Flame0409

代表作品 / 项目

• Android-Malware-Detection⭐ 17使用安卓Opcode字节码的N-gram序列特征进行恶意软件检测的完全步骤，使用算法RF，KNN
• Android⭐ 6基于opcode的N-gram安卓恶意软件检测，主要代码，完整代码请见Android_Malware_detection,有疑问请联系flameguocp@163.com
• Jane-Street-Market-Prediction⭐ 4Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.
• DNN_Classcify⭐ 2手撕代码，以tensorflow为基础框架，实现主要包括2分类及多分类ANN，CNN，InceptionNet，RES-net，VGG-Net等
• Android_Malware_Detection-Adversarial-attack⭐ 2实现了通过Android软件的Opcode的N-gram序列作为特征，在提取N-gram序列频率后，转化为7*7*7矩阵放入VGG-Net进行分类，并使用DeepFool进行对抗样本生成以及强化训练