DOTMUG: A Threat Model for Target Specific APT Attacks–Misusing Google Teachable Machine

Charan, P.V. Sai; Anand, P. Mohan; Shukla, Sandeep K.; Selvan, Naveen; Chunduri, Hrushikesh

doi:10.1109/isdfs55398.2022.9800780

Cited by 4 publications

(2 citation statements)

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“…Research is still needed in several areas, including the viability and incorporation of proactive defenses, the deployment of defensive mechanisms in the IIoT and cloud computing, and the emergence of Industry 4.0[ 26 ]. Furthermore, to validate defense mechanisms against Advanced Persistent Threats (APTs) and make them more integrable and usable so they can be readily integrated into more crucial infrastructures, it is vital to take into account existing APTs and APT phases [ 15 , 16 , 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A great example is the largest colonial gas pipeline in the USA, which was shut down for several days after a malicious cyberattack and attributed to the shadow criminal group DarkSide [ 14 – 16 ]. Also, in the first quarter of 2021, in the city of Oldsmar in Florida, there was an attack on the government infrastructure responsible for the city's water supply.…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection

Zhao

2022

Computational Intelligence and Neuroscience

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Industry 4.0 affects all components of the modern industry value chain. The accelerating use of the Internet and the convergence of industrial and operational networks constantly increase the need for secure industrial communication solutions. Therefore, “multilevel industrial cyber protection” is critical to Industry 4.0. In general, industrial protection refers to safeguarding information and data and the intellectual property rights of production processes related to the overall industry environment. The availability, integrity, and confidentiality of systems must be maintained. The goal challenge is the best possible protection from attacks and threats which create immediate financial damage and other risks in the industry (reputation, etc.). Based on the Defense-in-Depth strategy, a holistic, multilayered, and in-depth protection of industrial systems is developed in this paper. Specifically, a Semi-Self-Supervised Intrusion Detection System (S3IDS) is proposed, which combines advanced machine learning techniques for industrial data noise reduction to automate the discovery and separation of classes, which are essentially equivalent to cyber-related anomalies. As demonstrated by a mathematical simulation based on computational number theory and specifically on the concept of the single object, the proposed S3IDS learns to accurately reconstruct samples to predict the nature of an anomaly created directly by the industrial ecosystem.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%