An ensemble based approach for effective intrusion detection using majority voting

Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion

A Two-Stage Learning Approach for Goalie, Net and Stick Pose Estimation in Ice Hockey

Accurate pose estimation of ice hockey goaltenders presents a unique challenge due to the dynamic nature of the sport and the intricate interactions among the goalie, equipment, and net. This study introduces a comprehensive investigation into goalie pose estimation using both One-Stage and Two-Stage Learning GoalieNet architectures. The One-Stage Learning GoalieNet predicts all keypoints simultaneously, while the Two-Stage Learning GoalieNet employs a Keypoint Predictor Network (KPN) to predict 26 out of 29 keypoints and a Keyheatmap Fusion Network (KFN) to predict 3 stick-related keypoints. Evaluation on a NHL dataset underscores the effectiveness of both approaches in accurately predicting keypoints. Results on the test data reveal a median percentage of detected keypoints of 71% for the Two-Stage approach and 70% for the One-Stage approach, along with normalized localization errors on detected keypoints of 0.0187 for the Two-Stage and 0.0194 for the One-Stage approach. This work introduces the first-ever goalie pose estimation technique designed specifically for ice hockey, accompanied by a thorough analysis of the obtained results

Ensemble Methods for Lung Cancer Gene Mutation Prediction

Previous results from the project "Lung Cancer Screening - A non-invasive methodology for early diagnosis" and literature suggest that the most relevant information to predict the mutation status in lung cancer might be the combination of features from the nodule and other lung structures. Quantitative features extracted from cancer nodules have been used to create predictive models for gene mutation status and screening. Novel ensemble methods will be developed in order to use quantitative features from external structures to the nodule with traditional features from the nodule. The combination of relevant information by the learning models should improve the accuracy of diagnosis

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Entertainment capture through heart rate activity in physical interactive playgrounds

An approach for capturing and modeling individual entertainment (“fun”) preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children’s physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children’s heart rate (HR) signals, and their expressed preferences of how much “fun” particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children’s reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p−value 6 · 10−5). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.peer-reviewe

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.Research was funded by the Basque Government, through ELKARTEK (ref. KK-2020/00049) funding grant

Treatment Selection: Understanding What Works For Whom In Mental Health

Individuals seeking treatment for mental health problems often have to choose between several different treatment options. For disorders like depression and PTSD, many of the available treatments have been found to be, on average, equally effective. Research on precision medicine aims to identify the most effective treatment for each patient. This work is based on the idea that individuals respond differently to treatment, and that these differences can be studied and characterized. The push for personalized and precision approaches in mental health involves identifying moderators - variables that predict differential response into treatment recommendations. Unfortunately, there has been little real-world application of these findings, in part due to the lack of systems suited to translating the information in actionable recommendations. This dissertation will review the history of treatment selection in mental health, and will present specific examples of treatment selection models in depression and PTSD. Differences between treatment selection in the context of two equivalently effective interventions and stratified medicine applications in which goal is to optimize the allocation of stronger and weaker interventions will be discussed. Methodological challenges in building (e.g., variable selection) and evaluating (e.g., cross-validation) treatment selection systems will be explored. Approaches to precision medicine being used by different groups will be compared. Finally, recommendations for future directions will be made