Detecting Colluding Inter-App Communication in Mobile Environment

Casolare, Rosangela; Martinelli, Fabio; Mercaldo, Francesco; Santone, Antonella

doi:10.3390/app10238351

Cited by 5 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, Alde analyses gives insight into what private information can be leaked by apps that use the same analytics library. Casolare et al [34] also focused on the Android environment by proposing a model checking-based approach for detecting colluding between Android applications. A comparison of existing techniques is given in Table 1.…”

Section: Malware Detection Using Other Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms

Mathew¹,

Jyothish²,

Anandaram³

et al. 2021

Electronics

Self Cite

View full text Add to dashboard Cite

Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

show abstract

Section: Malware Detection Using Other Techniquesmentioning

confidence: 99%

“…R. Casolare et al [34] Static approach, based on formal methods, which exploit the model checking technique. Limitations of method is that the generation of the first heuristic is not automatic.…”

Section: Malware Detection Using Other Techniquesmentioning

confidence: 99%