Master's In Cyber Security Colleges

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Master's In Cyber Security Colleges

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The Assessment Of Smart City Information Security Risk In China Based On Zgt2fss And Iaa Method

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Editor’s Choice articles are based on the recommendations of scientific journal editors from around the world. The editors select a small number of recently published articles in the journal that they believe are of interest to readers or are important in the relevant research area. The aim is to provide a snapshot of some of the most exciting developments published in the various research areas of the journal.

By Kamran Shaukat 1, 2, *, Sohwai Lu 1, Vijay Varadarajan 1, Ibrahim A. Hamid 3, *, Shan Chen 1, Dongshi Liu 4, and Jiaming Li 4

Operational Planning Steps In Smart Electric Power Delivery System

Received: 3 April 2020 / Revised: 24 April 2020 / Accepted: 11 May 2020 / Published: 15 May 2020

Virtual space has become a major factor in all areas of the modern world. The world is becoming more and more dependent on the Internet for everyday life. Increased reliance on the Internet has also increased the risks of malicious threats. Due to the growing cyber security risks, cyber security has become the most central element in the cyber world to fight against all cyber threats, attacks and frauds. The ever-expanding cyberspace is highly vulnerable to the ever-increasing possibility of attack from endless cyber threats. The purpose of this study is to provide a brief overview of various Machine Learning (ML) techniques to keep up with all the advances made in methods for detecting potential cyber security threats. These cybersecurity risk detection methods mainly include fraud detection, intrusion detection, spam detection, and malware detection. In this review article, we present, based on the existing literature, the applications of ML models in cybersecurity and a comprehensive overview of ML techniques in cybersecurity. To our knowledge, we have made the first attempt to compare the time complexity of general ML models in cybersecurity. We comprehensively compared the performance of each classifier on widely used datasets and cyber threat subdomains. This work also provides a brief introduction to machine learning models along with common security datasets. Although a top priority, cyber security has limitations and challenges. This work also explains the enormous current challenges and limitations facing the application of machine learning techniques in cybersecurity.

In this era, virtual space is growing faster as the main source of node-to-node information transfer with all its charms and challenges. Cyberspace serves as an important source of access to an unlimited amount of information and resources worldwide. In 2017, the global internet usage rate was 48%, which later increased to 81% for developing countries [1]. The broad spectrum of cyberspace includes the Internet, users, system resources, technical skills of participants, and many other things, not just the Internet. The cyber world also plays an important role in creating unlimited vulnerabilities against cyber threats and attacks. Cyber ​​security is a collection of various techniques, devices and methods that are used to protect cyber space against cyber attacks and cyber threats [2]. In today’s world of computer and information technology, cybercrime is growing at a faster rate than the current cyber security system. Poor system configuration, unskilled personnel and few techniques are some of the factors that make a computer system vulnerable to threats [3]. Due to the increase in cyber threats, more progress must be made in the development of cyber security methods. The old and conventional cyber security methods have a significant disadvantage because these methods are ineffective against unknown and multifaceted security attacks. There is a need for strong and advanced security methods that can learn from their experience and identify previously unknown and new attacks. Cyber ​​threats are growing significantly. Dealing with the speed of security threats and providing the necessary solutions to prevent them is very challenging [4].

Machine Learning: One of the main modern methods of cybercrime detection is machine learning techniques. Machine learning techniques can be applied to overcome the limitations and limitations faced by conventional detection methods [5]. The researchers addressed the advances, limitations, and limitations of using machine learning techniques to detect cyberattacks and provided a comparison of conventional methods with machine learning techniques. Machine learning is a branch of artificial intelligence. ML techniques are built with the ability to learn from experience and data without explicit programming [6]. The application of ML techniques in various fields of life such as education [7, 8], medicine [9, 10, 11], business and cyber security [12, 13, 14] is expanding. Machine learning techniques play their role on both sides of the network ie. on the side of the attacker and the side of the defender. On the offensive side, ML techniques are used to break through the defensive wall. In contrast, on the defense side, machine learning techniques are used to create fast and robust defense strategies.

Top 100 Cybersecurity Universities

Cyber ​​threats: Machine learning techniques play a vital role in combating cyber security threats and attacks such as intrusion detection system [15, 16], malware detection [17], phishing detection [18, 19], spam detection [20, 21]. do , and fraud detection [22] to name a few. For this review, we will focus on malware detection, intrusion detection systems, and spam classification. Malware is a set of instructions maliciously designed to disrupt the normal flow of computer activities. Malicious codes are executed on a target machine in order to damage and compromise the integrity, confidentiality and availability of computer resources and services [23]. Saad et al. in [24] discuss the main fundamental issues in using machine learning techniques for malware detection. Saad et al., claim that machine learning techniques have the ability to detect new and polymorphic attacks. Machine learning techniques will lead to other conventional detection methods in the future. Training methods for malware detection must be cost-effective. Malware analysts must also be able to understand ML malware detection methods at an expert level. Ambalavanan et al in [25] describe some strategies for effective detection of cyber threats. One of the critical weaknesses of the security system is that the security reliability level of the computing resources is usually determined by the average user who has no technical knowledge of security.

Another threat to computer resources is spam. Spam messages are unwanted and solicited messages that consume a lot of network resources along with memory and computer speed. ML techniques are used to identify and classify a message as spam or spam. Machine learning techniques have contributed significantly to the detection of spam messages on computers [26, 27], SMS messages on mobile phones [28], spam tweets [29] or image/video [30, 31].

An intrusion detection system (IDS) is a system for protecting computer networks against any malicious intrusion by scanning for network vulnerabilities. Signature-based, anomaly-based and hybrid-based are the main classifications of intrusion detection system for network analysis. ML techniques have made significant contributions in identifying various types of network and host computer intrusions. However, there are several areas such as the detection of new and zero-day attacks that are considered significant challenges for machine learning techniques [32].

Threats to trust: For this review, we included studies that (1) address each of the six machine learning models in cybersecurity, (2) target cyberthreats, including intrusion detection, spam detection, and discussion of malware detection , and (3) performance evaluation in terms of accuracy, recall, or precision. We used multiple combinations of disciplines such as Machine Learning and Cybersecurity and Machine Learning and Cybersecurity to retrieve peer-reviewed journal articles, conference proceedings, book chapters, and reports. We targeted six databases, namely Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. Google Scholar is also used for forward and backward searches. We focus on recent developments over the last ten years. A total of 2,852 documents

Cybersecurity In The Cloud

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