Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad Campus, Islamabad 44000, Pakistan.
Department of Creative Technologies, Faculty of Computing and Artificial Intelligence, Air University, Islamabad 44000, Pakistan.
Sensors (Basel). 2022 Nov 29;22(23):9305. doi: 10.3390/s22239305.
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.
物联网 (IoT) 设备的使用随着互联网的普及呈指数级增长。随着物联网设备上数据容量的增加，这些设备容易受到恶意软件攻击；因此，恶意软件检测成为物联网设备中的一个重要问题。需要一种有效、可靠和高效的机制来识别复杂的恶意软件。近年来，研究人员已经提出了多种用于恶意软件检测的方法，但是，准确的检测仍然是一个挑战。我们提出了一种基于深度学习的集成分类方法，用于检测物联网设备中的恶意软件。它使用三步法；在第一步中，使用缩放、归一化和去噪对数据进行预处理，而在第二步中，选择特征并应用独热编码，然后基于 CNN 和 LSTM 的输出进行集成分类器以检测恶意软件。我们将结果与最先进的方法进行了比较，我们的方法在标准数据集上的平均准确率为 99.5%，优于现有的方法。
