Deep Learning based Gait Analysis for Contactless Dementia Detection System from Video Camera

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

Download Full-text

Deep Learning-Based Automatic Safety Detection System for Crack Detection

2020 7th International Conference on Dependable Systems and Their Applications (DSA) ◽

10.1109/dsa51864.2020.00034 ◽

2020 ◽

Author(s):

Quan Chen ◽

Xi-Xi Zhang ◽

Yu Chen ◽

Wei Jiang ◽

Guan Gui ◽

...

Keyword(s):

Deep Learning ◽

Crack Detection ◽

Detection System

Download Full-text

An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽

10.3390/e23030344 ◽

2021 ◽

Vol 23 (3) ◽

pp. 344

Author(s):

Jeyaprakash Hemalatha ◽

S. Abijah Roseline ◽

Subbiah Geetha ◽

Seifedine Kadry ◽

Robertas Damaševičius

Keyword(s):

Deep Learning ◽

Detection System ◽

Malware Detection ◽

Class Imbalance ◽

Learning Model ◽

Computational Time ◽

Learning Approaches ◽

Security Threat ◽

Static And Dynamic Analysis ◽

Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

Download Full-text

IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽

10.3390/electronics10161876 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1876

Author(s):

Ioana Apostol ◽

Marius Preda ◽

Constantin Nila ◽

Ion Bica

Keyword(s):

Deep Learning ◽

Detection System ◽

False Positive Rate ◽

Empirical Evaluation ◽

Learning Approaches ◽

Promising Alternative ◽

Positive Rate ◽

Unsupervised Deep Learning ◽

Attack Surface ◽

Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

Download Full-text