A Complete Dynamic Malware Analysis

Kaur, Navroop; Bindal, Amit Kumar

doi:10.5120/ijca2016908283

Cited by 11 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It stands as a valuable resource for anyone interested in the field of malware analysis and detection. Kaur and Bindal [30] focused on dynamic malware analysis, aimed to provide a general overview of the characteristics of recent malware and discuss the methods and resources utilized in this field, with a particular emphasis on the Cuckoo sandbox running on Windows XP (SP3). The paper began by highlighting the sheer volume of malware samples received by anti-malware companies daily, emphasizing the importance of automatically analyzing these samples.…”

Section: Chen Et Almentioning

confidence: 99%

“…Talukder [28] 2020 Tools and techniques for malware detection and analysis Presented a survey of various tools and techniques used for malware detection and analysis, including static analysis, dynamic analysis, and machine learning-based approaches. Kaur & Bindal [30] 2016 A complete dynamic malware analysis Discussed the process of dynamic malware analysis, including steps such as execution in a controlled environment, behaviour monitoring, and analysis of results. Sethi et al [8] 2018 A novel malware analysis framework for malware detection and classification using machine learning approach Developed a machine learning-based malware analysis framework for detecting and classifying malware based on features extracted from static and dynamic analysis.…”

Section: Dynamic Analysismentioning

confidence: 99%

See 1 more Smart Citation

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

Debas,

Alhumam,

Riad

2024

IJACSA

View full text Add to dashboard Cite

In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes conduct rudimentary analyses and have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.

show abstract

Section: Chen Et Almentioning

confidence: 99%

Section: Dynamic Analysismentioning

confidence: 99%

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

Debas,

Alhumam,

Riad

2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…It stands as a valuable resource for anyone interested in the field of malware analysis and detection. Kaur & Bindal [29] focused on dynamic malware analysis, aimed to provide a general overview of the characteristics of recent malware and discuss the methods and resources utilized in this field, with a particular emphasis on the Cuckoo sandbox running on Windows XP (SP3). The paper began by highlighting the sheer volume of malware samples received by anti-malware companies daily, emphasizing the importance of automatically analyzing these samples.…”

Section: Related Workmentioning

confidence: 99%

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

Debas,

Alhumam,

Riad

2023

Preprint

View full text Add to dashboard Cite

In contemporary times, the landscape of malware analysis has advanced into an era of sophisticated threat detection. Today's malware sandboxes not only conduct rudimentary analyses but have evolved to incorporate cutting-edge artificial intelligence and machine learning capabilities. These advancements empower them to discern subtle anomalies and recognize emerging threats with a heightened level of accuracy. Moreover, malware sandboxes have adeptly adapted to counteract evasion tactics, creating a more realistic and challenging environment for malicious entities attempting to detect and evade analysis. This paper delves into the maturation of malware sandbox technology, tracing its progression from basic analysis to the intricate realm of advanced threat hunting. At the core of this evolution is the instrumental role played by malware sandboxes in providing a secure and dynamic environment for the in-depth examination of malicious code, contributing significantly to the ongoing battle against evolving cyber threats. In addressing the ongoing challenges of evasive malware detection, the focus lies on advancing detection mechanisms, leveraging machine learning models, and evolving malware sandboxes to create adaptive environments. Future efforts should prioritize the creation of comprehensive datasets, distinguish between legitimate and malicious evasion techniques, enhance detection of unknown tactics, optimize execution environments, and enable adaptability to zero-day malware through efficient learning mechanisms, thereby fortifying cybersecurity defences against emerging threats.

show abstract

“…As mentioned in Section 4.3, the malicious program would present some unusual behaviors before attacks are actually generated, and it has to operate the underlying resources mapped form the seniors when they launch attacking. According to the probable relationships between senior behaviors and underlying operations of malicious program [37], VMM needs to focus on controlling the operations for kernel proper memory and monitoring the specific underlying activities of malicious program to protect the key resources of system. It mainly includes four aspects as follows.…”

Section: Resource Monitoringmentioning

confidence: 99%

A Multi-level Perception Security Model Using Virtualization

Lou¹,

Jiang²,

Chang³

et al. 2018

TIIS

View full text Add to dashboard Cite

Virtualization technology has been widely applied in the area of computer security research that provides a new method for system protection. It has been a hotspot in system security research at present. Virtualization technology brings new risk as well as progress to computer operating system (OS). A multi-level perception security model using virtualization is proposed to deal with the problems of oversimplification of risk models, unreliable assumption of secure virtual machine monitor (VMM) and insufficient integration with virtualization technology in security design. Adopting the enhanced isolation mechanism of address space, the security perception units can be protected from risk environment. Based on parallel perceiving by the secure domain possessing with the same privilege level as VMM, a mechanism is established to ensure the security of VMM. In addition, a special pathway is set up to strengthen the ability of information interaction in the light of making reverse use of the method of covert channel. The evaluation results show that the proposed model is able to obtain the valuable risk information of system while ensuring the integrity of security perception units, and it can effectively identify the abnormal state of target system without significantly increasing the extra overhead.

show abstract

A Complete Dynamic Malware Analysis

Cited by 11 publications

References 4 publications

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

Unveiling the Dynamic Landscape of Malware Sandboxing: A Comprehensive Review

A Multi-level Perception Security Model Using Virtualization

Contact Info

Product

Resources

About