Biblio

Microgrids are a promising alternative to the traditional distribution systems due to their highly desirable features, such as, reliability, resiliency, and efficiency. This paper covers the design, simulation, and economic analysis of a theoretically designed modern, mixed-use commercial and residential building on a feeder in Charleston, SC, USA. The designed system is simulated in PSCAD/EMTDC. The system combines a natural gas CHP turbine and generator block set, solar photovoltaics (PV), and a battery energy storage system (BESS). It is planned to provide power through a DC lighting bus and an AC to several different commercial load profiles as well as 40 apartments of varying sizes. Additionally, a comprehensive economic analysis is completed with available or estimated pricing to prove the feasibility of such a project.

Internet of Things (IoT) has been growing exponentially in the commercial market in recent years. It is also a fact that people hold one or more computing devices at home. Many of them have been developed to operate through internet connectivity with cloud computing technologies that result in the demand for fast, robust, and secure services. In most cases, the lack of these services makes difficult the transfer of data to fulfill the devices' purposes. Under these conditions, an intermediate layer or middleware is needed to process, filter, and send data through a more efficient alternative. This paper presents the adaptive solution of a middleware architecture as an intermediate layer between smart devices and cloud computing to enhance the management of the heterogeneity of data provining from IoT devices. The proposed middleware provides easy configuration, adaptability, and bearability for different environments. Finally, this solution has been implemented in the healthcare domain, in which IoT solutions are deployed into Ambient Assisted Living (AAL) environments.

Industrial Control Systems (ICSs) are widely used in critical infrastructure around the world to provide services that sustain peoples' livelihoods and economic operations. However, compared with the critical infrastructure, the security of the ICS itself is still insufficient, and there will be a degree of damage, if it is attacked or invaded. In the past, an ICS was designed to operate in a traditional closed network, so the industrial equipment and transmission protocol lacked security verification. In addition, an ICS has high availability requirements, so that its equipment is rarely replaced and upgraded. Although many scholars have proposed the defense mechanism that is applicable to ICS in the past, there is still a lack of tested means to verify these defense technologies. The purpose of this study is to analyze the security of a system using the Modbus transmission protocol in an ICS, to establish a modular security test system based on four types of attacks that have been identified in the past literature, namely, a detection attack, a command injection attack, a response injection attack and a denial of service, to implement the attack results and to display the process in the virtual environment of Conpot and Rapid SCADA, and finally, to adopt the ICS security standards mentioned by previous scholars, namely, confidentiality, integrity and availability, as the performance evaluation criteria of this study.

In this paper, a time-driven performance-aware mathematical model for trust in the robot is proposed for a Human-Robot Collaboration (HRC) framework. The proposed trust model is based on both the human operator and the robot performances. The human operator’s performance is modeled based on both the physical and cognitive performances, while the robot performance is modeled over its unpredictable, predictable, dependable, and faithful operation regions. The model is validated via different simulation scenarios. The simulation results show that the trust in the robot in the HRC framework is governed by robot performance and human operator’s performance and can be improved by enhancing the robot performance.

Much research is concentrated on improving models for host-based intrusion detection systems (HIDS). Typically, such research aims at improving a model's results (e.g., reducing the false positive rate) in the familiar static training/testing environment using the standard data sources. Matching advancements in the machine learning community, researchers in the syscall HIDS domain have developed many complex and powerful syscall-based models to serve as anomaly detectors. These models typically show an impressive level of accuracy while emphasizing on minimizing the false positive rate. However, with each proposed model iteration, we get further from the setting in which these models are intended to operate. As kernels become more ornate and hardened, the implementation space for anomaly detection models is narrowing. Furthermore, the rapid advancement of operating systems and the underlying complexity introduced dictate that the sometimes decades-old datasets have long been obsolete. In this paper, we attempt to bridge the gap between theoretical models and their intended application environments by examining the recent Linux kernel 5.7.0-rc1. In this setting, we examine the feasibility of syscall-based HIDS in modern operating systems and the constraints imposed on the HIDS developer. We discuss how recent advancements to the kernel have eliminated the previous syscall trace collect method of writing syscall table wrappers, and propose a new approach to generate data and place our detection model. Furthermore, we present the specific execution time and memory constraints that models must meet in order to be operable within their intended settings. Finally, we conclude with preliminary results from our model, which primarily show that in-kernel machine learning models are feasible, depending on their complexity.

In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51% with a 7.84% false negative rate.

Network security has become an important issue in our work and life. Hackers' attack mode has been upgraded from normal attack to APT( Advanced Persistent Threat, APT) attack. The key of APT attack chain is the penetration and intrusion of active directory, which can not be completely detected via the traditional IDS and antivirus software. Further more, lack of security protection of existing solutions for domain control aggravates this problem. Although researchers have proposed methods for domain attack detection, many of them have not yet been converted into effective market-oriented products. In this paper, we analyzes the common domain intrusion methods, various domain related attack behavior characteristics were extracted from ATT&CK matrix (Advanced tactics, techniques, and common knowledge) for analysis and simulation test. Based on analyzing the log file generated by the attack, the domain attack detection rules are established and input into the analysis engine. Finally, the available domain intrusion detection system is designed and implemented. Experimental results show that the network attack detection method based on the analysis of domain attack behavior can analyze the log file in real time and effectively detect the malicious intrusion behavior of hackers , which could facilitate managers find and eliminate network security threats immediately.

Network security is critical to be able to maintain the information, especially on servers that store a lot of information; several types of attacks can occur on servers, including brute force and DDoS attacks; in the case study in this research, there are four servers used so that a network security system that can synchronize with each other so that when one server detects an attack, another server can take precautions before the same attack occurs on another server.fail2ban is a network security tool that uses the IDPS (Intrusion Detection and Prevention System) method which is an extension of the IDS (Intrusion Detection System) combined with IP tables so that it can detect and prevent suspicious activities on a network, fail2ban automatically default can only run on one server without being able to synchronize on other servers. With a network security system that can run on multiple servers, the attack prevention process can be done faster because when one server detects an attack, another server will take precautions by retrieving the information that has entered the collector database synchronizing all servers other servers can prevent attacks before an attack occurs on that server.

Digital connectivity is fundamental to the health care system to deliver safe and effective care. However, insecure connectivity could be a major threat to patient safety and privacy (e.g., in August 2017, FDA recalled 465,000 pacemakers because of discovering security flaws). Although connecting a patient's pacemaker to the Internet has many advantages for monitoring the patient, this connectivity opens a new door for cyber-attackers to steal the patient data or even control the pacemaker or damage it. Therefore, patients are forced to choose between connectivity and security. This paper presents a framework for secure and private communications between wearable medical devices and patient monitoring systems. The primary objective of this research is twofold, first to identify and analyze the communication vulnerabilities, second, to develop a framework for combating unauthorized access to data through the compromising of computer security. Specifically, hiding targets from cyber-attackers could prevent our system from future cyber-attacks. This is the most effective way to stop cyber-attacks in their first step.

Computer virus detection technology is an important basic security technology in the information age. The current detection technology has a high success rate for the detection of known viruses and known virus infection technologies, but the development of detection technology often lags behind the development of computer virus infection technology. Under Windows system, there are many kinds of file viruses, which change rapidly, and pose a continuous security threat to users. The research of new file virus infection technology can provide help for the development of virus detection technology. In this paper, a new virus infection technology based on dynamic binary analysis is proposed to execute file virus infection. Using the new virus infection technology, the infected executable file can be detected in the experimental environment. At the same time, this paper discusses the detection method of new virus infection technology. We hope to provide help for the development of virus detection technology from the perspective of virus design.

Quasi-Realistic cyber-physical system (QR-CPS) testbed architecture and operational environment are critical for testing and validating various cyber attack-defense algorithms for the wide-area resilient power systems. These QR-CPS testbed environments provide a realistic platform for conducting the Grid Exercise (GridEx), CPS security training, and attack-defense exercise at a broader scale for the cybersecurity of Energy Delivery Systems. The NERC has established a tabletop based GridEx platform for the North American power utilities to demonstrate how they would respond to and recover from cyber threats and incidents. The NERC-GridEx is a bi-annual activity with tabletop attack injects and incidence response management. There is a significant need to build a testbed-based hands-on GridEx for the utilities by leveraging the CPS testbeds, which imitates the pragmatic CPS grid environment. We propose a CPS testbed-based Quasi-Realistic Grid Exercise (QR-GridEx), which is a model after the NERC's tabletop GridEx. We have designed the CPS testbed-based QR-GridEx into two parts. Part-I focuses on the modeling of synthetic grid models for the utilities, including SCADA and WAMS communications, and attack-and-defense software systems; and the Part-II focuses on the incident response management and risk-based CPS grid investment strategies. This paper presents the Part-I of the CPS testbed-based QRGridEx, which includes modeling of the synthetic grid models in the real-time digital simulator, stealthy, and coordinated cyberattack vectors, and integration of intrusion/anomaly detection systems. We have used our existing HIL CPS security testbed to demonstrate the testbed-based QR-GridEx for a Texas-2000 bus US synthetic grid model and the IEEE-39 bus grid models. The experiments demonstrated significant results by 100% real-time performance with zero overruns for grid impact characteristics against stealthy and coordinated cyberattack vectors.

Physical layer authentication scheme for node authentication using the time-reversal (TR) process and the location-specific key feature of the channel impulse response (CIR) in an underwater time-varying multipath environment is proposed. TR is a well-known signal focusing technique in signal processing; this focusing effect is used by the database maintaining node to authenticate the sensor node by convolving the estimated CIR from a probe signal with its database of CIRs. Maximum time-reversal resonating strength (MTRRS) is calculated to make an authentication decision. This work considers a static underwater acoustic sensor network (UASN) under the “Alice- Bob-Eve” scenario. The performance of the proposed scheme is expressed by the Probability of Detection (PD) and the Probability of False Alarm (PFA).

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-DEOBS, a JavaScript deobfuscation tool that we have built. The aim of SAFE-DEOBS is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-DEOBS through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.

Many detection approaches have been proposed to address growing threat of Distributed Denial of Service (DDoS) attacks on the Internet. The attack detection is the initial step in most of the mitigation systems. This study examined the methods used to detect DDoS attacks with the focus on learning based approaches. These approaches were compared based on their efficiency, operating load and scalability. Finally, it is discussed in details.

Penetration testing, also known as Pen testing is usually performed by a testing professional in order to detect security threats involved in a system. Penetration testing can also be viewed as a fake cyber Security attack, done in order to see whether the system is secure and free of vulnerabilities. Penetration testing is widely used for testing both Network and Software, but somewhere it fails to make IoT more secure. In IoT the security risk is growing day-by-day, due to which the IoT networks need more penetration testers to test the security. In the proposed work an effort has been made to compile and aggregate the information regarding VAPT(Vulnerability Assessment and Penetrating Testing) in the area of IoT.

Botnets are one of the major threats on the Internet. They are used for malicious activities to compromise the basic network security goals, namely Confidentiality, Integrity, and Availability. For reliable botnet detection and defense, deep learning-based approaches were recently proposed. In this paper, four different deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, and Multi-layer Perception (MLP) are applied for botnet detection and simulation studies are carried out using the CTU-13 botnet traffic dataset. We use several performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score to evaluate the performance of each model on classifying both known and unknown (zero-day) botnet traffic patterns. The results show that our deep learning models can accurately and reliably detect both known and unknown botnet traffic, and show better performance than other deep learning models.

Microarchitectural Side-Channel Attacks (SCAs) have emerged recently to compromise the security of computer systems by exploiting the existing processors' hardware vulnerabilities. In order to detect such attacks, prior studies have proposed the deployment of low-level features captured from built-in Hardware Performance Counter (HPC) registers in modern microprocessors to implement accurate Machine Learning (ML)-based SCAs detectors. Though effective, such attack detection techniques have mainly focused on binary classification models offering limited insights on identifying the type of attacks. In addition, while existing SCAs detectors required prior knowledge of attacks applications to detect the pattern of side-channel attacks using a variety of microarchitectural features, detecting unknown (zero-day) SCAs at run-time using the available HPCs remains a major challenge. In response, in this work we first identify the most important HPC features for SCA detection using an effective feature reduction method. Next, we propose Phased-Guard, a two-level machine learning-based framework to accurately detect and classify both known and unknown attacks at run-time using the most prominent low-level features. In the first level (SCA Detection), Phased-Guard using a binary classification model detects the existence of SCAs on the target system by determining the critical scenarios including system under attack and system under no attack. In the second level (SCA Identification) to further enhance the security against side-channel attacks, Phased-Guard deploys a multiclass classification model to identify the type of SCA applications. The experimental results indicate that Phased-Guard by monitoring only the victim applications' microarchitectural HPCs data, achieves up to 98 % attack detection accuracy and 99.5% SCA identification accuracy significantly outperforming the state-of-the-art solutions by up to 82 % in zero-day attack detection at the cost of only 4% performance overhead for monitoring.

Aiming at the requirements of network access control, illegal outreach control, identity authentication, security monitoring and application system access control of information network, an integrated network access and behavior control model based on security policy is established. In this model, the network access and behavior management control process is implemented through abstract policy configuration, network device and application server, so that management has device-independent abstraction, and management simplification, flexibility and automation are improved. On this basis, a general framework of policy-based access and behavior management control is established. Finally, an example is given to illustrate the method of device connection, data drive and fusion based on policy-based network access and behavior management control.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

The solution of using existing WiFi devices for measurement and maintenance, and establishing a WiFi fingerprint database for precise localization has become a popular method for indoor localization. The traditional WiFi fingerprint privacy protection scheme increases the calculation amount of the client, but cannot completely protect the security of the client and the fingerprint database. In this paper, we make use of WiFi devices to present a Practical Privacy Protection Scheme In WiFi Fingerprint-based Localization PPWFL. In PPWFL, the localization server establishes a pre-partition in the fingerprint database through the E-M clustering algorithm, we divide the entire fingerprint database into several partitions. The server uses WiFi fingerprint entries with partitions as training data and trains a machine learning model. This model can accurately predict the client's partition based on fingerprint entries. The client uses the trained machine learning model to obtain its partition location accurately, picks up WiFi fingerprint entries in its partition, and calculates its geographic location with the localization server through secure multi-party computing. Compared with the traditional solution, our solution only uses the WiFi fingerprint entries in the client's partition rather than the entire fingerprint database. PPWFL can reduce not only unnecessary calculations but also avoid accidental errors (Unexpected errors in fingerprint similarity between non-adjacent locations due to multipath effects of electromagnetic waves during the propagation of complex indoor environments) in fingerprint distance calculation. In particular, due to the use of Secure Multi-Party Computation, most of the calculations are performed in the local offline phase, the client only exchanges data with the localization server during the distance calculation phase. No additional equipment is needed; our solution uses only existing WiFi devices in the building to achieve fast localization based on privacy protection. We prove that PPWFL is secure under the honest but curious attacker. Experiments show that PPWFL achieves efficiency and accuracy than the traditional WiFi fingerprint localization scheme.

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer’s neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.

This paper presents a proximity coupled wideband wearable antenna operating between 4.71 GHz and 5.81 GHz with 5.2 GHz as centre frequency for biomedical telemetry applications in ISM band (IEEE 802.11 Standard). Two layers of different flexible substrate materials, ethylene-vinyl acetate and felt make the design mechanically stable. Bandwidth improvement is achieved by introducing two slots on elliptical ground plane. Highest gain of 3.72 dB and front to back ratio (FBR) of 6.55 is obtained in the given frequency band. The dimensions of antenna have been optimized to have desired bandwidth of 1100 MHz (\$\textbackslashtextbackslashsimeq\$21%). The specific absorption rate (SAR) value is 1.12 \$W/Kg\$ for 1 g of human body tissue. Both simulated and measured results are presented for the structure.

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/λ DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalization scheme exhibits 1.8-dB OSNR improvement at 15% FEC limit.

Recent work has quantified the degradations that occur in convolutional neural nets (CNN) deployed in harsh environments like space-based image or radio frequency (RF) processing applications. Such degradations yield a robust correlation and causality between single-event upset (SEU) induced errors in memory weights of on-orbit CNN implementations. However, minimal considerations have been given to how the resilience of CNNs can be improved algorithmically as opposed to via enhanced hardware. This paper focuses on RF-processing CNNs and performs an in-depth analysis of applying software-based error detection and correction mechanisms, which may subsequently be combined with protections of radiation-hardened processor platforms. These techniques are more accessible for low cost smallsat platforms than ruggedized hardware. Additionally, methods for minimizing the memory and computational complexity of the resulting resilience techniques are identified. Combined with periodic scrubbing, the resulting techniques are shown to improve expected lifetimes of CNN-based RF-processing algorithms by several orders of magnitude.