Research perceptions on ransomware attack: a complete analysis on conventional authentication protocols in network

Shemitha, P. A.; Dhas, Julia Punitha Malar

doi:10.1007/s12065-020-00502-9

Cited by 4 publications

(6 citation statements)

References 82 publications

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“…This development in cybersecurity prompted the rise of more advanced ransomware variants like Locky and Cerber, which not only employed more sophisticated encryption algorithms but also integrated evasion tactics to circumvent traditional cybersecurity measures [3,7]. Yet, as cyber defenses continued to advance, particularly with the enhancement of backup and recovery solutions, the potency of ransomware relying solely on encryption began to decline [8,9]. This led to a notable transition in the ransomware landscape, where recent trends have witnessed a pivot towards ransomware strategies that prioritize data exfiltration [10,11].…”

Section: Ransomware Evolution and Trendsmentioning

confidence: 99%

“…However, more recent trends have indicated a notable shift in the tactics employed by ransomware perpetrators [3,7]. Contemporary ransomware groups, moving beyond the sole reliance on file encryption, are progressively focusing on data exfiltration [8,9]. This progression has been recognized as a significant transformation in the threat landscape posed by ransomware [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Analyzing Data Theft Ransomware Traffic Patterns Using BERT

Almeida,

Vasconcelos

2023

Preprint

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This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

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Section: Ransomware Evolution and Trendsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing Data Theft Ransomware Traffic Patterns Using BERT

Almeida,

Vasconcelos

2023

Preprint

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“…Therefore, it become utmost important to develop an approach for removing and detecting such malware from the systems. A detailed documented study on prevention and perception of conventional authentication is discussed in [5]. In several state of the arts, local and global features have been attempted to cope up by introducing code interchange, null value insertion, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Hussain,

Razak,

Firdaus

2023

JCSANDM

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Globally extensive digital revolutions involved with every process related to human progress can easily create the critical issues in security aspects. This is promoted due to the important factors like financial crises and geographical connectivity in worse condition of the nations. By this fact, the authors are well motivated to present a precise literature on malware detection with deep learning approach. In this literature, the basic overview includes the nature of nature of malware detection i.e., static, dynamic, and hybrid approach. Another major component of this articles is the investigation of the backgrounds from recently published and highly cited state-of-the-arts on malware detection, prevention and prediction with deep learning frameworks. The technologies engaged in providing solutions are utilized from AI based frameworks like machine learning, deep learning, and hybrid frameworks. The main motivations to produce this article is to portrait clear pictures of the option challenging issues and corresponding solution for developing robust malware-free devices. In the lack of a robust malware-free devices, highly growing geographical and financial disputes at wide globes can be extensively provoked by malicious groups. Therefore, exceptionally high demand of the malware detection devices requires a very strong recommendation to ensure the security of a nation. In terms preventing and recovery, Zero-day threats can be handled by recent methodology used in deep learning. In the conclusion, we also explored and investigated the future patterns of malware and how deals with in upcoming years. Such review may extend towards the development of IoT based applications used many fields such as medical devices, home appliances, academic systems.

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“…Flooding attacks are detected through the route request time data and end‐to‐end delay 8 . The ransomware attack restricts the users by locking and encrypting the files and predicting through authentication and optimization algorithms 9 . In a man‐in‐the‐middle attack , it enters as a third party in two‐way communication; existing nodes think it is talking to the other 10 .…”

Section: Introductionmentioning

confidence: 99%

“…8 The ransomware attack restricts the users by locking and encrypting the files and predicting through authentication and optimization algorithms. 9 In a man-in-the-middle attack, it enters as a third party in two-way communication; existing nodes think it is talking to the other. 10 Man-in-the-middle attacks detect through the packet time transmission data.…”

Section: Introductionmentioning

confidence: 99%

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

Selvi

Shen

2022

Concurrency and Computation

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Wireless sensor network (WSN) is widely used in many fields, including agriculture, military, healthcare, monitoring, and surveillance. WSN application increases day by day due to growth in embedded and internet technologies. However, data security in WSN is challenging, and different types of attacks are increasing. Traditional intrusion detection methods analyze and identify attacks such as Denial of Service, anomaly detection, wormhole, sybil, and blackhole. Traditional methods never addressed the application layer's novel attack, such as the delete attack. Unique DELETE attack (DA) forms in the application layer and targets web servers and applications. Since unique DA use uniform resource locator requests, distinguishing them from legitimate traffic becomes difficult. The unique DA classify as unique (or) single, multiple, and repeated unique DELETE attacks; for detecting the unique DA types, gradient thresholding-long short-term memory (GT-LSTM) algorithm is proposed using packet per second and traffic rate data in WSN nodes. GT-LSTM algorithm detects unique DA through different thresholding at the training option layer in LSTM. Different gradient thresholding values in LSTM reduce the exploding gradients which fail to detect the unique DA nodes. Gradient thresholding in LSTM layers lies 1 to 3 for detecting unique DELETE attack nodes. The proposed method reduces detection time, improves accuracy, and identifies the hidden node that performs a unique DA in WSN. The unique DELETE Attack identification and performance through the proposed GT-LSTM is analyzed in the NS2 simulation environment and the test bed of WSN. Forms the experimental and simulation results, GT-LSTM performs better than fuzzy, KNN, and linear regression-based intruder detection systems. The proposed algorithm achieves 99% accuracy in detecting the DELETE attack nodes in WSN.

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Research perceptions on ransomware attack: a complete analysis on conventional authentication protocols in network

Cited by 4 publications

References 82 publications

Analyzing Data Theft Ransomware Traffic Patterns Using BERT

Analyzing Data Theft Ransomware Traffic Patterns Using BERT

Deep Learning Based Hybrid Analysis of Malware Detection and Classification: A Recent Review

Detection of unique delete attack in wireless sensor network using gradient thresholding‐long short‐term memory algorithm

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