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

Abstract

The astonishing growth 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 accuracy, recall, precision, F1score, 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.

Analysis and microcontroller implementation of Josephson junction driven by Wien bridge circuit

“…It is with great achievement to note that this research paper considers the WBC with conventional parameters coupled to the RCSJJ model which remain not yet been investigated and the interest in coupling both systems with separate rich behaviors is to obtain complex characteristics like chaos which find wide application in security regarding encryption of sEMG signals [46,47], communication [48] and network [49,50]. This paper then further realized the interesting application of security with the success reported in encrypting a biomedical image.…”

A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization

“…Comparative experiments using the intrusion detection method proposed in this article with the intrusion detection method in the literature ( Qureshi et al, 2021 ; Zhongshi, Yan & Yudong, 2019 ; Esmaeili et al, 2022 ) on the N-BaIoT dataset with multiple classifications are shown in Table 5 . From the detection results in the table, we can see that the performance detection index of each method proposed in this article is as high as 99.98% in the N-BaIoT dataset, and the detection performance is significantly better than several others.…”

“…We compared the DL-BiLSTM model with recent studies ( Shieh et al, 2021 ; Wang et al, 2021 ; Bhardwaj, Mangat & Vig, 2020 ; Qazi, Almorjan & Zia, 2022 ; Qureshi et al, 2021 ; Alharbi et al, 2021 ; Attique, Hao & Ping, 2022 ; Om Kumar et al, 2022 ) on two datasets, CIC IDS2017 and N-BaIoT, in order to further illustrate the superiority of the detection performance of the model provided in this article. The experimental results can be seen in Tables 7 and 8 , and the performance test results not given in the paper are indicated with “-” in the table.…”

Artificial Intelligence Techniques in Cybersecurity Management

“…However, these edge devices have become exploitation points for intruders, it raised security and privacy challenge to the trustworthiness of edge devices by compromised devices that transmit false information to cloud servers. An intrusion detection system (IDS) is widely accepted as a technique to monitor malicious activities [26].…”