malware analysis
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Author(s):  
Adhokshaj Mishra ◽  
Animesh Roy ◽  
Manjesh K. Hanawal
Keyword(s):  

2022 ◽  
pp. 102613
Author(s):  
Songsong Liu ◽  
Pengbin Feng ◽  
Shu Wang ◽  
Kun Sun ◽  
Jiahao Cao

2021 ◽  
Vol 63 ◽  
pp. 102995
Author(s):  
Durmuş Özkan Şahin ◽  
Oğuz Emre Kural ◽  
Sedat Akleylek ◽  
Erdal Kılıç

2021 ◽  
Vol 13 (6) ◽  
pp. 105-122
Author(s):  
Ioannis G. Kiachidis ◽  
Dimitrios A. Baltatzis

To fight against the evolution of malware and its development, the specific methodologies that are applied by the malware analysts are crucial. Yet, this is something often overlooked in the relevant bibliography or in the formal and informal training of the relevant professionals. There are only two generic and allencompassing structured methodologies for Malware Analysis (MA) – SAMA and MARE. The question is whether they are adequate and there is no need for another one or whether there is no such need at all. This paper will try to answer the above and it will contribute in the following ways: it will present, compare and dissect those two malware analysis methodologies, it will present their capacity for analysing modern malware by applying them on a random modern specimen and finally, it will conclude on whether there is a procedural optimization for malware analysis over the evolution of these two methodologies.


2021 ◽  
Author(s):  
Miuyin Yong Wong ◽  
Matthew Landen ◽  
Manos Antonakakis ◽  
Douglas M. Blough ◽  
Elissa M. Redmiles ◽  
...  
Keyword(s):  

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Jacob Williams ◽  
Phil Legg

AbstractMalicious software, known as malware, is a perpetual game of cat and mouse between malicious software developers and security professionals. Recent years have seen many high profile cyber attacks, including the WannaCry and NotPetya ransomware attacks that resulted in major financial damages to many businesses and institutions. Understanding the characteristics of such malware, including how malware can propagate and interact between systems and networks is key for mitigating these threats and containing the infection to avoid further damage. In this study, we present visualisation techniques for understanding the propagation characteristics in dynamic malware analysis. We propose the use of pixel-based visualisations to convey large-scale complex information about network hosts in a scalable and informative manner. We demonstrate our approach using a virtualised network environment, whereby we can deploy malware variants and observe their propagation behaviours. As a novel form of visualising system and network activity data across a complex environment, we can begin to understand visual signatures that can help analysts identify key characteristics of the malicious behaviours, and, therefore, provoke response and mitigation against such attacks.


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