Machine learning is penetrating various domains virtually, thereby proliferating excellent results. It has also found an outlet in digital forensics, wherein it is becoming the prime driver of computational efficiency. A prominent feature that exhibits the effectiveness of ML algorithms is feature extraction that can be instrumental in the applications for digital forensics. Convolutional Neural Networks are further used to identify parts of the file. To this end, we observed that the literature does not include sufficient information about the identification of the algorithms used to compress file fragments. With this research, we attempt to address this gap as compression algorithms are beneficial in generating higher entropy comparatively as they make the data more compact. We used a base dataset, compressed every file with various algorithms, and designed a model based on that. The used model was accurately able to identify files compressed using compress, lzip and bzip2.

翻译：机器学习实际上渗透了多个领域, 从而扩散了优异的结果 。 它还在数字法学中发现了一个插座, 它正在成为计算效率的主要驱动力 。 一个突出的特征是显示 ML 算法的有效性的特征提取可以在数字法学应用中发挥作用 。 进化神经网络被进一步用于识别文件的部件 。 为此, 我们观察到文献中没有包含足够的关于压缩文件碎片所用算法的识别信息 。 通过这项研究, 我们试图弥补这一差距, 因为压缩算法在使数据更加紧凑时, 有助于生成更高的酶。 我们使用一个基础数据集, 用各种算法压缩每个文件, 并设计了一个基于这个模型的模型 。 所使用的模型能够准确地识别使用压缩器、 Lzip 和 bzip2 来压缩的文件 。