Haoran Ge | Searcharxiv

arXiv2025

Shot noise signatures identifying non-Abelian properties of Jackiw-Rebbi zero modes

Jackiw-Rebbi zero modes were first proposed in 1976 as topologically protected zero-energy states localized at domain walls in one-dimensional Dirac systems. They have attracted widespread attention in the field of topological quantum computing, as they serve as non-superconducting analogs of Majorana zero modes and support non-Abelian statistics in topological insulator systems. %In the braiding process of the Jackiw-Rebbi zero modes, their braiding properties are closely related to the strength of disorder. However, compared to their Majorana cousins, the braiding properties of Jackiw-Rebbi zero modes are vulnerable to the on-site energy deviation between the modes involved in the experiment. In this work, we propose to estimate the braiding properties of Jackiw-Rebbi zero-modes through measurements of transport signatures, which are readily measurable in current experiments. We find that the fidelity of braiding operation reaches unity when the current noise is fully suppressed, while this braiding fidelity monotonously decreases with the increasing of the current noise. Based on these transport signatures, we further discuss the correspondence between Majorana and Jackiw-Rebbi zero modes, highlighting their similarity in supporting non-Abelian statistics.

cond-mat.mes-hall↗

arXiv2020

Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning

Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each unit in the extracted behavior sequence is inventively represented as a vector, which allows Droidetec to automatically analyze the semantics of sequence segments and eventually find out the malicious code. Experiments with 9616 malicious and 11982 benign programs show that Droidetec reaches an accuracy of 97.22% and an F1-score of 98.21%. In all, Droidetec has a hit rate of 91% to properly find out malicious code segments.

cs.CR↗