Understanding Cybercrime from Its Stakeholders' Perspectives: Part 1--Attackers

Arief, Budi; Adzmi, Mohd Azeem Bin; Groß, Thomas

doi:10.1109/msp.2015.19

Cited by 23 publications

(9 citation statements)

References 5 publications

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“…According to the Ponemon Institute, more than 25% and 28% of security breaches in the world, respectively, are caused by misuse of resources by employees and by system glitches inside the organization. 37 At least 53% of global security breaches last year were found within organizations. 37 The Man-in-the-Middle (MitM) attack is the best-known example of misuse of resources attacks.…”

Section: Cyber-attacksmentioning

confidence: 99%

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Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

126

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Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minute millions of data bytes are being generated, processed, exchanged, shared, and utilized to yield outcomes in specific applications. Thus, securing the data, machines (devices), and user’s privacy in cyberspace has become an utmost concern for individuals, business organizations, and national governments. In recent years, machine learning (ML) has been widely employed in cybersecurity, for example, intrusion or malware detection and biometric-based user authentication. However, ML algorithms are vulnerable to attacks both in the training and testing phases, which usually leads to remarkable performance decreases and security breaches. Comparatively, limited studies have been conducted to understand the essence and degree of the vulnerabilities of ML techniques against security threats and their defensive mechanisms. It is imperative to systematize recent works related to cybersecurity using ML to seek the attention of researchers, scientists, and engineers. Therefore, in this paper, we provide a comprehensive survey of the works that have been carried out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.

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Section: Cyber-attacksmentioning

confidence: 99%

“…37 At least 53% of global security breaches last year were found within organizations. 37 The Man-in-the-Middle (MitM) attack is the best-known example of misuse of resources attacks.…”

Section: Cyber-attacksmentioning

confidence: 99%

Machine learning in cybersecurity: a comprehensive survey

Dasgupta

Akhtar

Sen

2020

Journal of Defense Modeling & Simulation

126

View full text Add to dashboard Cite

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“…4 However, cybercrime is much more complex than this deinition suggests. For one thing, the accuracy of reported losses due to cybercrime is oten arguable.…”

Section: Cybercrime Stakeholdersmentioning

confidence: 99%

Understanding Cybercrime from Its Stakeholders' Perspectives: Part 2--Defenders and Victims

Arief

Adzmi

2015

IEEE Secur. Privacy

Self Cite

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“…Information encryption is an important research hotspot in information security today. According to incomplete statistics, cybercrime has caused approximately US $400 billion in economic losses worldwide [ 1 , 2 ]. With the rapid developments of the Internet and artificial intelligence, the field of information security has received more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Optical Encryption Based on Computer Generated Holograms in Photopolymer

Wang

et al. 2021

Polymers

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An optical encryption method based on computer generated holograms printing of photopolymer is presented. Fraunhofer diffraction is performed based on the Gerchberg-Saxton algorithm, and a hologram of the Advanced Encryption Standard encrypted Quick Response code is generated to record the ciphertext. The holograms of the key and the three-dimensional image are generated by the angular spectrum diffraction algorithm. The experimental results show that large-size encrypted Quick Response (QR) code and miniature keys can be printed in photopolymers, which has good application prospects in optical encryption. This method has the advantages of high-density storage, high speed, large fault tolerance, and anti-peeping.

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Understanding Cybercrime from Its Stakeholders' Perspectives: Part 1--Attackers

Cited by 23 publications

References 5 publications

Machine learning in cybersecurity: a comprehensive survey

Machine learning in cybersecurity: a comprehensive survey

Understanding Cybercrime from Its Stakeholders' Perspectives: Part 2--Defenders and Victims

Optical Encryption Based on Computer Generated Holograms in Photopolymer

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