Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently. This paper presents an Analytic-based deep neural network, Android Malware detection (ADAM), that employs a fine-grained set of features to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones for recycling the pre-trained model(s) and making them more adaptable by model personalization and federated learning techniques. This adjustability is also assisted by federated learning guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. The ADAM's feature-specific DNNs, on average, achieved more than 98% accuracy, resulting in an outstanding performance against data manipulation attacks

A data placement approach for scientific workflow execution in hybrid clouds

Empirical thesis.Bibliography: pages 47-52.1. Introduction -- 2. Literature review -- 3. Problem statement -- 4. Hybrid scheduling for hybrid clouds -- 5. Evaluation -- 6. Conclusion -- List of symbols -- References.Cloud computing has been widely adopted by industry practitioners and researchers. Recently, applications in science and engineering such as scientific workflows have also been increasingly deployed in clouds. As these applications are becoming resource intensive in both data and computing, private clouds struggle to cope with their resource requirements. Public clouds claim to overcome many shortcomings of private clouds. However, the complete offloading of workflow execution to public clouds may introduce excessive data transfer and privacy/governance concerns. In this thesis, we propose a hybrid cloud solution for workflow scheduling explicitly considering data placement. To this end, we present Hybrid Scheduling for Hybrid Clouds (HSHC), which schedules scientific workflows across private and public clouds incorporating a novel dynamic data placement policy. HSHC consists of two phases: static and dynamic. The former uses an extended genetic algorithm to solve the problem of workflow scheduling with static information of workflows and cloud resources. The latter adjusts scheduling and data placement decisions reflecting changing conditions of workflow execution in the hybrid cloud. We evaluate HSHC with both real-world scientific applications and random workflows in performance and cost. Experimental results demonstrate HSHC’s two-phase approach effectively deals with the dynamic nature of the hybrid cloud.1 online resource (x, 52 pages) diagrams, graph