A Review on Cybersecurity based on Machine Learning and Deep Learning Algorithms

Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL plays a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, main principle of information security and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms

Security Enhancement of IoT and Fog Computing Via Blockchain Applications

Blockchain technology is now becoming highly appealing to the next generation because it is better tailored to the information age. Blockchain technologies can also be used in the Internet of Things (IoT) and fog computing. The development of IoT and Fog Computing technologies in different fields has resulted in a major improvement in distributed networks. Blockchain technology is now becoming highly appealing to the next generation because it is better tailored to the information age. Blockchain technologies can also be used in IoT and fog computing.  The blockchain principle necessitates a transparent data storage mechanism for storing and exchanging data and transactions throughout the network. In this paper, first, we explained Blockchain, its architecture, and its security. Then we view Blockchain application in IoT security. Then we explained Fog computing, Generic Security Requirements for Fog Computing, and we also discuss Blockchain applications that enhance Fog Computing Security. Finally, we conduct a review of some recent literature on using Blockchain applications to improve the security of IoT and fog computing and a comparison of the methods proposed in the literature

Adaptive Discrete Filters for Telephone Channels Based on the Wavelet Packet Transform

The wavelet transform provides good and in many times excellent results when used as a basic block transform in many systems such as electronic, communication, medical and even chemical systems. The paper uses the wavelet packet transform to adjust the tap gains of the adaptive filter used in channel equalization and estimation. The results using the wavelet technique achieve good improvements in convergence time over the ordinary LMS algorithm. The two systems were compared on full mathematical and simulation basis. Learning curves for adaptive channel equalization and adaptive channel estimation using wavelet packet transform with different mother functions, different level decompositions, different step sizes, different levels of signal to noise ratio, different telephone channels and different filter sizes were compared with conventional LMS adaptive channel equalization and channel estimation. The simulation results carried out using the MATLAB package version 6.1, demonstrate the efficiency of the proposed technique

Design and Hardware Implementation of a Speech Cipher System

Digital ciphering of speech signals based on one of modern cryptography algorithms, called the Rijndael algorithm, is studied and presented in this paper. The algorithm meets most of the requirements of security level in recent applications. A system to encrypt speech files recorded with Sound Blaster Card of a personal computer was proposed and simulated successfully using MATLAB® language.  Subjective measure and objective measure using segmental spectral signal-to-noise ratio, were used to test the proposed system performance. In these tests residual intelligibility of the encrypted speech and quality of the recovered speech were calculated and assessed. Finally, a hardware implementation of the above cipher system has been proposed using the TMS320-C30. The real time requirements from the speech cipher system have been computed in terms of execution time together with factors affecting such implementation. The results show the capability of the cipher system to be implemented using the DSP device suggested. Furthermore, the results of hardware implementation also show the security of the system is very close to that of the simulated version

Smart Healthcare for ECG Telemonitoring System

Cardiovascular disorders are one of the major causes of sudden death among older and middle-aged people. Over the past two decades, health monitoring services have evolved quickly and had the ability to change the way health care is currently provided. However, the most challenging aspect of the mobile and wearable sensor-based human activity recognition pipeline is the extraction of the related features. Feature extraction decreases both computational complexity and time. Deep learning techniques are used for automatic feature learning in a variety of fields, including health, image classification, and, most recently, for the extraction and classification of simple and complex human activity recognition in smart health care. This paper presents a review on a recent state of the art in the area of electrocardiogram (ECG) smart health monitoring systems based on the Internet of things with the machine and deep learning techniques. Moreover, the paper provided possible lines of research and challenges that can help researchers in advancing the state of art in future work

The adaptive adjustment of digital data receivers using pre-detection filter

This thesis is concerned with the adaptive adjustment of digital data receivers employed m synchronous serial data transmission systems that use quadrature amplitude modulation The receiver employs a pre-detection filter that forms the first part of a decision feedback equalizer or else is used ahead of a near maximum-likelihood detector. The filter is ideally adjusted such that the sampled impulse response of the channel and filter is minimum phase. [Continues.

Firewall and VPN Investigation on Cloud Computing Performance

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

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

Smart Android Graphical Password Strategy: A Review

In recent years, when users across the world have embraced smart devices in greater numbers owing to recent advances and appealing applications, they have also become a target for criminals who are zealously attempting to breach protection. As a result, a significant number of attacks have been observed on these systems. As a result, several password-based authentication mechanisms have been proposed to counteract these attacks. Among them, the graphical password scheme is more consistent with smart devices, which are highly graphic-oriented. However, current graphical password schemes are vulnerable to a variety of assaults, including shoulder surfing, smudging, intersection attacks, and reflection attacks. Thus, the paper aims to review recent published papers on android smart phone graphical password and identify used techniques. Moreover, the paper analyzes results to give better understanding for users of such devices to protect their devices from unauthorized access and attacks

Image Authentication Based on Watermarking Approach: Review

Digital image authentication techniques have recently gained a lot of attention due to their importance to a large number of military and medical applications, banks, and institutions, which require a high level of security. Generally, digital images are transmitted over insecure media, such as the Internet and computer networks of various kinds. The Internet has become one of the basic pillars of life and a solution to many of the problems left by the coronavirus. As a result, images must be protected from attempts to alter their content that might affect important decision-making. An image authentication (IA) system is a solution to this difficult problem. In the previous literature, several methods have been proposed to protect the authenticity of an image. Digital image watermark is a strategy to ensure the reliability, resilience, intellectual property, and validity of multimedia documents. Digital media, such as images, audio, and video, can hide content. Watermarking of a digital image is a mechanism by which the watermark is embedded in multimedia and the image of the watermark is retrieved or identified in a multimedia entity. This paper reviews IA techniques, watermark embedding techniques, tamper detection methods and discusses the performance of the techniques, the pros and cons of each technique, and the proposed methods for improving the performance of watermark techniques