Notice of Violation of IEEE Publication Principles: A study to Understand Malware Behavior through Malware Analysis

Samantray, Om Prakash; Tripathy, Satya Narayan; Das, Susanta Kumar

doi:10.1109/icscan.2019.8878680

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…[44][45][46] Cybercrime Criminal activity either targets or uses a computer, a computer network, or a networked device. [47,48] Malware Malicious software designed to infiltrate a device without knowledge [49,50] DDoS Denial of service attack on a computer system or network that causes a service or resource tobe inaccessible to legitimate users [51][52][53][54][55] Ransomware A type of malicious program that restricts access to certain parts of files of the infected operating system and demands a ransom in exchange for removing this restriction [56][57][58][59][60] Mobile malware Its name suggests malicious software that targets explicitly the operating systems on mobile phones [61][62][63][64] Watering hole Refers to a tactic used during targeted attack campaigns where the APT is distributed through a trusted website that is usually visited by employees of the target company or entity [65,66] We contrasted this result with an international organization related to cybersecurity. We found that some of them were considered the most relevant cyberattacks in the year 2020, according to The European Union Agency for Cybersecurity (ENISA) [34].…”

Section: Type Description Referencementioning

confidence: 99%

An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

et al. 2022

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Cognitive security is the interception between cognitive science and artificial intelligence techniques used to protect institutions against cyberattacks. However, this field has not been addressed deeply in research. This study aims to define a Cognitive Cybersecurity Model by exploring fundamental concepts for applying cognitive sciences in cybersecurity. For achieving this, we developed exploratory research based on two steps: (1) a text mining process to identify main interest areas of research in the cybersecurity field and (2) a valuable review of the papers chosen in a systematic literature review that was carried out using PRISMA methodology. The model we propose tries to fill the gap in automatizing cognitive science without taking into account the users’ learning processes. Its definition is supported by the main findings of the literature review, as it leads to more in-depth future studies in this area.

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Section: Type Description Referencementioning

confidence: 99%

An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

et al. 2022

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“…The equation (5) would be used as constraints of model learning. If the dataset has n characteristic functions f i (x, y), i = 1, 2, .…”

Section: B Weight Computingmentioning

confidence: 99%

“…Recently, researchers have shown an increased interest in the detection based on more fundamental signatures, like API and opcode. Some related works focus on dynamic malware detectors based on system-calls [5]. They have analyzed existed malware and generated system call sequences or calls flow graph (CFG).…”

Section: Introductionmentioning

confidence: 99%

A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph

Yang

Chen

et al. 2020

IEEE Access

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“…In response to the low-latency, high-reliability characteristics of the industrial IoT, edge computing devices are highly susceptible to being injected with malware [13] that is highly sophisticated and resource-consuming. Thereby corrupting edge data on a massive scale, leading to DoS attacks [14].…”

Section: Introductionmentioning

confidence: 99%

New Security State Awareness Model for IoT Devices With Edge Intelligence

Lei¹,

Wen²,

Hou³

et al. 2021

IEEE Access

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Edge computing has become one of the most critical technologies in the Internet of Things (IoT), which is vulnerable to be hacked itself due to its closing to the terminals' wide variety of applications and digital assets. Aiming to find a new paradigm for cybersecurity based on edge computing at the device level, we leverage the storage, communication, and computational capabilities of edge computing devices to acquire and transform the massive amounts of data generated during work into the states. Through the analysis of the states, the security threats of the edge computing device can be identified. Based on the complex network theory, a security state awareness model is established, which can accurately indicate a device's current security state, be aware of the attack behavior, and perform protection methods.

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Notice of Violation of IEEE Publication Principles: A study to Understand Malware Behavior through Malware Analysis

Cited by 10 publications

References 10 publications

An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

An Exploratory Study of Cognitive Sciences Applied to Cybersecurity

A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph

New Security State Awareness Model for IoT Devices With Edge Intelligence

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