Zero-day Ransomware Attack Detection using Deep Contractive Autoencoder and Voting based Ensemble Classifier

Zahoora, Umme; Rajarajan, Muttukrishnan; Pan, Zahoqing; Khan, Asifullah

doi:10.1007/s10489-022-03244-6

Cited by 35 publications

(13 citation statements)

References 48 publications

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“…Similarly, for some other proposals e.g., [ 19 , 20 , 24 ], there is a huge difference among various statistics, which is convincing enough to claim that the security provision with the proposed approach is significantly different from these proposals. Similar trends can also be observed with other proposals i.e., [ 25 , 27 , 28 ].…”

Section: Experimental Resultssupporting

confidence: 90%

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An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Tariq

Ullah

Uddin

et al. 2022

Sensors

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Remote healthcare systems and applications are being enabled via the Internet of Medical Things (IoMT), which is an automated system that facilitates the critical and emergency healthcare services in urban areas, in addition to, bridges the isolated rural communities for various healthcare services. Researchers and developers are, to date, considering the majority of the technological aspects and critical issues around the IoMT, e.g., security vulnerabilities and other cybercrimes. One of such major challenges IoMT has to face is widespread ransomware attacks; a malicious malware that encrypts the patients’ critical data, restricts access to IoMT devices or entirely disable IoMT devices, or uses several combinations to compromise the overall system functionality, mainly for ransom. These ransomware attacks would have several devastating consequences, such as loss of life-threatening data and system functionality, ceasing emergency and life-saving services, wastage of several vital resources etc. This paper presents a ransomware analysis and identification architecture with the objective to detect and validate the ransomware attacks and to evaluate its accuracy using a comprehensive verification process. We first develop a comprehensive experimental environment, to simulate a real-time IoMT network, for experimenting various types of ransomware attacks. Following, we construct a comprehensive set of ransomware attacks and analyze their effects over an IoMT network devices. Furthermore, we develop an effective detection filter for detecting various ransomware attacks (e.g., static and dynamic attacks) and evaluate the degree of damages caused to the IoMT network devices. In addition, we develop a defense system to block the ransomware attacks and notify the backend control system. To evaluate the effectiveness of the proposed framework, we experimented our architecture with 194 various samples of malware and 46 variants, with a duration of sixty minutes for each sample, and thoroughly examined the network traffic data for malicious behaviors. The evaluation results show more than 95% of accuracy of detecting various ransomware attacks.

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Section: Experimental Resultssupporting

confidence: 90%

“…Zahoora et al [ 27 ] proposed a novel Deep Contractive Autoencoder based Attribute Learning (DCAE-ZSL) system. The projected method was able to efficiently discover code insertion that can study the semantic depiction of zero-day attacks in an unsubstantiated style.…”

Section: Related Workmentioning

confidence: 99%

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Tariq

Ullah

Uddin

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Zahoora et al [ 23 ] successfully deployed a sophisticated heterogeneous voting ensemble named DCAE-ZSL-HVE, leveraging the capabilities of Contractive Autoencoder (CAE) for the detection of zero-day ransomware attacks. They achieved an impressively high recall value of approximately 0.95.…”

Section: Related Studiesmentioning

confidence: 99%

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Chung,

Lee

2023

Sensors

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Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.

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“…In the related works section details of voting works will be discussed. Some applications concentrated on voting to present a solution to some problems [120]- [128].…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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Zero-day Ransomware Attack Detection using Deep Contractive Autoencoder and Voting based Ensemble Classifier

Cited by 35 publications

References 48 publications

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

An Effective Self-Configurable Ransomware Prevention Technique for IoMT

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Ensemble learning based on relative accuracy approach and diversity teams

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