A deep learning-enhanced botnet detection system based on Android manifest text mining

Android botnets remain a significant threat to mobile and IoT systems and networks as they continue to infect millions of devices worldwide. Therefore, there is a need to develop more effective solutions to tackle their spread. Hence, in this paper we propose a system for detecting Android botnets through automated text mining of the manifest files obtained from apps. The proposed method utilizes NLP techniques to extract features from the manifest files and a deep learning-based classification model is used to detect botnet applications. The classification model is implemented using CNN and a traditional machine learning classifier such as SVM, Random Forest or KNN. We performed experiments to evaluate the proposed system with 3858 Android applications consisting of 1929 botnet and 1929 benign samples. The results showed the best overall performance with the CNN-SVM hybrid model which had an average accuracy of 96.9% thus outperforming the singular machine learning classifiers

Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in predicting patch correctness on a deduplicated dataset of 1000 labeled patches. Our study shows that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature

What Is It Like to Be a PNG?

In this thesis, I discuss my recent compositions from the perspective of contemporary strands within philosophical realism. In particular, I focus on the realist theme of the autonomous existence of objects, an existence independent of human awareness. I propose that while we cannot directly or fully access objects outside human perception, it is fruitful to speculate on their autonomous existence, especially within the realm of aesthetics. My objective as an artist is to inspire speculation on this autonomous existence or what I refer to as “objects performing their own existence.” I have attempted to accomplish this objective by using common, found, and simple objects and by integrating minimal aesthetic cues within an ecological context – one in which objects and spectators engage as they normally would outside an institutionalized art context. I begin this discourse by laying the theoretical framework for my practice, focusing on certain principles of realism. I then survey pieces from other artists that I believe engage these realist principles. Lastly, I discuss four of my own compositions in relation to the notion of an objects’ autonomy and irreducibility