Cybercrimes: A Proposed Taxonomy and Challenges

Brar, Harmandeep Singh; Kumar, Gulshan

doi:10.1155/2018/1798659

Cited by 27 publications

(41 citation statements)

References 23 publications

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“…The method involves usage of unethical techniques of persuasion so an unsuspecting user downloads malware directing them to search engine pages of fake websites. In addition, the attackers may further exploit social networking platforms using fake profiles, message links, malicious URLs and application to launch an attack [37,44,49,50].…”

Section: Fig 2: Mechanism Of Social Engineering Attacksmentioning

confidence: 99%

“…Social engineering exploits also include their ability to disguise malicious traffic payloads, making them look legitimate. With a large amount of data flowing through the networks, distinguishing between the traffic payloads becomes difficult [35,50,52].…”

Section: Network Infrastructurementioning

confidence: 99%

“…These attacks enable the social engineers to have control over other network devices in connection using switches and routers. Thus, malwares usually present a higher risk to an entire organizational information system [30,38,49,50].…”

Section: Desktopmentioning

confidence: 99%

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A Taxonomy for Social Engineering Attacks via Personal Devices

Aldawood¹,

Skinner²

2019

IJCA

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Social engineering attacks are a major threat to organizations and individuals as digitization and connectivity through the internet increase. This study aims to review scholarly research analyzing the topic of social engineering and further chart the evolution of the threat. The review identifies methods of such attacks on various platforms and devices and discusses motivations behind social engineering attacks. Finally, the paper analyzes the nature and impact of social engineering attacks and presents a taxonomy on socially engineered attacks by analyzing their anatomy.

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Section: Fig 2: Mechanism Of Social Engineering Attacksmentioning

confidence: 99%

Section: Network Infrastructurementioning

confidence: 99%

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A Taxonomy for Social Engineering Attacks via Personal Devices

Aldawood¹,

Skinner²

2019

IJCA

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“…[1]. However, the association of chief police officers of England (ACPO) and the U.S Department of Justice (DOJ) define cybercrime as any crime committed by any electronic computing devices [2][3][4]. Likewise, CERT-In and CERT-US are providing services to the users by prior notifications on the list of vulnerabilities that causes them to become victims of the cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

Computational System to Classify Cyber Crime Offenses using Machine Learning

et al. 2020

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Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

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“…The terms 'computer crime' and 'cybercrime' are intertwined; their entities are intertwined and therefore difficult to disentangle (Ibrahim, 2016). The most common targets of cyber-attackers are: entertainment, hacktivism, financial gain, spying and revenge (Singh Brar and Kumar, 2018). According to the literature analyzed the term cybercrime includes all unlawful attacks and threats to attack computers, networks and information stored in them for the purpose of causing financial and non-financial harm to persons, the economy and society.…”

Section: Introductionmentioning

confidence: 99%

A new Systemic Taxonomy of Cyber Criminal activity

Babič

Purković

2020

Politehnika (Online)

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Cybercrime commonly refers to a broad range of different criminal activities that involve computers and information systems, either as primary tools or as primary targets. Cybercrime Science combines the methodology of Crime Science with the technology of Information Security. The few existing taxonomies of Cybercrime provide only general insights into the benefits of information structures; they are neither complete nor elaborated in a systemic manner to provide a proper framework guided by real system-principles. The main problem with such taxonomies is the inability to dynamically upgrade, which is why there is no timely cybersecurity actions. The current and past approaches were based mainly on the technical nature of cyberattacks and such approaches classified the impact of the activities from a criminological perspective. In this article, we present a systemic taxonomy of Cybercrime, based on definitions of the field items and the related data specifications. We develop a new method for estimating the fractal dimension of networks to explore a new taxonomy of Cybercrime activity. This method can serve to dynamically upgrade taxonomy and thus accelerate the prevention of cybercrime.

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Cybercrimes: A Proposed Taxonomy and Challenges

Cited by 27 publications

References 23 publications

A Taxonomy for Social Engineering Attacks via Personal Devices

A Taxonomy for Social Engineering Attacks via Personal Devices

Computational System to Classify Cyber Crime Offenses using Machine Learning

A new Systemic Taxonomy of Cyber Criminal activity

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