查看更多>>摘要:Investigators publish new report on ar tificial intelligence. According to news reporting out of Prince Mohammad Bin Fa hd University by NewsRx editors, research stated, "The ubiquitous adoption of An droid devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mecha nisms, with anomaly-based approaches leveraging machine learning (ML) gaining at traction due to their signature-agnostic nature." Our news editors obtained a quote from the research from Prince Mohammad Bin Fah d University: "However, the problem lies in devising accurate ML models which ca pture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved int o this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridizat ion. The core idea of this framework was the conversion of text-based data extra cted from Android APKs into grayscale images. The novelty aspect of this work li ed in the unique image feature extraction strategies and their subsequent hybrid ization to achieve accurate malware classification using ML models. More specifi cally, four distinct feature extraction methodologies, namely, Texture and histo gram of oriented gradients (HOG) from spatial domain, and discrete wavelet trans form (DWT) and Gabor from the frequency domain were employed to hybridize the fe atures for improved malware identification. To this end, three image-based datas ets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, incl uding naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM ), and random forest (RF), were employed for the classification task."