Social Engineering: The Art of Attacks

Duarte, Nelson; Coelho, Nuno; Guarda, Teresa

doi:10.1007/978-3-030-90241-4_36

Cited by 11 publications

(14 citation statements)

References 12 publications

(12 reference statements)

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“…The various methods identified throughout the articles consulted in this research, as well as others that, although they were analyzed, were not described in the article, demonstrate that machine learning can indeed be a very valid tool for identifying malware on the Android OS [12], [13].…”

Section: Discussionmentioning

confidence: 99%

Can machine learning be used to detect malware?

Lima¹

2022

ARIS2-Journal

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Nowadays everyone has one or even more than one smartphone or tablet. The existing applications with the most diverse purposes allow us to perform a series of tasks such as using home banking or checking the email, using only our smartphone/tablet. Android OS being one of the most used in this type of equipment becomes an appealing target for viruses, malware and others. At a time when technology is evolving faster and faster, both in terms of hardware and software, Artificial Intelligence has more and more weight in technological evolution, being used in the most diverse purposes. This review aims to demonstrate how Machine Learning can assist in identifying vulnerabilities in Android OS.

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Section: Discussionmentioning

confidence: 99%

Can machine learning be used to detect malware?

Lima¹

2022

ARIS2-Journal

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“…3 Human Behavior and Emerging Technologies and the actual URL in the status bar indicating that the document is a file that has to be clicked. This type of attack takes advantage of a victim's sense of curiosity using a video click as bait [14]. Once the victim clicks the video or image or post, the control of their computer is taken over by the attacker to acquire sensitive information or files.…”

Section: Methodsmentioning

confidence: 99%

“…Social engineering has emerged as a serious threat in virtual communities and is an effective way to attack information systems [13]. It is considered to be the most effective technique for attacking even the most secure system, since the weakest link of any system is the users [14]. It is one of the highest risks among other threats such as identity theft, key logger, and cyberbullying [15].…”

Section: Introductionmentioning

confidence: 99%

Are You a Soft Target for Cyber Attack? Drivers of Susceptibility to Social Engineering-Based Cyber Attack (SECA): A Case Study of Mobile Messaging Application

Wulandari

Adnan

Wicaksono

2022

Human Behavior and Emerging Technologies

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A rise in working and studying from home, activities which depend on the Internet, and the exchange of data coupled with a lack of understanding about security for interacting in cyberspace have made cybersecurity one of the most pressing concerns today. One form, in particular, is a social engineering-based cyber attack. Unfortunately, not much research has been conducted on the susceptibility factors that cause this to happen. This study attempts to understand what factors make a person susceptible to cyber attacks that can be seen from three perspectives: habitual perspective, perception perspective, and social and motivation perspective. The objective of the research is to identify specific characteristics and drivers regarding the social engineering-based cyber attack (SECA) susceptibility of a consumer exposed to social media messaging applications. A quantitative survey was employed to test a total of 114 respondents in Indonesia who are categorized as active Internet users. The study found variables within two of the three perspectives that positively contributed to a consumer’s susceptibility to cyberattack. These factors will provide valuable insight into prevention and knowledge of related risks of cyberattacks based on social engineering in the future.

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“…We selected DSR because it is a problem-solving approach with a clear objective to enhance human knowledge, develop innovative artifacts, and solve problems through them [14], [44], [45].…”

Section: Research Strategymentioning

confidence: 99%

Predicting Cybersecurity Risk - A Methodology for Assessments

Ferreira¹,

Mamede²

2022

ARIS2-Journal

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Defining an appropriate cybersecurity incident response model is a critical challenge that all companies face on a daily basis.However, there is not always an adequate answer. This is due to the lack of predictive models based on data (evidence). There is a significant investment in research to identify the main factors that can cause such incidents, always trying to have the most appropriate response and, consequently, enhancing response capacity and success. At the same time, several different methodologies assess the risk management and maturity level of organizations.There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully adopt cybersecurity and an even more significant gap in assessing it from a risk management perspective. This paper proposes a model to evaluate this capacity, a model that intends to evaluate the methodological aspects of an organization and indicates the apparent gaps that can negatively impact the future of the organization in the management of cybersecurity incidents and presents a model that intends to be proactive.

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Social Engineering: The Art of Attacks

Cited by 11 publications

References 12 publications

Can machine learning be used to detect malware?

Can machine learning be used to detect malware?

Are You a Soft Target for Cyber Attack? Drivers of Susceptibility to Social Engineering-Based Cyber Attack (SECA): A Case Study of Mobile Messaging Application

Predicting Cybersecurity Risk - A Methodology for Assessments

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