scholarly journals A Kernel Rootkit Detection Approach Based on Virtualization and Machine Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 91657-91666
Author(s):  
Donghai Tian ◽  
Rui Ma ◽  
Xiaoqi Jia ◽  
Changzhen Hu
Keyword(s):  
Machine Learning ◽  
Detection Approach ◽  
Rootkit Detection

IoT-Controlled Railway Gate System With ML Object Detection Approach

2020 ◽  
pp. 241-263
Author(s):  
Megha Kamble ◽  
Jaspreet Mehra ◽  
Monika Jain
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Sensor Integration ◽  
Security Threats ◽  
Iot Security ◽  
Accident Probability ◽  
Railway Infrastructure ◽  
Detection Approach ◽  
Gate System ◽  
Railway Crossing

The growing demand of internet of things (IoT) has rendered advancement in the practical fields towards society. In spite of recent advancements and cost effective IoT solutions for smart railway infrastructure, presently, most of the railway gates in India are opened and closed manually. This is a time-consuming process. It is an error-prone system and raises the accident probability. This chapter evaluates and demonstrates the applicability of IoT to resolve the problem of unmanned automatic railway crossing. The aim is to propose a prototype that will control with the help of microcontroller board, IoT sensor integration, and integrating it to machine learning-based image analysis to detect the intermediate real time obstacle (obstacle on the track). This kind of IoT system will also invite potential attacks, and traditional security countermeasures can be inefficient in dynamic IoT environments. So open challenges related to IoT security threats and emerging security mechanisms for security of the proposed smart railway crossing system are also elaborated.

scholarly journals Towards a climatology of fog frequency in the Atacama Desert via multi-spectral satellite data and machine learning techniques

2021 ◽  
Author(s):  
Christoph Böhm ◽  
Jan H. Schween ◽  
Mark Reyers ◽  
Benedikt Maier ◽  
Ulrich Löhnert ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Satellite Data ◽  
Atacama Desert ◽  
Arid Ecosystems ◽  
Data Set ◽  
Geological Processes ◽  
The Neural Network ◽  
Detection Approach ◽  
Fog Detection

AbstractIn many hyper-arid ecosystems, such as the Atacama Desert, fog is the most important fresh water source. To study biological and geological processes in such water-limited regions, knowledge about the spatio-temporal distribution and variability of fog presence is necessary. In this study, in-situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog data set which enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog detection approach is introduced which uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential non-linear relationships. Compared to a second fog detection approach based on MODIS cloud top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 compared to 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

2021 ◽  
pp. 1-13
Author(s):  
Qing Zhou ◽  
Xi Shi ◽  
Liang Ge
Keyword(s):  
Machine Learning ◽  
Mental Disorders ◽  
Anomaly Detection ◽  
Well Being ◽  
Machine Learning Techniques ◽  
Keyword Extraction ◽  
Learning Techniques ◽  
Detection Approach ◽  
Scaling Methods ◽  
Machine Learning Models

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

Malware Inputs Detection Approach (Tool) based on Machine Learning [MIDT-SVM]

2021 ◽  
Author(s):  
Romil Rawat ◽  
Mukesh Chouhan ◽  
Bhagwati Garg ◽  
SHRIKANT TELANG ◽  
Vinod Mahor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Approach

NETWORK ANOMALIES DETECTION APPROACH BASED ON WEIGHTED VOTING

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Computationally Efficient ◽  
Weighted Voting ◽  
Detection Systems ◽  
Learning Techniques ◽  
Detection Approach ◽  
Accuracy Increase

To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.

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