Security importance assessment for system objects and malware detection

Mao, Weixuan; Cai, Zhongmin; Towsley, Don; Qian, Feng; Guan, Xiaohong

doi:10.1016/j.cose.2017.02.009

Cited by 30 publications

(9 citation statements)

References 42 publications

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“…Authors in [13] presented a compression-based feature mining on system/API calls' quantitative information flow graphs to detect Windows malware. Mao et al [14] designed a security dependence network from access behaviors to evaluate importance of system resources (e.g., files, registry, and processes) and malware detection. While, Egele et al [15] presented a dynamic blanket execution function that employs high-level API-relevant semantic features.…”

Section: ) Static Analysismentioning

confidence: 99%

Malware Detection and Analysis: Challenges and Research Opportunities

Akhtar¹

2021

Preprint

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Malwares are continuously growing in sophistication and numbers. Over the last decade, remarkable progress has been achieved in anti-malware mechanisms. However, several pressing issues (e.g., unknown malware samples detection) still need to be addressed adequately. This article first presents a concise overview of malware along with anti-malware and then summarizes various research challenges. This is a theoretical and perspective article that is hoped to complement earlier articles and works.

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Section: ) Static Analysismentioning

confidence: 99%

Malware Detection and Analysis: Challenges and Research Opportunities

Akhtar¹

2021

Preprint

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“…Mao et al [16] proposed a system for building security dependency to measure the significance of security of a system from a wide perspective of the system. e effect of small-world and power-law distribution for the degree for in-and out-degree in security dependency network was observed.…”

Section: Related Workmentioning

confidence: 99%

Multicriteria Decision and Machine Learning Algorithms for Component Security Evaluation: Library-Based Overview

Zhang¹,

Nazir

Huang³

et al. 2020

Security and Communication Networks

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Components are the significant part of a system which plays an important role in the functionality of the system. Components are the reusable part of a system which are already tested, debugged, and experienced based on the previous practices. A new system is developed based on the reusable components, as reusability of components is recommended to save time, effort, and resources as such components are already made. Security of components is a significant constituent of the system to maintain the existence of the component as well as the system to function smoothly. Component security can protect a component from illegal access and changing its contents. Considering the developments in information security, protecting the components becomes a fundamental issue. In order to tackle such issues, a comprehensive study report is needed which can help practitioners to protect their system. The current study is an endeavor to report some of the existing studies regarding component security evaluation based on multicriteria decision and machine learning algorithms in the popular searching libraries.

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“…e evaluation models from 2000 to 2018 have been reviewed for e-commerce [21]. Mao et al [22] proposed a system for building security dependency to measure the significance of security of system from a wide perspective of the system. e effect of small-world and power-law distribution for the degree of in-and out-degree in security dependency network was observed.…”

Section: Related Workmentioning

confidence: 99%

Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

Gu¹,

Nazir

Hong³

et al. 2020

Security and Communication Networks

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With the development of communication systems, information securities remain one of the main concerns for the last few years. The smart devices are connected to communicate, process, compute, and monitor diverse real-time scenarios. Intruders are trying to attack the network and capture the organization’s important information for its own benefits. Intrusion detection is a way of identifying security violations and examining unwanted occurrences in a computer network. Building an accurate and effective identification system for intrusion detection or malicious activities can secure the existing system for smooth and secure end-to-end communication. In the proposed research work, a deep learning-based approach is followed for the accurate intrusion detection purposes to ensure the high security of the network. A convolution neural network based approach is followed for the feature classification and malicious data identification purposes. In the end, comparative results are generated after evaluating the performance of the proposed algorithm to other rival algorithms in the proposed field. These comparative algorithms were FGSM, JSMA, C&W, and ENM. After evaluating the performance of these algorithms and the proposed algorithm based on different threshold values ranging, Lp norms, and different parametric values for c, it was concluded that the proposed algorithm outperforms with small Lp values and high Kitsune scores. These results reflect that the proposed research is promising toward the identification of attack on data packets, and it also reflects the applicability of the proposed algorithms in the network security field.

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Security importance assessment for system objects and malware detection

Cited by 30 publications

References 42 publications

Malware Detection and Analysis: Challenges and Research Opportunities

Malware Detection and Analysis: Challenges and Research Opportunities

Multicriteria Decision and Machine Learning Algorithms for Component Security Evaluation: Library-Based Overview

Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

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