A Review on Ransomware Attack

Ekta, .; Bansal, Urvashi

doi:10.1109/icsccc51823.2021.9478148

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Enta Nawral et al [26] proposed the neural network model based on perceptron which is used to have the security check in VANET system. In addition, the public key infrastructure (PKI)…”

Section: Perceptron Model (Ann)mentioning

confidence: 99%

A Secure VANET Model for Eavesdropping Attack Prevention

Abbasaghababazadeh

2023

Preprint

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<p>A Vehicular Ad Hoc Network (VANET) technology is becoming one of the most advanced technologies; however, the major barrier it faces is to keep the information secure and to improve its confidentiality. Recently, there have been some methods proposed to keep the information safe and implement the reliable platform for information transmission. However, based on the attacks varieties, and the way they are changing their approach, makes the system still insecure against some certain attacks. To overcome the vulnerability under the eavesdropping attack, we propose a solution to minimize the eavesdropping risk and protect the communication between vehicle and server. The solution can be considered as adding the road side unit (RSU). In fact, RSU is an extra neural network between vehicle and server which does not generate any key; however, it transfers the key from vehicle to the server. In this model, cipher text (encryption) is generated by RSU. In addition, the filters in the convolutional layers are used for encrypting the messages and to protect it against eavesdropping efficiently. We formally verify the security functionality of the solution scheme by training the neural networks based on different parameters. Analysis shows that our proposal has a strong ability to prevent eavesdropping attack.</p>

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“…Enta Nawral et al [26] proposed the neural network model based on perceptron which is used to have the security check in VANET system. In addition, the public key infrastructure (PKI)…”

Section: Perceptron Model (Ann)mentioning

confidence: 99%

A Secure VANET Model for Eavesdropping Attack Prevention

Abbasaghababazadeh

2023

Preprint

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“…With the increasing presence of Android devices and the open nature of the platform, which allows easy app downloads from unofficial sources, the severity of ransomware attacks has reached alarming levels. In such a situation, data recovery can be challenging, and the risk of further attacks and identity theft is increased, leading to a diminished trust in security solutions by users [ 5 , 6 ]. Ransomware attacks commonly focus on specific industries.…”

Section: Introductionmentioning

confidence: 99%

Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis

Albin Ahmed,

Shaahid,

Alnasser

et al. 2023

Sensors

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In today’s digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users’ sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks. Due to the fact that ransomware encrypts user data and requests a ransom payment in exchange for the decryption key, it is one of the most devastating types of malicious software. The implications of ransomware attacks can range from a loss of essential data to a disruption of business operations and significant monetary damage. Artificial intelligence (AI)-based techniques, namely machine learning (ML), have proven to be notable in the detection of Android ransomware attacks. However, ensemble models and deep learning (DL) models have not been sufficiently explored. Therefore, in this study, we utilized ML- and DL-based techniques to build efficient, precise, and robust models for binary classification. A publicly available dataset from Kaggle consisting of 392,035 records with benign traffic and 10 different types of Android ransomware attacks was used to train and test the models. Two experiments were carried out. In experiment 1, all the features of the dataset were used. In experiment 2, only the best 19 features were used. The deployed models included a decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), ensemble of (DT, SVM, and KNN), feedforward neural network (FNN), and tabular attention network (TabNet). Overall, the experiments yielded excellent results. DT outperformed the others, with an accuracy of 97.24%, precision of 98.50%, and F1-score of 98.45%. Whereas, in terms of the highest recall, SVM achieved 100%. The acquired results were thoroughly discussed, in addition to addressing limitations and exploring potential directions for future work.

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“…Ransomware is a type of malware that encrypts files on a device, rendering them and the systems that rely on them inoperable. Malicious actors will then demand a ransom to decrypt the data [1]. Ransomware is one of the most severe cyber threats to businesses and operations.…”

Section: Introductionmentioning

confidence: 99%

CVE Records of Known Exploited Vulnerabilities

Lim,

Lau,

Ming Chan

et al. 2023

2023 8th International Conference on Computer and Communication Systems (ICCCS)

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Understanding vulnerability trends is critical to the risk management process. The goal of this paper is to raise the awareness and inform the users about the Common Vulnerabilities and Exposures (CVE) associated with Known Exploited Vulnerabilities that might affect users of the products by gathering data from the National Vulnerability Database (NVD) and CVE details from 2017 to Present. We built a system using Python and CVE Analyzer, which allow users to search for specific CVE records based on CVE ID, vendors, products, published date and the last updated date and export rows of data based on the filtered keyword. We also analyze the overall frequency of CVE records each year regarding Common Vulnerability Scoring System (CVSS) base metric trends, the vendors, and products with the most ransomware vulnerabilities. Our findings show that the frequency of all ransomware vulnerabilities increased from 2019 to 2021. Also, the number of high CVSS score of CVE records associated with ransomware is higher than those with low CVSS scores. Apple and Microsoft are the top vendors with the most exploited ransomware vulnerabilities, and IOS and Windows 10 are the products with the highest vulnerable versions by Apple and Microsoft. Our system can help users to prevent and mitigate the impact of ransomware attacks using datasets and data analysis.

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A Review on Ransomware Attack

Cited by 8 publications

References 6 publications

A Secure VANET Model for Eavesdropping Attack Prevention

A Secure VANET Model for Eavesdropping Attack Prevention

Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis

CVE Records of Known Exploited Vulnerabilities

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