Malware Detection Module using Machine Learning Algorithms to Assist in Centralized Security in Enterprise Networks

Malicious software is abundant in a world of innumerable computer users, who are constantly faced with these threats from various sources like the internet, local networks and portable drives. Malware is potentially low to high risk and can cause systems to function incorrectly, steal data and even crash. Malware may be executable or system library files in the form of viruses, worms, Trojans, all aimed at breaching the security of the system and compromising user privacy. Typically, anti-virus software is based on a signature definition system which keeps updating from the internet and thus keeping track of known viruses. While this may be sufficient for home-users, a security risk from a new virus could threaten an entire enterprise network. This paper proposes a new and more sophisticated antivirus engine that can not only scan files, but also build knowledge and detect files as potential viruses. This is done by extracting system API calls made by various normal and harmful executable, and using machine learning algorithms to classify and hence, rank files on a scale of security risk. While such a system is processor heavy, it is very effective when used centrally to protect an enterprise network which maybe more prone to such threats.Comment: 6 page

INTEGRATING SENSOR DATA AND MACHINE LEARNING FOR PREDICTIVE MAINTENANCE IN INDUSTRY 4.0

The availability of manufacturing machinery is crucial for having a productive production line. So, for industrialists, being successful in the field of maintenance is crucial if they want to make sure that key equipment is performing as it should and that unscheduled downtime is kept to a minimum. Predictive maintenance skills are viewed as being essential with the rise of complex industrial processes. The assistance that contemporary value chains may provide for a company's maintenance role is another area of focus. The development of sensors and Industry 4.0 technologies has greatly improved access to data from equipment, processes, and products. Electric motor condition monitoring and predictive maintenance help the industry avoid significant financial losses brought on by unforeseen motor breakdowns and significantly increase system dependability. This research offers Enhanced Nave Bayes Artificial Neural Network-based machine learning architecture for Predictive Maintenance. The system was tested in an industrial setting by building a data collection and analysis system using sensors, analyzing the data with a machine learning approach, and comparing the results to those generated by a simulation tool. With the help of the Azure Cloud, the Data Analysis Tool may access information collected by a wide variety of sensors, machine PLCs, and communication protocols. Preliminary results show that the method correctly predicts a wide range of machine states

Monitoring and alerting the physicians related to trauma cases using behavioural DL models

Presently Every nation places a high value on healthcare due to the rise in trauma instances. The ideal approach in this case is thought to be an Internet of Things (IoT) based health monitoring system. A novel approach in internet technology, the Internet of Things (IoT) is an increasing study area, mainly in the field of healthcare. The rising use of wearable sensors and cellphones has allowed these remote health care monitoring systems to grow at such a rapid rate. IoT health monitoring helps patients receive the appropriate care for their present level of health even when a physician is far away. The notion of the IoT and deep learning are combined in this study to present a systematic method to identify trauma instances more effectively. By utilizing some of the biological information from the patient's body, such as temperature, heart rate, and other factors, this approach presents an overview of the IoT and is also used to monitor health status and identify symptoms in the human body. For fall detection using sensor nodes, the Deep Convolutional Neural Network (DCNN) analyses individuals' motion and architecture. The suggested method may be put in place for less money but has significant potential to identify symptoms by giving patients and suspected cases the care they need and reacting to life-or-death circumstances. Metrics such as precision, recall, F-measure, and accuracy are utilized to evaluate the results when applying classification algorithms. When compared to techniques like the Decision Tree (DT), Support Vector Machine (SVM), and K Nearest Neighbour (k-NN), the findings show that it has been very successful

Contextual Proactive Suggestions for Custom Commands for Vehicle Components

Many vehicle components that are sourced from OEMs offer interactive capabilities that enable users to control them using mechanisms such as a voice-based virtual assistant via a spoken command. However, users typically lack awareness of the existence of the commands, thus failing to use them despite their availability. Mechanisms for facilitating proactive and contextually opportune discovery of interactive control options are typically limited to first party components. This disclosure describes techniques that enable OEMs to facilitate user discovery of their custom interactive capabilities by registering the commands and associated metadata with the vehicle platform. With user permission, local contextual information is analyzed to provide suggestions for OEM commands at opportune times based on triggering rules. OEMs can contribute to defining the triggering rules. The user can issue the command in any convenient manner, such as tapping or speaking which is then executed on the OEM component. The proactive and contextually relevant assistance can make the driving experience safer and more comfortable while enhancing the knowledge of vehicle capabilities and how they can be controlled through a virtual assistant