Biblio

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources.

Cross-site scripting (XSS) attack is a kind of code injection that allows an attacker to inject malicious scripts code into a trusted web application. When a user tries to request the injected web page, he is not aware that the malicious script code might be affecting his computer. Nowadays, attackers are targeting the web applications that holding a sensitive data (e.g., bank transaction, e-mails, healthcare, and e-banking) to steal users' information and gain full access to the data which make the web applications to be more vulnerable. In this research, we applied three approaches to find a solution to this most challenging attacks issues. In the first approach, we implemented Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) algorithms to discover and classify XSS attack. In the second approach, we implemented the Content Security Policy (CSP) approach to detect XSS attacks in real-time. In the last approach, we propose a new approach that combines the Web Application Firewall (WAF), Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) to detect and prevent XSS attack in real-time. Our experiment results demonstrated the high performance of AI algorithms. The CSP approach shows the results for the detection system report in real-time. In the third approach, we got more expected system results that make our third model system a more powerful tool to address this research problem than the other two approaches.

Radio-frequency (RF) sensing is a key technology for designing intelligent and secure wireless networks with high spectral efficiency and environment-aware adaptation capabilities. However, existing sensing techniques can extract only limited information from RF signals or assume that the RF signals are generated by certain known protocols. As a result, their applications are limited if proprietary protocols or encryption methods are adopted, or in environments subject to errors such as unintended interference. To address this challenge, we study protocol-agnostic cross-layer sensing to extract high-layer protocol information from raw RF samples without any a priori knowledge of the protocols. First, we present a framework for protocol-agnostic sensing for over-the-air (OTA) RF signals, by taking packet boundary recognition (PBR) as an example. The framework consists of three major components: OTA Signal Generator, Agnostic RF Sink, and Ground Truth Generator. Then, we develop a software-defined testbed using USRP SDRs, with eleven benchmark statistical algorithms implemented in the Agnostic RF Sink, including Kullback-Leibler divergence and cross-power spectral density, among others. Finally, we test the effectiveness of these statistical algorithms in PBR on OTA RF samples, considering a wide variety of transmission parameters, including modulation type, transmission distance, and packet length. It is found that none of these benchmark statistical algorithms can achieve consistently high PBR rate, and new algorithms are required particularly in next-generation low-latency wireless systems.

Software Defined Networking (SDN) is a networking paradigm that has been very popular due to its advantages over traditional networks with regard to scalability, flexibility, and its ability to solve many security issues. Nevertheless, SDN networks are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML). Different than other approaches found in literature which use the native flow features only for attack detection, our model extends the native features. The extended flow features are the average flow packet size, the number of flows to the same host as the current flow in the last 5 seconds, and the number of flows to the same host and port as the current flow in the last 5 seconds. Six ML algorithms were evaluated, namely Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experiments showed that RF is the best performing ML algorithm. Also, results showed that our model is able to detect attacks accurately and quickly, with a low probability of dropping normal traffic.

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In the upcoming advent of 6G networks, underwater communications are expected to play a relevant role in the context of overlapping hybrid wireless networks, following a multilayer architecture i.e., aerial-ground-underwater. The concept of Internet of Underwater Things defines different communication and networking technologies, as well as positioning and tracking services, suitable for harsh underwater scenarios. In this paper, we present a footprinting localization algorithm based on optical wireless signals in the visible range. The proposed technique is based on a hybrid Radio Frequency (RF) and Visible Light Communication (VLC) network architecture, where a central RF sensor node holds an environment channel gain map i.e., database, that is exploited for localization estimation computation. A recursive localization algorithm allows to estimate user positions with centimeter-based accuracy, in case of different turbidity scenarios.

This paper proposes a machine-learning-based advanced online dynamic security assessment (DSA) method, which provides a detailed evaluation of the system stability after a disturbance by predicting impending loss of synchronism (LOS) of generators. Voltage angles at generator buses are used as the features of the different random forest (RF) classifiers which are trained to consecutively predict LOS of the generators as a contingency proceeds and updated measurements become available. A wide range of contingencies for various topologies and operating conditions of the IEEE 118-bus system has been studied in offline analysis using the GE positive sequence load flow analysis (PSLF) software to create a comprehensive dataset for training and testing the RF models. The performances of the trained models are evaluated in the presence of measurement errors using various metrics. The results reveal that the trained models are accurate, fast, and robust to measurement errors.

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%.

In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.

Nowadays most of our data is stored on an electronic device. The risk of that device getting infected by Viruses, Malware, Worms, Trojan, Ransomware, or any unwanted invader has increased a lot these days. This is mainly because of easy access to the internet. Viruses and malware have evolved over time so identification of these files has become difficult. Not only by viruses and malware your device can be attacked by a click on forged URLs. Our proposed solution for this problem uses machine learning techniques and signature matching techniques. The main aim of our solution is to identify the malicious programs/URLs and act upon them. The core idea in identifying the malware is selecting the key features from the Portable Executable file headers using these features we trained a random forest model. This RF model will be used for scanning a file and determining if that file is malicious or not. For identification of the virus, we are using the signature matching technique which is used to match the MD5 hash of the file with the virus signature database containing the MD5 hash of the identified viruses and their families. To distinguish between benign and illegitimate URLs there is a logistic regression model used. The regression model uses a tokenizer for feature extraction from the URL that is to be classified. The tokenizer separates all the domains, sub-domains and separates the URLs on every `/'. Then a TfidfVectorizer (Term Frequency - Inverse Document Frequency) is used to convert the text into a weighted value. These values are used to predict if the URL is safe to visit or not. On the integration of all three modules, the final application will provide full system protection against malicious software.

In recent years, cyber security has become a challenging task to protect the networks and computing systems from various types of digital attacks. Therefore, to preserve these systems, various innovative methods have been reported and implemented in practice. However, still more research work needs to be carried out to have malware free computing system. In this paper, an attempt has been made to develop simple but reliable ML based malware detection systems which can be implemented in practice. Keeping this in view, the present paper has proposed and compared the performance of three ML based malware detection systems applicable for computer systems. The proposed methods include k-NN, RF and LR for detection purpose and the features extracted comprise of Byte and ASM. The performance obtained from the simulation study of the proposed schemes has been evaluated in terms of ROC, Log loss plot, accuracy, precision, recall, specificity, sensitivity and F1-score. The analysis of the various results clearly demonstrates that the RF based malware detection scheme outperforms the model based on k-NN and LR The efficiency of detection of proposed ML models is either same or comparable to deep learning-based methods.

This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.

Explainable Artificial Intelligence (XAI) is expected to play a key role in the design phase of next generation cellular networks. As 5G is being implemented and 6G is just in the conceptualization stage, it is increasingly clear that AI will be essential to manage the ever-growing complexity of the network. However, AI models will not only be required to deliver high levels of performance, but also high levels of explainability. In this paper we show how fuzzy models may be well suited to address this challenge. We compare fuzzy and classical decision tree models with a Random Forest (RF) classifier on a Quality of Experience classification dataset. The comparison suggests that, in our setting, fuzzy decision trees are easier to interpret and perform comparably or even better than classical ones in identifying stall events in a video streaming application. The accuracy drop with respect to RF classifier, which is considered to be a black-box ensemble model, is counterbalanced by a significant gain in terms of explainability.

Federated learning (FL) is an emerging paradigm for facilitating multiple organizations' data collaboration without revealing their private data to each other. Recently, vertical FL, where the participating organizations hold the same set of samples but with disjoint features and only one organization owns the labels, has received increased attention. This paper presents several feature inference attack methods to investigate the potential privacy leakages in the model prediction stage of vertical FL. The attack methods consider the most stringent setting that the adversary controls only the trained vertical FL model and the model predictions, relying on no background information of the attack target's data distribution. We first propose two specific attacks on the logistic regression (LR) and decision tree (DT) models, according to individual prediction output. We further design a general attack method based on multiple prediction outputs accumulated by the adversary to handle complex models, such as neural networks (NN) and random forest (RF) models. Experimental evaluations demonstrate the effectiveness of the proposed attacks and highlight the need for designing private mechanisms to protect the prediction outputs in vertical FL.

The need of more secured and environmental energy is becoming a necessity and priority in an environment suffering from serious problems due to technological development. Since the Smart Grid is a promising alternative that supports green energy and enhances a better management of electricity, the security side has became one of the major and critical associated issues in building the communication network in the microgrid.In this paper we will present the Smart Grid Cyber security challenges and propose a distributed algorithm that face one of the biggest problems threatening the smart grid which is fires.

In this paper, an E-band MIMO radar with 1 transmit and 4 receive channels is designed. The signal bandwidth is 2GHz at 77GHz, the max power of transmitted signal which is Frequency-modulated continuous-wave (FMCW) is 13dBm. This radar consists of two cascade parts: RF frond-end and digital signal process block. The RF front-end part includes antenna array, millimeter wave transceiver chips, and the digital signal process part includes FPGA, DSP and power supply circuits. It could be used in foreign object detection (FOD), landing assistance of helicopter and security checking.

Surface acoustic wave devices based on LiNbO3/interlayer/substrate layered structure have attracted great attention due to the high electromechanical coupling coefficient (K2) of LiNbO3 and the energy confinement effect of the layered structure. In this study, 30° YX-LiNbO3 (LN)/SiO2/Si multilayered structure, which can excited shear-horizontal surface acoustic wave (SH-SAW) with high K2, was proposed. The optimized orientation of LiNbO3 was verified by the effective permittivity method based on the stiffness matrix. The phase velocity, K2 value, and temperature coefficient of frequency (TCF) of the SH-SAW were calculated as a function of the LiNbO3 thickness at different thicknesses of the SiO2 in 30° YX-LiNbO3/SiO2/Si multilayer structure by finite element method (FEM). The results show that the optimized LiNbO3 thickness is 0.1 and the optimized SiO2 thickness is 0.2λ. The optimized Al electrode thickness and metallization ratio are 0.07 and 0.4, respectively. The K2 of the SH-SAW is 29.89%, the corresponding phase velocity is 3624.00 m/s and TCF is about 10 ppm/°C with the optimized IDT/30° YX-LiNbO3/SiO2/Si layered structure.

In this paper, we propose two new attacks: the Adversarial Universal False Positive (UFP) Attack and the Adversarial Universal False Negative (UFN) Attack. The objective of this research is to introduce a new class of attack using only feature vector information. The results show the potential weaknesses of five machine learning (ML) classifiers. These classifiers include k-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forrest (RF), a Support Vector Machine (SVM) with a Radial Basis Function (RBF) Kernel, and XGBoost (XGB).

This work focuses on the design and implementation of a microcontroller for apply all the knowledge acquired during Engineering Electronics career. In order to improve the knowledge about RF technologies, security system have been created, which increases the number of applications used in these days. This design utilizes light sensors as the end device for detecting any changes of resistance. The results show that the designed system can send and receive data until 100 meters of distance between module sides (receiver-transmitter). This security system designed using PIC 16F84 microcontroller as entire brain of the system with sensors, has been successfully designed and implement considering some factors such as economy, availability of components and durability in the design process.

Majority of the daily and business activities nowadays are integrated and interconnected to the world across national, geographic and boundaries. Securing the Internet of Things (IoT) system is a challenge as these low powered devices in IoT system are very vulnerable to cyber-attacks and this will reduce the reliability of the system. Software Defined Network (SDN) intends to greatly facilitate the policy enforcement and dynamic network reconfiguration. This paper presents several architectures in the integration of IoT via SDN to improve security in the network and system.

In recent years, the Internet of Things technology has developed rapidly. RFID technology, as an important branch of the Internet of Things technology, is widely used in logistics, medical, military and other fields. RFID technology not only brings convenience to people's production and life, but also hides many security problems. However, the current research on RFID technology mainly focuses on the technology application, and there are relatively few researches on its security analysis. This paper firstly studies the authentication mechanism and storage mechanism of RFID technology, then analyzes the common vulnerabilities of RFID, and finally gives the security protection suggestions.

Wireless technology is growing at a fast rate to accommodate the expanding user demands. Currently the radio frequency (RF) spectrum is highly congested and more susceptible to signal fratricide and interference. Therefore, full duplexing techniques are required to enhance the access to the spectrum. Simultaneous Transmit and receive systems (STAR), also known as in-band full duplex systems, are gaining higher attention due to their capability to double spectral efficiency. However, successful implementation of STAR systems requires significant isolation between the transmit and receive signals to reduce self-interference (SI) signal. To minimize this self-interference, front-end coupling cancellation circuits are employed in STAR system. In this paper, an RF coupling suppression circuit is presented based on a hybrid finite impulse response filter (FIR) and resonator architecture. Notably, this newly developed FIR-resonator circuit achieves \textbackslashtextgreater30dB cancellation across a \textbackslashtextgreater1.5:1 bandwidth.

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

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.

Simultaneous Wireless Information and Power Transfer (SWIPT) technique is introduced in Radio Frequency (RF) communication to carry both information and power in same medium. In this approach, the energy can be harvested while decoding the information carries in an RF wave. Recently, the same concept applied in Visible Light Communication (VLC) namely Simultaneous Light Wave Information and Power Transfer (SLIPT), which is highly recommended in an indoor applications to overcome the problem facing in RF communication. Thus, SLIPT is introduced to transmit the power through a Light Emitting Diode (LED) luminaries. In this work, we compare both SWIPT and SLIPT technologies and realize SLIPT technology archives increased performance in terms of the amount of harvested energy, outage probability and error rate performance.