Abstract

The rapid growth of malware systems has identified significant threats to the security of computer technology. Therefore it stimulates anti-malware providers and researchers to create new strategies that can protect users from global threats. The voluminous releases of malware that appear on the Internet identified major security threats. To address this issue, we introduce a hybrid Customized Convolutional Neural Network (CCNN) with a K-nearest neighbor (KNN) classification system that can classify variations of malware into their respective families automatically. The hybrid CCNN-KNN classification system efficiently overcomes the limitations of the existing CNN system. Thus, the proposed novel hybrid system which works on uneven nonlinear data. The experiment’s result demonstrates that CCNN-KNN is more than 99.35% precise, contrary to other CNN malware classification models. The proposed hybrid model, therefore, represents the most extensive malware classification schemes in conceptual models.

摘要

恶意软件系统的快速增长已经确定了对计算机技术安全的重大威胁。因此，它刺激了反恶意软件提供商和研究人员制定新的策略，可以保护用户免受全球威胁。Internet上出现的大量恶意软件释放确定了主要的安全威胁。为了解决此问题，我们引入了混合自定义卷积神经网络（CCNN）和K近邻（KNN）分类系统，该系统可以自动将恶意软件的变体分类到各自的家族中。CCNN-KNN混合分类系统有效地克服了现有CNN系统的局限性。因此，提出了新颖的混合系统，该系统可以处理不均匀的非线性数据。实验结果表明，CCNN-KNN的准确率超过99.35％，与其他CNN恶意软件分类模型相反。因此，提出的混合模型代表了概念模型中最广泛的恶意软件分类方案。