A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

A Hybrid DL-Based Detection Mechanism for Cyber Threats in Secure Networks

Blog Article

The astonishing growth simply boho classroom of sophisticated ever-evolving cyber threats and attacks throws the entire Internet-of-Things (IoT) infrastructure into chaos.As the IoT belongs to the infrastructure of interconnected devices, it brings along significant security challenges.Cyber threat analysis is an augmentation of a network security infrastructure that primarily emphasizes on detection and prevention of sophisticated network-based threats and attacks.Moreover, it requires the security of network by investigation and classification of malicious activities.

In this study, we propose a DL-enabled malware detection scheme using a hybrid technique based on the combination of a Deep Neural Network(DNN) and Long Short-Term Memory(LSTM) for the efficient identification of multi-class malware families in IoT infrastructure.The proposed scheme utilizes latest 2018 dataset named as N_BaIoT.Furthermore, our proposed scheme is evaluated using standard performance metrics such as 9x11 pergola accuracy, recall, precision, F1-score, and so forth.The DL-based malware detection system achieves 99.

96% detection accuracy for IoT based threats.Finally, we also compare our proposed work with other robust and state-of-the-art detection schemes.