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Prof
Terzoli, Alfredo
Research Interest(s): Computer networking, Computer engineering, Software development, Information and communication technology for development.
Biography: To be added
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- Detecting mobile Android malware is still a challenge, despite the numerous research efforts. This paper presents a static detection approach that employs music information retrieval techniques. Detection based on a single, or a few, acoustic features suffer from reduction in classification accuracy, due to the use of limited ‘views’. In this paper, we propose a multi-audio feature-fusion approach, which merges audio features of heterogeneous views in order to detect Android malware. Sixty-three standard audio signal processing features and thirty-nine biologically inspired audio features were extracted, after converting the Android application package files into waveform audio files. The biologically inspired features were derived from Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), and Bark Frequency Cepstral Coefficients (BFCC). Experimental results show that the proposed audio-based malware detection features are effective and need to be further studied. Using the traditional eXtreme Gradient Boosting machine learning algorithm on the CICMaldroid 2020 dataset, the proposed approach achieved accuracy, recall, f1-score, and AUC scores of 98.96%, 99.65%, 99.30% and 98.14% respectively. An average-precision (AP) score of 100% was also achieved.
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- Android has grown to become the leading mobile operating system on the market due, in part, to its popularity and open-source nature. On the other hand, the Android ecosystem has become a fertile ground for malware, posing substantial security risks to ordinary mobile users. This is hardly surprising given the sheer number of devices it controls. Despite several research efforts over the years, Android malware detection remains a challenge. This is largely due to the fact that it is still unknown which features or combination of features can effectively distinguish malicious Android applications from benign ones. To this end, this research explores an Android malware detection system that uses the low-level audio Normalized Gammachirp Cepstral Coefficients as features to classify malware with machine learning techniques. First, we convert the Android Application Package datasets into audio datasets, then extract the audio features. To evaluate our approach, twenty-four machine learning algorithms were implemented, and results were collected. The experimental results show that the proposed malware features achieved the highest accuracy, precision, recall, f1-score and AUC and with respective values of 97.6%, 98.3%, 98.6%, 98.4% and 96.4%%. It also achieved 98% area under the precision-recall curve indicating that Normalized Gammachirp Cepstral Coefficients are effective for Android malware classification. Moreover, the processing and detection times were also reasonably short. Amongst the best-performing models were Extratrees, Random forest, CatBoost, XGBoost and KNeighbors.
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- Since their inaugural releases in 2007, Google’s Android and Apple’s iOS have grown to dominate the mobile OS market share. Currently, they jointly possess over 99% of the global market share with Android being the leading mobile Operating System of choice worldwide, controlling close to 70% of the market share. Mobile devices have enabled the exponential growth of a plethora of mobile applications that play key roles in enabling many use cases that are pivotal in our daily lives. On the other hand, access to a large pool of potential end users is available to both legitimate and nefarious applications, thus making mobile devices a burgeoning target of malicious applications. Current malware detection solutions rely on tedious, time-consuming, knowledge-based, and manual processes to identify malware. This paper introduces BarkDroid, a novel Android malware detection technique that uses the low-level Bark Frequency Cepstral Coefficients audio features to detect malware. The initial results obtained show that Bark Frequency Cepstral Coefficient shave high discriminative capabilities to achieve accurate preditions. BarkDroid achieved 97.9% accuracy, 98.5% precision, an F1 score of 98.6%,and shorter execution times.
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