Real time Detection of Spectre and Meltdown Attacks Using Machine Learning

Ahmad, Bilal

doi:10.48550/arxiv.2006.01442

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presence of machine learning and deep learning based detection systems is vital in this regard as it has the potential of timely analysis and recognition of meltdown behavior that assists the parents or caretakers in perceiving the situation and hence handling it efficiently before it gets more extreme [10]. Hence, the implications of early diagnosing drew the consideration of researchers to use different machine learning-based techniques [11]. Multiple analyses are done using deep learning (DL) Algorithms for Human Activity Recognition (HAR) but till date HAR dataset has not been applied to foresee meltdowns in children affected by ASD and ADHD.…”

Section: Introductionmentioning

confidence: 99%

Predicting meltdown situation in Autism and ADHD in real-time through camera using deep learning algorithm.

Sumbul Alam

2024

jes

View full text Add to dashboard Cite

Neurodevelopmental issues such as Autism spectrum disorder (ASD) and Attention deficit hyperactive disorders (ADHD) are quite prevalent in small children detecting and differentiating them at a very early stage is necessary for the future of the affected children and their parents or care giver. This may require 24 hours of surveillance in level 3 cases in which the affected may experience meltdown situation. It is well known by clinical psychologist that sudden meltdowns are common in autistic children, which makes the situation difficult for the parents or care givers and is also a physical threat to the affected children and people around them as they most likely injure themselves. Research has shown that children diagnosed with autism spectrum disorder display specific behaviors that allow us to predict their violent outbursts. Our aim is to develop a CNN-based system that can identify these kinds of behaviors using real time camera. In our study, we are trying to make a model that can perform Human Activity Recognition (HAR) in real time. Based on the available training data we have trained our model on a few common pre meltdown actions or gestures creating two classes of dataset. but in future we may take huge number of video frame of different types of gestures (using HMBD51 datasets) to train the algorithm so that it can practically identify the situation in real time and alarm the caregiver before they enter the meltdown situation, this will save the patients from self-inflicted injuries and panic attacks not just in the above mentioned two cases but many other brain disorders. The present model has achieved a training accuracy of 100% ,a satisfactory FPS(Frame processed per Second) and the validation accuracy is slightly increasing in each epoch.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting meltdown situation in Autism and ADHD in real-time through camera using deep learning algorithm.

Sumbul Alam

2024

jes

View full text Add to dashboard Cite

show abstract

Secure cloud-based mobile apps: attack taxonomy, requirements, mechanisms, tests and automation

Chimuco

Sequeiros

Lopes

et al. 2023

Int. J. Inf. Secur.

View full text Add to dashboard Cite

The adoption and popularization of mobile devices, such as smartphones and tablets, accentuated after the second decade of this century, has been motivated by the growing number of mobile applications, which can solve problems in different areas of contemporary societies. Conversely, the software development industry is motivated by the increasing number and quality of resources that mobile devices possess nowadays (e.g., memory, sensors, processing power or battery). While powerful mobile devices do exist, one of the main driving factors behind the increase of resources is the usage of Cloud technology, which strongly complement mobile computing. As expected, the adoption of measures to mitigate security issues has not accompanied the growth and speed of development for Cloud and Mobile software, to ensure that these are resilient to attacks by design. Aiming to contribute to decrease the gap between software and security engineering, this paper presents a deep approach to attack taxonomy, security mechanisms, and security test specification for the Cloud and Mobile ecosystem of applications. This is also the first time an encompassing and conjoined approach is provided for attack taxonomy and specification of security tests automation tools for this ecosystem.

show abstract

Real time Detection of Spectre and Meltdown Attacks Using Machine Learning

Cited by 2 publications

References 0 publications

Predicting meltdown situation in Autism and ADHD in real-time through camera using deep learning algorithm.

Predicting meltdown situation in Autism and ADHD in real-time through camera using deep learning algorithm.

Secure cloud-based mobile apps: attack taxonomy, requirements, mechanisms, tests and automation

Contact Info

Product

Resources

About