Unsupervised detection of security threats in cyberphysical system and IoT devices based on power fingerprints and RBM autoencoders

Albasir, Abdurhman; Hu, Q.; Naik, Kshirasagar; Naik, N. Rupsingh

doi:10.20517/jsss.2020.19

Cited by 2 publications

(1 citation statement)

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“…The tests showed rapid progress in detection ratios, without the need for large pre-processing steps. In the same direction, several authors have applied different AE assemblies to improve detection techniques based on anomalies occurring in IoT devices [59], security situation assessment [60], and for cyber-physical system (CPS) environments [61]. Having mentioned the applications of an AE, Figure 4 displays its formal structure.…”

Section: Data Pre-processingmentioning

confidence: 99%

Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks

et al. 2022

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With the growing popularity of cryptocurrencies, which are an important part of day-to-day transactions over the Internet, the interest in being part of the so-called cryptomining service has attracted the attention of investors who wish to quickly earn profits by computing powerful transactional records towards the blockchain network. Since most users cannot afford the cost of specialized or standardized hardware for mining purposes, new techniques have been developed to make the latter easier, minimizing the computational cost required. Developers of large cryptocurrency houses have made available executable binaries and mainly browser-side scripts in order to authoritatively tap into users’ collective resources and effectively complete the calculation of puzzles to complete a proof of work. However, malicious actors have taken advantage of this capability to insert malicious scripts and illegally mine data without the user’s knowledge. This cyber-attack, also known as cryptojacking, is stealthy and difficult to analyze, whereby, solutions based on anti-malware extensions, blocklists, JavaScript disabling, among others, are not sufficient for accurate detection, creating a gap in multi-layer security mechanisms. Although in the state-of-the-art there are alternative solutions, mainly using machine learning techniques, one of the important issues to be solved is still the correct characterization of network and host samples, in the face of the increasing escalation of new tampering or obfuscation techniques. This paper develops a method that performs a fingerprinting technique to detect possible malicious sites, which are then characterized by an autoencoding algorithm that preserves the best information of the infection traces, thus, maximizing the classification power by means of a deep dense neural network.

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Section: Data Pre-processingmentioning

confidence: 99%