Ransomware detection, prevention and protection in IoT devices using ML techniques based on dynamic analysis approach

Sharma, Purushottam; Kapoor, Shaurya; Sharma, Richa

doi:10.1007/s13198-022-01793-0

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…The severity of the attack is classified according to the degree of damage to the target network environment according to the attack intent, from 0 to 9 divided into ten levels, the higher the level, the higher the severity of the consequences of the attack [9]. The classification is mainly based on the destructiveness of the attack and the purpose and means of the invasion.…”

Section: Attack Severity Classificationmentioning

confidence: 99%

“…Implementing effective cybersecurity measures can be costly and time-consuming, and smaller organizations may not have the resources to invest in such measures. Using information technology to evaluate and prevent cyber threats in a hierarchical manner can help organizations make better use of their limited resources by identifying the areas of greatest risk and focusing their efforts on those areas [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…The process of assessing network security situations involves examining the risk management perspective and utilizing scientific methods and techniques to analyze various elements of the network, including potential security threats and vulnerabilities [9]. By quantifying the risk associated with potential threats, it becomes possible to develop effective protective measures and defense countermeasures to mitigate, avoid, or resist risks while keeping them within acceptable levels.…”

Section: Introductionmentioning

confidence: 99%

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Using Information Technology to Quantitatively Evaluate and Prevent Cybersecurity Threats in a Hierarchical Manner

Mai¹

2023

Int. J. Appl. Inf. Manag.

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The vulnerability of traditional network security technology in the face of rapid advancements in information technology and the constant changes in network security. As a result, hackers can easily exploit loopholes in traditional security measures, such as cracking cryptographic algorithms, and stealing sensitive user information and data, which has led to a crisis of trust in recent years. To ensure safe and effective operation of massive data on the network, this article presents a quantitative assessment of the network's threat situation. The assessment is divided into two parts: support evaluation and credibility evaluation. These parts are further broken down into three levels of evaluation and severity evaluation. The article also provides a list of network security-related precautions that can be taken to mitigate potential risks. The experimental results show that implementing these hierarchical security measures can improve the security rate of Internet users' information by 4%-5%. Overall, the article highlights the importance of adopting more advanced and sophisticated security measures to combat the increasingly complex threats posed by cybercriminals.

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Section: Attack Severity Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Information Technology to Quantitatively Evaluate and Prevent Cybersecurity Threats in a Hierarchical Manner

Mai¹

2023

Int. J. Appl. Inf. Manag.

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“…The core motivation behind NTFS+ is to integrate advanced security measures directly into the file system [20], [19]. These could include anomaly detection algorithms to monitor and flag unusual file encryption activities, integrated backup solutions for quick recovery, and more robust access controls to prevent unauthorized changes [7], [17]. The development of NTFS+ aims not only to defend against current ransomware methodologies but also to establish a foundation that can adapt to and counter future evolutions in ransomware tactics.…”

Section: B Ntfsmentioning

confidence: 99%

“…A prominent strategy in this field has been the deployment of machine learning algorithms, which scrutinize file access patterns to pinpoint irregular encryption activities that are typically indicative of ransomware infiltration [5], [23], [8]. These algorithms are trained on vast datasets to accurately distinguish between normal operations and potential ransomware threats [17], [24], [25]. In addition to machine learning, heuristic analysis has been pivotal in this area [26], [27].…”

Section: A Ransomware Detectionmentioning

confidence: 99%

NTFS+: An Enhanced NTFS File System Against Ransomware Invasions

Gu,

Kang

2023

Preprint

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Ransomware poses a significant threat to digital security, evolving rapidly with sophisticated encryption and data exfiltration tactics. This study introduces NTFS+, an enhanced version of the New Technology File System (NTFS), designed to combat these emerging ransomware threats. NTFS+ integrates machine learning, specifically Generative Adversarial Networks, to detect abnormal file system activities indicative of ransomware attacks. The system's core architecture features minifilter drivers that analyze file access patterns in real-time, enabling proactive response to potential threats. Evaluation of NTFS+ demonstrates its efficacy in accurately distinguishing between benign and ransomware activities, with a focus on minimizing false positives and negatives. Despite its effectiveness, challenges such as zero-day ransomware attacks highlight areas for future improvement, including enhanced learning capabilities and cloud integration. NTFS+ represents a significant advancement in file system-based ransomware mitigation, offering a scalable, adaptable, and robust solution for modern cybersecurity needs.

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Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

Safeer,

Tahir,

Nawaz

et al. 2024

Security and Privacy

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The Internet of Things (IoT) effortlessly enables communication between items on the World Wide Web and other systems. This extensive use of IoTs has created new services and automation in numerous industries, enhancing the standard of living, especially in healthcare. Internet of Medical Things (IoMT) adoption has been beneficial during pandemic conditions by enabling remote patient monitoring and therapy. Nevertheless, the excessive use of IoMT has raised security concerns as it can compromise critical data. This breach in security can result in an inaccurate diagnosis or violate privacy. This research presents a novel approach to hybrid deep learning‐based detection of malware solutions for the IoT. This study uses RNN‐Bi‐LSTM to detect and extract significant features related to an already existing dataset. The proposed model exhibits a detection accuracy of 98.38% when evaluated using these existing datasets. Statistical tests like Mathew co‐relation and Log Loss also indicated reliability of proposed framework. The distinguished feature of our framework is its ability to combine complex deep learning models for IoMT security, which is of economic and scientific importance. It certainly offers a reliable solution for healthcare applications that rely on real‐time functionality and dependency on IoMT systems.

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Ransomware detection, prevention and protection in IoT devices using ML techniques based on dynamic analysis approach

Cited by 7 publications

References 17 publications

Using Information Technology to Quantitatively Evaluate and Prevent Cybersecurity Threats in a Hierarchical Manner

Using Information Technology to Quantitatively Evaluate and Prevent Cybersecurity Threats in a Hierarchical Manner

NTFS+: An Enhanced NTFS File System Against Ransomware Invasions

Advanced hybrid malware identification framework for the Internet of Medical Things, driven by deep learning

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