Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

Due to their low power consumption and computing power, Internet of Things (IoT) devices are difficult to secure, and the rapid growth of IoT devices in the home increases the risk of cyber-attacks. One method of preventing cyberattacks is to employ an intrusion detection system (IDS), which detects incoming attacks and notifies the user, allowing for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning, but these efforts have been unsuccessful. In this paper, we classify network attacks using two Convolutional Neural Networks (CNN) models i.e., MyCNN and IoTCNN to automatically detect various kind malignant and benign intrusion in IoT network. The purpose of this research is to evaluate the use of deep learning in IoT IDS. The neural network was trained in this experiment using the NF-UNSW-NB15-v2 dataset, which contains nine distinct types of attacks. The data from the network stream was converted to Red Green and Blue (RGB) images, which were then used to train the neural network. To establish baseline models, we proposed two models with the name of MyCNN and IoTCNN. When compared the proposed MyCNN convolutional neural network for network attack classification, the IoTCNN was outperformed by the MyCNN model. Additionally, it demonstrates that both networks achieve higher accuracy in the majority of categories but the IoTCNN achieved lower than the proposed MyCNN model for network attack detection. We discovered that the MyCNN is generally more suitable to be deployed for intrusion detection in IoT devices

CNN-LSTM framework to automatically detect anomalies in farmland using aerial images from UAVs

Using aerial inspection techniques in farmlands can yield vital data instrumental in mitigating various impediments to optimizing farming practices. Farmland anomalies (standing water and clusters of weeds) can impede farming practices, leading to the improper utilization of farmland and the disruption of agricultural development. Utilizing Unmanned Aerial Vehicles (UAVs) for remote sensing is a highly effective method for obtaining extensive imagery of farmland. Visual data analytics in the context of automatic pattern recognition from collected data is valuable for advancing Deep Learning (DL) -assisted farming models. This approach shows significant potential in enhancing agricultural productivity by effectively capturing crop patterns and identifying anomalies in farmland. Furthermore, it offers prospective solutions to address the inherent barriers farmers encounter. This study introduces a novel framework, namely the hybrid Convolutional Neural Networks and Long Short-Term Memory (HCNN-LSTM), which aims to detect anomalies in farmland using images obtained from UAVs automatically. The system employs a Convolutional Neural Network (CNN) for deep feature extraction, while Long Short-Term Memory (LSTM) is utilized for the detection task, leveraging the extracted features. By integrating these two Deep Learning (DL) architectures, the system attains an extensive knowledge of farm conditions, facilitating the timely identification of irregularities such as the presence of water, clusters of weeds, nutrient deficit, and crop disease. The proposed methodology is trained and evaluated using the Agriculture-Vision challenge database. The results obtained from the experiment demonstrate that the proposed system has achieved a high level of accuracy, with a value of 99.7%, confirming the effectiveness of the proposed approach

Comparing a Hybrid Multi-layered Machine Learning Intrusion Detection System to Single-layered and Deep Learning Models

Advancements in computing technology have created additional network attack surface, allowed the development of new attack types, and increased the impact caused by an attack. Researchers agree, current intrusion detection systems (IDSs) are not able to adapt to detect these new attack forms, so alternative IDS methods have been proposed. Among these methods are machine learning-based intrusion detection systems. This research explores the current relevant studies related to intrusion detection systems and machine learning models and proposes a new hybrid machine learning IDS model consisting of the Principal Component Analysis (PCA) and Support Vector Machine (SVM) learning algorithms. The NSL-KDD Dataset, benchmark dataset for IDSs, is used for comparing the models’ performance. The performance accuracy and false-positive rate of the hybrid model are compared to the results of the model’s individual algorithmic components to determine which components most impact attack prediction performance. The performance metrics of the hybrid model are also compared to two deep learning Autoencoder Neuro Network models and the results found that the complexity of the model does not add to the performance accuracy. The research showed that pre-processing and feature selection impact the predictive accuracy across models. Future research recommendations were to implement the proposed hybrid IDS model into a live network for testing and analysis, and to focus research into the pre-processing algorithms that improve performance accuracy, and lower false-positive rate. This research indicated that pre-processing and feature selection/feature extraction can increase model performance accuracy and decrease false-positive rate helping businesses to improve network security

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure