Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security

<p class="0abstract">Smart devices like smartphones and smartwatches have made this world smarter. These wearable devices are created through complex research methodologies to make them more usable and interactive with its user. Various interactive mobile applications such as augmented reality (AR), virtual reality (VR) or mixed reality (MR) applications solely depend on the in-built sensors of the smart devices. A lot of facilities can be taken from these devices with sensors such as accelerometer and gyroscope. Different physical activities such as walking, jogging, sitting, etc., can be important for analysis like health state prediction and duration of exercise by using those sensors based on artificial intelligence. In this paper, we have implemented machine learning and deep learning algorithms to detect and recognize eight activities namely, walking, jogging, standing, walking upstairs, walking downstairs, sitting, sitting-in-a-car and cycling; with a maximum of 99.3% accuracy. A few activities are almost similar in action, such as sitting and sitting-in-a-car, but difficult to distinguish; which makes it more challenging to predict tasks. In this paper, we have hypothesized that with more sensors (sensor fusion) and data collection points (sensor-body positions) a wide range of activities can be recognized and the recognition accuracies can be increased. Finally, we showed that the combination of all the sensors data of both pocket/waist and wrist can be used to recognize a wide range of activities accurately. The possibility of using the proposed methodologies for futuristic mobile technologies is quite significant. The adaptation of most recent deep learning algorithms such as convolutional neural network (CNN) and bi-directional Long Short Time Memory (Bi-LSTM) demonstrated high credibility of the methods presented as experimentation.<strong></strong></p>

Section: Discussionmentioning

confidence: 99%

Leveraging Sensor Fusion and Sensor-Body Position for Activity Recognition for Wearable Mobile Technologies

Алам

Das

Tasjid

et al. 2021

“…The IoT comprising WSNs (Wireless Sensor Networks) WBANs (Wireless Body Area Networks) are playing a very essential role in numerous health-related issues and healthcare atmospheres [3][4][5][6], [29][30][31]. Smart homes, automated and hyper-connected homes, intelligent household systems, and devices including light bulbs, power outlets, thermostats, and other numerous applications are connected through the Internet which allows operating remotely.…”

Section: Over Of Iot Scope and Iot Architecturementioning

confidence: 99%

“…Whereas, agricultural IoT devices include green-house sensor systems, monitoring fields, and irrigation controllers. The streetlights water distribution systems are applications for city IoT domains [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IoT-based Application of Information Security Triad

Reshan

2021

Information Security is the foremost concern for IoT (Internet of things) devices and applications. Since the advent of IoT, its applications and devices have experienced an exponential increase in numerous applications which are utilized. Nowadays we people are becoming smart because we started using smart devices like a smartwatch, smart TV, smart home appliances. These devices are part of the IoT devices. The IoT device differs widely in capacity storage, size, computational power, and supply of energy. With the rapid increase of IoT devices in different IoT fields, information security, and privacy are not addressed well. Most IoT devices having constraints in computational and operational capabilities are a threat to security and privacy, also prone to cyber-attacks. This study presents a CIA triad-based information security implementation for the four-layer architecture of the IoT devices. An overview of layer-wise threats to the IoT devices and finally suggest CIA triad-based security techniques for securing the IoT devices.

“…Machine Learning [28] is considered a useful tool to develop prediction models. Machine Learning (a branch of Artificial Intelligence) refers to learning from the previous dataset to enhance future performance automatically without any explicit programming [29]. The two major categories of machine learning algorithms are supervised and unsupervised.…”

Section: Machine Learningmentioning

confidence: 99%

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

Khan

Ahmad

Jabeur

et al. 2021

One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.