. The effective stiffness of the system and therefore the resonance frequency abruptly increase during tapping, leading to duffed transmission response curves towards higher frequency. Conceptually, the addition stopper in the doubled-sided impacting resonator effects additional stiffness hardening that in turn pushes the impact induced resonance frequency even further. A numerical model established here arrives the same trend about tapping bandwidth widening in the structure with double-sided stoppers.
Deep Q-Learning for Joint Server Selection, Offloading, and Handover in Multi-access Edge Computing

In this paper, we propose a deep reinforcement learning (DRL) based approach to solving the problem of joint server selection, task offloading and handover in a multi-access edge computing (MEC) wireless network. The 5G networks tend to have a large number of users and MEC servers involving large numbers of different states and actions (both continuous and discrete), in which evaluating every possible combination becomes very challenging for traditional DRL methods. In addition, user mobility in 5G requires multiple handover decisions to be made in real-time, adding a new level of complexity to this already hard problem. Based on the recursive decomposition of the action space available to each state, we propose a deep Q-network (DQN) based online algorithm for this high-complexity problem. Numerical results show the proposed algorithm significantly outperforms the traditional Q-learning method and local computation in terms of task success rate and total delay.
A Novel Parameter-Free Energy Efficient Fuzzy Nearest Neighbor Classifier for Time Series Data

Time series classification is an important model in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature of events (i.e., events with similar values may belong to different classes) within the data. Consequently, these studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this motivation, this paper proposes a novel fuzzy nearest neighbor classifier for time series data. The basic idea of our classifier is to transform the very large training data into a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier outperforms the current parameter-free time series classifiers and also the popular deep learning techniques.
Low-Latency Task Classification and Scheduling in Fog/Cloud based Critical e-Health Applications

5G wireless networks have been designed to provide high reliability, ultra-low latency, and support of massive amount of connected devices, in the scope of the Internet of Things (IoT). Advances in electronics and networking are enabling the wide adoption of multiple types of verticals, under the umbrella of IoT. One area of IoT, which is gaining lots of attention, is e-Health. Within e-Health, users’ monitoring in general, and monitoring of vital signs, such as heartbeat rate, are very useful in saving many lives; however, they require ultra-low latency. Cloud-based networking and computing have been proposed to achieve the required low latency. Furthermore, fog computing was proposed to further decrease the achieved latency for critical tasks and services; however, this adds more complexity to the control of the network, in addition to the task scheduling among both the cloud and fog layer. In this paper, we tackle this problem by proposing a task classification and scheduling scheme in a fog-cloud networking environment, by considering comprehensively modeled characteristics of tasks, user profile, environmental exposure and networking features, targeting the improvement of overall latency for higher priority critical tasks. Moreover, we perform an analytical study on the execution comparison between cloud and fog computing services, which paves the way to further develop an orchestrator for task scheduling, among the multi-layer fog-cloud based e-Health systems. Simulation results show that the proposed task scheduling scheme outperforms the benchmark, by guaranteeing ultra-low latency for critical tasks (high-priority), while ensuring sufficient latency performance for latency-tolerant tasks.
Fiber-based Frequency Modulated LiDAR With MEMS Scanning Capability for Long-range Sensing in Automotive Applications

Safe operation of driver assistance systems, especially at higher speeds, remains a challenge. For this automotive application, we propose a long-range coherent LiDAR system concept with two-dimensional micro electro-mechanical system (MEMS) scanning and sparse sensing capability. We address system design challenges for the transmitter, the fiber-based optics and coherent receiver, and the impact on MEMS mirror design. Furthermore, we compare compressed sensing and deep learning techniques to implement sparse sensing and we provide preliminary results of the systems performance with a simplified setup.
LIFH: Learning Interactive Features from HTTP Payload using Image Reconstruction

The complexity and intelligence of the attacks towards the application layer have raised to an unprecedented level. HyperText Transfer Protocol (HTTP), as the widely used application layer protocol, is part of the main vectors for various malicious attacks. The previous detection based on Deep Packet Inspection (DPI) relies heavily on packets, which leads to insufficient detection and a high false alarm rate. In this paper, we propose LIFH, a deep neural network model equipped with interactive information for detecting application-layer attacks. Firstly, the image reconstruction method is designed to reconstruct the HTTP traffic session into an image. Then, the latent features, instead of explicit features which are typically used in machine learning models, are extracted by HTTP-CNN in order to respond against forgery attacks. Finally, the high-level features are further fed to multi-classifiers to identify the traffic involved in malicious activities. We make exclusive experiments and evaluate the performance of LIFH on the standard dataset CICIDS_2017 and IIE_data collected from critical web servers. The results demonstrate that the proposed model can significantly improve the performance of malicious traffic detection with an accuracy of 99.07% and a false positive rate of 0.40% which is superior to the state of the arts.
Fast IPv6 Network Periphery Discovery and Security Implications

Numerous measurement researches have been performed to discover the IPv4 network security issues by leveraging the fast Internet-wide scanning techniques. However, IPv6 brings the 128-bit address space and renders brute-force network scanning impractical. Although significant efforts have been dedicated to enumerating active IPv6 hosts, limited by technique efficiency and probing accuracy, large-scale empirical measurement studies under the increasing IPv6 networks are infeasible now. To fill this research gap, by leveraging the extensively adopted IPv6 address allocation strategy, we propose a novel IPv6 network periphery discovery approach. Specifically, XMap, a fast network scanner, is developed to find the periphery, such as a home router. We evaluate it on twelve prominent Internet service providers and harvest 52M active peripheries. Grounded on these found devices, we explore IPv6 network risks of the unintended exposed security services and the flawed traffic routing strategies. First, we demonstrate the unintended exposed security services in IPv6 networks, such as DNS, and HTTP, have become emerging security risks by analyzing 4.7M peripheries. Second, by inspecting the periphery’s packet routing strategies, we present the flawed implementations of IPv6 routing protocol affecting 5.8M router devices. Attackers can exploit this common vulnerability to conduct effective routing loop attacks, inducing DoS to the ISP’s and home routers with an amplification factor of $\gt {200}$. We responsibly disclose those issues to all involved vendors and ASes and discuss mitigation solutions. Our research results indicate that the security community should revisit IPv6 network strategies immediately.
Nonlinear HPA Impact on Artificial Noise Aided MISO Secure Systems

In this paper, we consider the artificial noise (AN) aided multiple-input single-output (MISO) secure systems in which a multi-antenna transmitter simultaneously transmits the combination of information-bearing signal and artificial noise to a single-antenna legitimate receiver and eavesdropper. At the transmitter front-ends, the signal and/or AN will be amplified by high-power amplifiers (HPAs) which however may work in a nonlinear region and introduce nonlinear distortion. We calculate the received signal-to-noise ratio (SNR) at legitimate receiver and eavesdropper under HPA nonlinearity, and derive the approximated closed-form expression of secrecy rate. Numerical results suggest that the approximated theoretical secrecy performance is very close to Monte Carlo simulations for relatively lower SNRs, and is severely degraded by the nonlinear distortion caused by HPAs as the transmit power increases. Moreover, we further provide a power allocation strategy between information-bearing signal and AN to alleviate the nonlinear distortion.
Joint Beamformer Design and User Scheduling in Integrated Terrestrial-Satellite Networks

In this paper, we investigate the downlink transmission in the integrated terrestrial satellite networks, whereby the same spectrum is shared between two systems, and thus interference to each other should be carefully mitigated. We address this challenging issue by unifying the terrestrial beamformer design and satellite user scheduling into the same optimization framework. This nontrivial problem is decomposed into two subproblems, one deals with the terrestrial beamformer design to control the interference from the terrestrial base stations to the satellite users, the other tries to optimize the scheduling of the satellite users following the framing structure the DVB-S2X standards for satellite communication systems. A deep clustering user scheduling scheme is developed to group suitable satellite users to the same frame using the channel state information as the input feature. Finally, a joint iterative algorithm is designed to maximize the sum rate of all users in the integrated systems. We conduct extensive simulation results to show the effectiveness of the proposed scheme.
Crowd-Sensing Assisted Vehicular Distributed Computing for HD Map Update

High-definition map (HD Map) for autonomous driving brings huge pressure on networks due to its bandwidth-greedy, computing-intensive, and latency-sensitive characteristics. Data collection, transmission, and processing for HD Map update should cooperate to meet these requirements. In this paper, crowd-sensing which exploits the sensing ability of autonomous vehicles is adopted for real-time data collection. Vehicular distributed computing is adopted to improve the computing capability and reduce the transmission of massive raw environmental data. And a crowd-sensing assisted vehicular distributed computing (CS-VDC) mechanism is proposed based on the convergence of sensing, communication, and computation. In addition, considering the differences in sensing range and computing capability of different vehicles, the selection of crowd-sensing nodes and task allocation are jointly optimized to further minimize the communication load. A heuristic algorithm is developed to solve the optimization problem. The performance of the proposed mechanism is evaluated and CS-VDC can always achieve the minimum missing update ratio and amount of equivalent transmission data regardless of the parameter configuration. Especially, the amount of equivalent transmission data under the proposed CS-VDC can be reduced by 37% compared with the nearest node selection mechanism.
An Improved Integrated Cumulant Method by Probability Distribution Pre-Identification in Power System With Wind Generation

The increase of wind generation (WG) has challenged the conventional way of probabilistic load flow (PLF) calculation. A reliable and efficient PLF method is required to face the stochastic nature of various power systems with WG. Firstly, the paper analyzes several typical cumulant methods (CMs) for PLF, such as Gram-Charlier expansion of type A (GCA), Gram-Charlier expansion of type C (GCC), and maximum entropy (ME). Then, an improved integrated CM by probability distribution pre-identification is proposed for power systems with WG based on doubly fed induction generations (DFIGs). The skewness and kurtosis are used as probability distribution pre-identification indices in the CM framework. Meanwhile, the influence of the DFIG control strategy on reactive power is considered in the load flow model and the moment calculation. Finally, the accuracy and efficiency of the proposed method are validated with the IEEE test system. In various scenarios, suitable CM is selected and applied to the PLF based on pre-identifying distribution characteristics. Results reveal that probabilistic density functions (PDFs) of bus voltages and line flows obtained by the proposed method have both accuracy and efficiency.