Malware classification and composition analysis: A survey of recent developments

Abusitta, Adel; Li, Miles Q.; Fung, Benjamin C. M.

doi:10.1016/j.jisa.2021.102828

Cited by 54 publications

(37 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wireless Communications and Mobile Computing researchers in [75][76][77] are data loss, privacy breaching, account hacking, system crashing. Around 100 mailboxes of NHS workers become victims of phishing (cyber security) attack (https://duo.com/blog/how-phishing-impactshealthcare).…”

Section: Spam Emailsmentioning

confidence: 99%

Social Networking Security during COVID-19: A Systematic Literature Review

Abid

Rizwan

Veselý

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

During the Covid-19 Pandemic, the usage of social media networks increased exponentially. People engage in education, business, shopping, and other social activities (i.e., Twitter, Facebook, WhatsApp, Instagram, YouTube). As social networking expands rapidly, its positive and negative impacts affect human health. All this leads to social crimes and illegal activities like phishing, hacking, ransomware, password attacks, spyware, blackmailing, Middle-man-attack. This research extensively discusses the social networking threats, challenges, online surveys, and future effects. We conduct an online survey using the google forms platform to collect the responses of social networking sites (SNS) users within Pakistan to show how SNS affects health positively and negatively. According to the collected response, we analyzed that 50% of the users use SNS for education purposes, 17.5% use it for shopping purposes, 58.2% use it for entertainment, 37.1% use it for communication, and 9.8% use it for other purposes. According to the response, the excessive use of SNS affects the health that 9.8% users face the physical threat, 42.8% user faces mental health issues due to excessive or inappropriate use of SN, and 50.5% users feel moral threat using Social sites. Finally, we conclude our paper by discussing the open challenges, conclusions, and future directions.

show abstract

Section: Spam Emailsmentioning

confidence: 99%

Social Networking Security during COVID-19: A Systematic Literature Review

Abid

Rizwan

Veselý

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…al. [33] categorized malware detection and classification approaches based on features and algorithms used, as shown in Figure 11.…”

Section: Related Work a Malware Detection And Classificationmentioning

confidence: 99%

Self-Supervised Vision Transformers for Malware Detection

Seneviratne¹,

Shariffdeen²,

Rasnayaka³

et al. 2022

Preprint

View full text Add to dashboard Cite

Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming inevitable to find a solution that can self-learn from unlabeled sample data. This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation. Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of-the-art techniques. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of .497 and .491 respectively.

show abstract

“…In this section, the researcher discusses malware analysis and classification [11] research methodology. Performing malware analysis there are three analyzes, namely static analysis [12], dynamic analysis and hybrid analysis.…”

Section: Metodologymentioning

confidence: 99%

Android APK Identification using Non Neural Network and Neural Network Classifier

Hindarto

Santoso²

2021

J-Cosine

View full text Add to dashboard Cite

Currently adoption of mobile phones and mobile applications based on Android operating system is increasing rapidly. Many companies and emerging startups are carrying out digital transformation by using mobile applications to provide disruptive digital services to replace existing old styled services. This transformation prompted the attackers to create malicious software (malware) using sophisticate methods to target victims of Android mobile phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non Neural Network (NNN). The ANN is Multi-Layer Perceptron Classifier (MLPC), while the NNN are KNN, SVM, Decision Tree, Logistic Regression and Naïve Bayes methods. The results show that the performance using NNN has decreasing accuracy when training using larger datasets. The use of the K-Nearest Neighbor algorithm with a dataset of 600 APKs achieves an accuracy of 91.2% and dataset of 14170 APKs achieves an accuracy of 88%. The using of the Support Vector Machine algorithm with the 600 APK dataset has an accuracy of 99.1% and the 14170 APK dataset has an accuracy of 90.5%. The using of the Decision Tree algorithm with the 600 APK dataset has an accuracy of 99.2%, the 14170 APK dataset has an accuracy of 90.8%. The experiment using the Multi-Layer Perceptron Classifier has increasing with the 600 APK dataset reaching 99%, the 7000 APK dataset reaching 100% and the 14170 APK dataset reaching 100%.

show abstract

Malware classification and composition analysis: A survey of recent developments

Cited by 54 publications

References 39 publications

Social Networking Security during COVID-19: A Systematic Literature Review

Social Networking Security during COVID-19: A Systematic Literature Review

Self-Supervised Vision Transformers for Malware Detection

Android APK Identification using Non Neural Network and Neural Network Classifier

Contact Info

Product

Resources

About