Technique for IoT malware detection based on control flow graph analysis

Bobrovnikova, Kira; Lysenko, Sergii; Savenko, Bohdan; Gaj, Piotr; Savenko, Oleg

doi:10.32620/reks.2022.1.11

Cited by 16 publications

(8 citation statements)

References 12 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is impossible to unambiguously answer this question in numerical and parametric form based on the conducted research. This point needs additional investigation in the context of implementing proactive technologies of AI-powered protection of assets against cyberattacks [46][47][48] . However, these aspects do not affect the functionality and adequacy of the material presented in the article.…”

Section: Discussionmentioning

confidence: 99%

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

show abstract

Section: Discussionmentioning

confidence: 99%

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Kovtun,

Grochla,

Kharchenko

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The use of mathematical models for systems with functions important for safety in normative documents is recommendatory. The following classes of models can be distinguished: risk-oriented [3], Bayesian [4], fault trees [5], FMECA [6], Markov and semi-Markov [7], [8], multi-phase [9], control flow graph analysis [10], etc.…”

Section: B Related Workmentioning

confidence: 99%

Multi-fragmental and multi-phase availability models of the safety-critical I&C systems with two-cascade redundancy

Kharchenko,

Ponochovnyi,

Babeshko

2024

International Journal of Electronics and Telecommunications

View full text Add to dashboard Cite

Traditional availability, reliability, and safety models face the dimension problem due to a huge number of components in modern systems, motivating further research in this field. This paper focuses on multi-fragmental and multiphase models for availability and functional safety assessment of the information and control (I&C) systems with two-cascade redundancy considering design faults manifestation during operation. The methodology of the research is based on Markov and semi-Markov chains with the utilization of multi-phase modeling. Several multi-phase models are developed and investigated considering different conditions of operation and failures caused by version faults. The case study of the research is based on the analysis of safety-critical nuclear power plant I&C systems such as the reactor trip systems developed using the programmable platform RadICS.

show abstract

“…Bobrovnikova [23] introduced a novel approach rooted in control flow graph analysis for the IoT malware detection, demonstrating its effectiveness in safeguarding the IoT devices from cyber threats. This innovative method contributes to the advancement of the IoT security measures.…”

Section: State Of the Artmentioning

confidence: 99%

A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

Idouglid,

Tkatek,

Elfayq

et al. 2024

reks

View full text Add to dashboard Cite

In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.

show abstract

Technique for IoT malware detection based on control flow graph analysis

Cited by 16 publications

References 12 publications

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Multi-fragmental and multi-phase availability models of the safety-critical I&C systems with two-cascade redundancy

A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

Contact Info

Product

Resources

About