A Multi-layer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT

5 G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective low-latency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system, and introduce new powerful attacks vectors leading to severe security and data privacy risks. Malware attacks are starting targeting weak but highly connected IoT devices showing the importance of security and privacy in this scenario. This paper designs a 5G-enabled system, consisted in a deep learning-based architecture aimed to classify malware attacks on the IIoT. Our methodology is based on an image representation of the malware and a Convolutional Neural Networks (CNNs) that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy

System details for evaluation.

<p>System details for evaluation.</p

Comparison of existing integrity schemes in Cloud.

<p>Comparison of existing integrity schemes in Cloud.</p

Provenance based data integrity checking and verification in cloud environments

<div><p>Cloud computing is a recent tendency in IT that moves computing and data away from desktop and hand-held devices into large scale processing hubs and data centers respectively. It has been proposed as an effective solution for data outsourcing and on demand computing to control the rising cost of IT setups and management in enterprises. However, with Cloud platforms user’s data is moved into remotely located storages such that users lose control over their data. This unique feature of the Cloud is facing many security and privacy challenges which need to be clearly understood and resolved. One of the important concerns that needs to be addressed is to provide the proof of data integrity, i.e., correctness of the user’s data stored in the Cloud storage. The data in Clouds is physically not accessible to the users. Therefore, a mechanism is required where users can check if the integrity of their valuable data is maintained or compromised. For this purpose some methods are proposed like mirroring, checksumming and using third party auditors amongst others. However, these methods use extra storage space by maintaining multiple copies of data or the presence of a third party verifier is required. In this paper, we address the problem of proving data integrity in Cloud computing by proposing a scheme through which users are able to check the integrity of their data stored in Clouds. In addition, users can track the violation of data integrity if occurred. For this purpose, we utilize a relatively new concept in the Cloud computing called “Data Provenance”. Our scheme is capable to reduce the need of any third party services, additional hardware support and the replication of data items on client side for integrity checking.</p></div

Architecture of <i>DataStore</i> application with different layers of abstraction.

<p>Architecture of <i>DataStore</i> application with different layers of abstraction.</p

Results of the calculated time (minutes:seconds format) with and without the provenance for Eucalyptus Walrus.

<p>Results of the calculated time (minutes:seconds format) with and without the provenance for Eucalyptus Walrus.</p

Cost of provenance collection in terms of elapsed time (in seconds) for different number of objects.

<p>Cost of provenance collection in terms of elapsed time (in seconds) for different number of objects.</p

Various components of integrity service (integrity tracker phase).

<p>Various components of integrity service (integrity tracker phase).</p

Black box architecture of provenance based data integrity verification.

<p>Black box architecture of provenance based data integrity verification.</p

Improved Electrochemical Performance of Aqueous Hybrid Supercapacitors Using CrCo₂O₄ Mesoporous Nanowires: An Innovative Strategy toward Sustainable Energy Devices

High-rate aqueous hybrid supercapacitors (AHSCs) have attracted relevant scientific significance owing to their expected energy density, supercapacitor-level power density, and battery-level energy density. In this work, a bimetallic nanostructured material with chromium-incorporated cobalt oxide (CCO, i.e., CoCr2O4) was prepared via a hydrothermal method to form a stable cubic obelisk structure. Compared with CCO materials prepared using traditional methods, CCO displayed a nanowire structure (50 nm diameter), suggesting an enhanced specific surface area and a large number of active sites for chemical reactions. The electrode possessed a high specific capacitance (2951 F g–1) at a current density of 1 A g–1, minimum Rct (0.135 Ω), and the highest capacitance retention (98.7%), making it an ideal electrode material for AHSCs. Ex situ analysis based on X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) showed a favorable stability of CCO after 10,000 cycles without any phase changes being detected. GGA and GGA + U methods employed in density functional theory (DFT) also highlighted the enhanced metallic properties of CCO originating from the synergistic effect of semiconducting Cr2O3 and Co3O4 materials