论文标题
基于IoT恶意软件检测的新的深层CNN和合奏学习
A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection
论文作者
论文摘要
安全问题在各种类型的网络中受到威胁，尤其是在需要早期检测的物联网（IoT）环境中。物联网是Home Automation Systems等实时设备的网络，可以通过开源Android设备来控制，这可能是攻击者的开放式基地。攻击者可以访问网络凭据，启动其他类型的安全漏洞，并损害网络控制。因此，及时检测越来越多的复杂恶意软件攻击是确保网络保护信誉的挑战。在这方面，我们开发了一个新的恶意软件检测框架，深层挤压和集合学习（DSBEL），该框架由新颖的挤压增强的边界区域拆分转换 - 转换 - 群（SB-BR-STM）CNN和集合学习组成。拟议的STM块采用多路径扩张的卷积，边界和区域操作来捕获同质和异质的全球恶意模式。此外，使用转移学习和基于多路径的挤压和提高初始和最终级别来实现各种特征地图，以学习微小的模式变化。最后，从开发的深层SB-BR-STM CNN中提取了增强的判别特征，并提供给集合分类器（SVM，MLP和ADABOOSTM1），以改善混合学习概括。 IOT_MALWARE数据集评估了针对现有技术的建议DSBEL框架和SB-BR-STM CNN的性能分析。评估结果表明，渐进式性能为98.50％的精度，97.12％的F1得分，91.91％MCC，95.97％的召回和98.42％的精度。拟议的恶意软件分析框架对及时发现恶意活动非常有用，并提出了未来的策略
Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source android devices, which can be an open ground for attackers. Attackers can access the network credentials, initiate a different kind of security breach, and compromises network control. Therefore, timely detecting the increasing number of sophisticated malware attacks is the challenge to ensure the credibility of network protection. In this regard, we have developed a new malware detection framework, Deep Squeezed-Boosted and Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning. The proposed STM block employs multi-path dilated convolutional, Boundary, and regional operations to capture the homogenous and heterogeneous global malicious patterns. Moreover, diverse feature maps are achieved using transfer learning and multi-path-based squeezing and boosting at initial and final levels to learn minute pattern variations. Finally, the boosted discriminative features are extracted from the developed deep SB-BR-STM CNN and provided to the ensemble classifiers (SVM, MLP, and AdabooSTM1) to improve the hybrid learning generalization. The performance analysis of the proposed DSBEL framework and SB-BR-STM CNN against the existing techniques have been evaluated by the IOT_Malware dataset on standard performance measures. Evaluation results show progressive performance as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97 % Recall, and 98.42 % Precision. The proposed malware analysis framework is robust and helpful for the timely detection of malicious activity and suggests future strategies
