The alarming growth rate of malicious apps has become a serious issue. Numerous malware detection tools have been developed, including system-level and network-level approaches. In this paper, we introduce a malware detection system based on permission usage analysis. We develop 3-levels of pruning by mining the permission data to identify the most significant permissions. We utilizes machine-learning based classification methods to classify different families of malware and benign apps. Our evaluation finds that only 22 permissions are significant. We then compare the performance against a baseline approach. The results indicate that when Support Vector Machine is used as the classifier, we can achieve over 90% of precision, recall, accuracy. When we compare the detection effectiveness of SigPID to those of state-of-the-art approaches, SigPID can always get a better accuracy.

Federated learning (FL) enables a large number of clients to collaboratively train a global model through sharing their gradients in each synchronized epoch of local training. However, a centralized server used to aggregate these gradients can be compromised and forge the result in order to violate privacy or launch other attacks, which incurs the need to verify the integrity of aggregation. In this work, we explore how to design communication-efficient and fast verifiable aggregation in FL. We propose VeriFL, a verifiable aggregation protocol, with O(N) (dimension-independent) communication and O(N+d) computation for verification in each epoch, where N is the number of clients and d is the dimension of gradient vectors. Since d can be large in some real-world FL applications (e.g., 100K), our dimension-independent communication is especially desirable for clients with limited bandwidth and high-dimensional gradients. In addition, the proposed protocol can be used in the FL setting where secure aggregation is needed or there is a subset of clients dropping out of protocol execution. Experimental results indicate that our protocol is efficient in these settings.

Automatic cost learning for steganography based on deep neural networks is receiving increasing attention. Steganographic methods under such a framework have been shown to achieve better security performance than methods adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality, including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.