Coordinator and router investigation in IEEE802.15.14 ZigBee wireless sensor network

2021

AJRCoS

Self Cite

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

Section: Fig 7 Pointwise Collective and Contextual Anomaly Typesmentioning

confidence: 99%

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Haji

2021

AJRCoS

Self Cite

“…However, the single modal model cannot easily judge the consumer's emotion ] 14 , 15 [ . With cannot decide someone is emotional simply by looking at a particular entity or occurrence in front of our eyes [16,17]. This is one reason why emotional recognition should be treated as a multimodal problem [18,19].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Emotion Recognition using Deep Learning

Abdullah

Sadeeq

et al. 2021

JASTT

167

New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

“…Deep learning approaches are applied in network intrusion detection that can uncover secret patterns and identify attacks. Furthermore, the significant point in deep learning is that it can perform feature extraction and classification tasks together [17][18][19]. The intrusion has different features and behaviors.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches for Intrusion Detection

Salih

Zeebaree

et al. 2021

AJRCoS

Self Cite

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.