MTEDS: Multivariant Time Series-Based Encoder-Decoder System for Anomaly Detection

Intrusion detection systems examine the computer or network for potential security vulnerabilities. Time series data is real-valued. The nature of the data influences the type of anomaly detection. As a result, network anomalies are operations that deviate from the norm. These anomalies can cause a wide range of device malfunctions, overloads, and network intrusions. As a result of this, the network\u27s normal operation and services will be disrupted. The paper proposes a new multi-variant time series-based encoder-decoder system for dealing with anomalies in time series data with multiple variables. As a result, to update network weights via backpropagation, a radical loss function is defined. Anomaly scores are used to evaluate performance. The anomaly score, according to the findings, is more stable and traceable, with fewer false positives and negatives. The proposed system\u27s efficiency is compared to three existing approaches: Multiscaling Convolutional Recurrent Encoder-Decoder, Autoregressive Moving Average, and Long Short Term Medium-Encoder-Decoder. The results show that the proposed technique has the highest precision of 1 for a noise level of 0.2. Thus, it demonstrates greater precision for noise factors of 0.25, 0.3, 0.35, and 0.4, and its effectiveness

Detecting Invasive Insects Using Uncewed Aerial Vehicles and Variational Autoencoders

In this thesis, we use machine learning techniques to address limitations in our ability to monitor pest insect migrations. Invasive insect populations, such as the brown marmorated stink bug (BMSB), cause significant economic and environmental damages. In order to mitigate these damages, tracking BMSB migration is vital, but it also poses a challenge. The current state-of-the-art solution to track insect migrations is called mark-release-recapture. In mark-release-recapture, a researcher marks insects with a fluorescent powder, releases them back into the wild, and searches for the insects using ultra-violet flashlights at suspected migration destination locations. However, this involves a significant amount of labor and has a low recapture rate. By automating the insect search step, the recapture rate can be improved, reducing the amount of labor required in the process and improving the quality of the data. We propose a solution to the BMSB migration tracking problem using an unmanned aerial vehicle (UAV) to collect video data of the area of interest. Our system uses an ultra violet (UV) lighting array and digital cameras mounted on the bottom of the UAV, as well as artificial intelligence algorithms such as convolutional neural networks (CNN), and multiple hypotheses tracking (MHT) techniques. Specifically, we propose a novel computer vision method for insect detection using a Convolutional Variational Auto Encoder (CVAE). Our experimental results show that our system can detect BMSB with high precision and recall, outperforming the current state-of-the-art. Additionally, we associate insect observations using MHT, improving detection results and accurately counting real-world insects

LogBERT: Log Anomaly Detection via BERT

When systems break down, administrators usually check the produced logs to diagnose the failures. Nowadays, systems grow larger and more complicated. It is labor-intensive to manually detect abnormal behaviors in logs. Therefore, it is necessary to develop an automated anomaly detection on system logs. Automated anomaly detection not only identifies malicious patterns promptly but also requires no prior domain knowledge. Many existing log anomaly detection approaches apply natural language models such as Recurrent Neural Network (RNN) to log analysis since both are based on sequential data. The proposed model, LogBERT, a BERT-based neural network, can capture the contextual information in log sequences. LogBERT is trained on normal log data considering the scarcity of labeled abnormal data in reality. Intuitively, LogBERT learns normal patterns in training data and flags test data that are deviated from prediction as anomalies. We compare LogBERT with four traditional machine learning models and two deep learning models in terms of precision, recall, and F1 score on three public datasets, HDFS, BGL, and Thunderbird. Overall, LogBERT outperforms the state-of-art models for log anomaly detection