Malware is a nuisance for smartphone users. The impact is detrimental to smartphone users if the smartphone is infected by malware. Malware identification is not an easy process for ordinary users due to its deeply concealed dangers in application package kit (APK) files available in the Android Play Store. In this paper, the challenges of creating malware datasets are discussed. Long before a malware classification process and model can be built, the need for datasets with representative features for most types of malwares has to be addressed systematically. Only after a quality data set is available can a quality classification model be obtained using machine learning (ML) or deep learning (DL) algorithms. The entire malware classification process is a full pipeline process and sub processes. The authors purposefully focus on the process of building quality malware datasets, not on ML itself, because implementing ML requires another effort after the reliable dataset is fully built. The overall step in creating the malware dataset starts with the extraction of the Android Manifest from the APK file set and ends with the labeling method for all the extracted APK files. The key contribution of this paper is on how to generate datasets systematically from any APK file.

Android-manifest extraction and labeling method for malware compilation and dataset creation

Toward Enhancing Malware Detection Using Practical Swarm Optimization in Honeypot

Recently, electronics devices, cognitive computing, and sensing enable the deployment of internet-of-things (IoTs) with a huge application domain. However, resource constraints such as low computing powers or limited storage leave IoTs infrastructures vulnerable to a variety of cyber-attacks. In dark-net the address space developed as designated unrestricted internet address space anticipated to be used by trustworthy hosts anywhere in the world, therefore, any communication activity is presumed to be unwanted and particularly treated as a probe, backscatter, or miss-configuration. This chapter investigates and evaluates the operation of dark-net traffic detection systems in IoTs networks. Moreover, the most recent work done to ensure security in the IoTs network has been discussed. In particular, the areas of privacy provisioning, lightweight cryptographic framework, secure routing, robustness, and DoS attacks have been addressed. Moreover, based on the analysis of existing state-of-the-art protocols, the security requirements and challenges are highlighted along with identified open issues. 

Security in Internet of Things

Retracted: Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code

With the rapid development of big data technology, the issue of user privacy security on the Android platform is becoming increasingly prominent. This paper aims to conduct an in-depth analysis of the privacy permissions of Android users under the big data environment and explore effective protection mechanisms. Through research on permission management, application behavior, and user privacy leakage pathways in the Android system, this paper proposes a comprehensive privacy protection strategy to enhance the privacy security level of Android users in the big data environment.

Research on android user privacy permission analysis and protection mechanism under big data environment

The risk of malware has increased drastically in recent years due to advances in the IT industry but it also increased the need for malware analysis and prevention. Hackers inject malicious code using awful applications. In this research, a framework is proposed to identify malicious Android applications based on repacked malicious code. The sensitive features of android applications are extracted using source code. These extracted features are compared with existing malware signatures to identify repacked malicious android applications. Experiments are performed using 3490 android-based malware samples belonging to 21 different malware families. A threshold value for malware categorization is defined using fuzzy logic. If the fuzzy comparison match is greater than 40%, the application is malicious. Meanwhile, if the match is greater than 10% and less than 40%, the application is suspicious otherwise benign. Furthermore, the proposed framework presents around 74% of the repacked malware compared to other similar approaches.

Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code

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Security Hardened and Privacy Preserved Android Malware Detection Using Fuzzy Hash of Reverse Engineered Source Code