scholarly journals Cyber Security Audit and Attack Detection Toolkit

2012 ◽  
Author(s):  
Dale Peterson
Keyword(s):  
Cyber Security ◽  
Attack Detection ◽  
Security Audit

Cyber Security of Water SCADA Systems—Part II: Attack Detection Using Enhanced Hydrodynamic Models

2013 ◽  
Vol 21 (5) ◽  
pp. 1679-1693 ◽  
Author(s):  
Saurabh Amin ◽  
Xavier Litrico ◽  
S. Shankar Sastry ◽  
Alexandre M. Bayen
Keyword(s):  
Cyber Security ◽  
Attack Detection ◽  
Hydrodynamic Models ◽  
Scada Systems

Internal auditing and cyber security: audit role and procedural contribution

2021 ◽  
Vol 13 (1) ◽  
pp. 25
Author(s):  
Petros Lois ◽  
George Drogalas ◽  
Alkiviadis Karagiorgos ◽  
Alkis Thrassou ◽  
Demetris Vrontis
Keyword(s):  
Cyber Security ◽  
Internal Auditing ◽  
Security Audit

Effects of cyber security knowledge on attack detection

2015 ◽  
Vol 48 ◽  
pp. 51-61 ◽  
Author(s):  
Noam Ben-Asher ◽  
Cleotilde Gonzalez
Keyword(s):  
Cyber Security ◽  
Attack Detection

scholarly journals GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Drones ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Elena Basan ◽  
Alexandr Basan ◽  
Alexey Nekrasov ◽  
Colin Fidge ◽  
Nikita Sushkin ◽  
...  
Keyword(s):  
Cyber Security ◽  
Detection System ◽  
Attack Detection ◽  
Abnormal Behavior ◽  
Mathematical Methods ◽  
Training Time ◽  
Detection Technology ◽  
Normal Behavior ◽  
Leibler Divergence ◽  
Aerial Vehicle

Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2021 ◽  
pp. 1658-1671
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2020 ◽  
pp. 283-299 ◽  
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

scholarly journals A Survey of Cyber Security Approaches for Attack Detection, Prediction, and Prevention

2018 ◽  
Vol 12 (4) ◽  
pp. 15-28 ◽  
Author(s):  
Ayei E. Ibor ◽  
Florence A. Oladeji ◽  
Olusoji B. Okunoye
Keyword(s):  
Cyber Security ◽  
Attack Detection ◽  
Prediction And Prevention

scholarly journals A comparative analysis of metaheuristic algorithms in fuzzy modelling for phishing attack detection

2021 ◽  
Vol 23 (2) ◽  
pp. 1146
Author(s):  
Noor Syahirah Nordin ◽  
Mohd Arfian Ismail ◽  
Tole Sutikno ◽  
Shahreen Kasim ◽  
Rohayanti Hassan ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Cyber Security ◽  
Fuzzy System ◽  
Optimal Solution ◽  
Optimization Method ◽  
Complex Problem ◽  
Attack Detection ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Fuzzy Modelling

<div>Phishing attack is a well-known cyber security attack that happens to many people around the world. The increasing and never-ending case of phishing attack has led to more automated approaches in detecting phishing attack. One of the methods is applying fuzzy system. Fuzzy system is a rule-based system that utilize fuzzy sets and fuzzy logic concept to solve problems. However, it is hard to achieve optimal solution when applied to complex problem where the process of identify the fuzzy parameter becomes more complicated. To cater this issue, an optimization method is needed to identify the parameter of fuzzy automatically. The optimization method derives from the metaheuristic algorithm. Therefore, the aim of this study is to make a comparative analysis between the metaheuristic algorithms in fuzzy modelling. The study was conducted to analyse which algorithm performed better when applied in two datasets: website phishing dataset (WPD) and phishing websites dataset (PWD). Then the results were obtained to show the performance of every metaheuristic algorithm in terms of convergence speed and four metrics including accuracy, recall, precision, and f-measure. </div>

Threat Detection in Cyber Security Using Data Mining and Machine Learning Techniques

2020 ◽  
pp. 234-253
Author(s):  
Daniel Kobla Gasu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Threat Detection ◽  
Use Of The Internet ◽  
Using Data

The internet has become an indispensable resource for exchanging information among users, devices, and organizations. However, the use of the internet also exposes these entities to myriad cyber-attacks that may result in devastating outcomes if appropriate measures are not implemented to mitigate the risks. Currently, intrusion detection and threat detection schemes still face a number of challenges including low detection rates, high rates of false alarms, adversarial resilience, and big data issues. This chapter describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection and cyber-attack detection. Key literature on ML and DM methods for intrusion detection is described. ML and DM methods and approaches such as support vector machine, random forest, and artificial neural networks, among others, with their variations, are surveyed, compared, and contrasted. Selected papers were indexed, read, and summarized in a tabular format.

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