A Cyberattacks Detection Technique Based on Evolutionary Algorithms

Lysenko, Sergii; Bobrovnikova, Kira; Shchuka, Roman; Savenko, Oleg

doi:10.1109/dessert50317.2020.9125016

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…In order to evaluate the efficiency of the proposed approach the experiments were held. They were based on the usage of the BotGRABBER -cyberattacks' detection toolwas used [34][35][36][37][38].…”

Section: Methodsmentioning

confidence: 99%

Technique for IoT malware detection based on control flow graph analysis

Bobrovnikova

Lysenko

Savenko

et al. 2022

reks

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The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats. The rapid integration of the Internet into the IoT infrastructure in various areas of human activity, including vulnerable critical infrastructure, makes the detection of malware in the Internet of Things increasingly important. Annual reports from IoT infrastructure cybersecurity companies and antivirus software vendors show an increase in malware attacks targeting IoT infrastructure. This demonstrates the failure of modern methods for detecting malware on the Internet of things. This is why there is an urgent need for new approaches to IoT malware detection and to protect IoT devices from IoT malware attacks. The subject of the research is the malware detection process on the Internet of Things. This study aims to develop a technique for malware detection based on the control flow graph analysis. Results. This paper presents a new approach for IoT malware detection based on control flow graph analysis. Control flow graphs were built for suspicious IoT applications. The control flow graph is represented as a directed graph, which contains information about the components of the suspicious program and the transitions between them. Based on the control flow graph, metrics can be extracted that describe the structure of the program. Considering that IoT applications are small due to the simplicity and limitations of the IoT operating system environment, malware detection based on control flow graph analysis seems to be possible in the IoT environment. To analyze the behavior of the IoT application for each control flow graph, the action graph is to be built. It shows an abstract graph and a description of the program. Based on the action graph for each IoT application, a sequence is formed. This allows for defining the program’s behavior. Thus, with the aim of IoT malware detection, two malware detection models based on control flow graph metrics and the action sequences are used. Since the approach allows you to analyze both the overall structure and behavior of each application, it allows you to achieve high malware detection accuracy. The proposed approach allows the detection of unknown IoT malware, which are the modified versions of known IoT malware. As the mean of conclusion-making concerning the malware presence, the set of machine learning classifiers was employed. The experimental results demonstrated the high accuracy of IoT malware detection. Conclusions. A new technique for IoT malware detection based on control flow graph analysis has been developed. It can detect IoT malware with high efficiency.

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Section: Methodsmentioning

confidence: 99%

Technique for IoT malware detection based on control flow graph analysis

Bobrovnikova

Lysenko

Savenko

et al. 2022

reks

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“…Solutions devoted to cyberattack detection against Internet of Things infrastructure are widely presented [7,8]. Quite possibly, the most encouraging approaches for IoT cyberattack detection are based on machine learning algorithms (MLA) [9][10][11][12][13].…”

Section: The State-of-the-artmentioning

confidence: 99%

IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency

et al. 2022

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Cybersecurity is a common Internet of Things security challenge. The lack of security in IoT devices has led to a great number of devices being compromised, with threats from both inside and outside the IoT infrastructure. Attacks on the IoT infrastructure result in device hacking, data theft, financial loss, instability, or even physical damage to devices. This requires the development of new approaches to ensure high-security levels in IoT infrastructure. To solve this problem, we propose a new approach for IoT cyberattack detection based on machine learning algorithms. The core of the method involves network traffic analyses that IoT devices generate during communication. The proposed approach deals with the set of network traffic features that may indicate the presence of cyberattacks in the IoT infrastructure and compromised IoT devices. Based on the obtained features for each IoT device, the feature vectors are formed. To conclude the possible attack presence, machine learning algorithms were employed. We assessed the complexity and time of machine learning algorithm implementation considering multi-vector cyberattacks on IoT infrastructure. Experiments were conducted to approve the method’s efficiency. The results demonstrated that the network traffic feature-based approach allows the detection of multi-vector cyberattacks with high efficiency.

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“…Several studies focused on using prediction models for attack detections in smart grid infrastructures. For example, the authors of [123] proposed a detection method that uses cosine similarity and chisquare detector to identify FDIA in networks. They also employed Kalman filter to find expected measurements and calculate any deviation between actual measurements and estimated values.…”

Section: Prediction Modelsmentioning

confidence: 99%

“…Another technique is elliptic curve cryptosystems. The authors of [92] introduce elliptic curve cryptosystems as a public-key cryptography with the same power as RSA, despite its key size smaller than RSA [123].…”

Section: ) Cryptographic and Authenticationmentioning

confidence: 99%

Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

Khoei¹,

Slimane²,

Kaabouch³

2022

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One of the significant challenges that smart grid networks face is cyber-security. Several studies have been conducted to highlight those security challenges. However, the majority of these surveys classify attacks based on the security requirements, confidentiality, integrity, and availability, without taking into consideration the accountability requirement. In this survey paper, we provide a classification of attacks based on the OSI model and discuss in more detail the cyber-attacks that can target the different layers of smart grid networks communication. We also propose new classifications for the detection and countermeasure techniques and describe existing techniques under each category. Finally, we discuss challenges and future research directions.

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A Cyberattacks Detection Technique Based on Evolutionary Algorithms

Cited by 19 publications

References 28 publications

Technique for IoT malware detection based on control flow graph analysis

Technique for IoT malware detection based on control flow graph analysis

IoT Multi-Vector Cyberattack Detection Based on Machine Learning Algorithms: Traffic Features Analysis, Experiments, and Efficiency

Cyber-Security of Smart Grids: Attacks, Detection, Countermeasure Techniques, and Future Directions

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