Leveraging Deep Learning Models for Ransomware Detection in the Industrial Internet of Things Environment

Al-Hawawreh, Muna; Sitnikova, Elena

doi:10.1109/milcis.2019.8930732

Cited by 31 publications

(21 citation statements)

References 16 publications

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“…Considering the existing ransomware defense research targeting IoT/CPS platforms, only behavioral features, namely, network activities were used in the literature. Network Activity: Network-related features are captured by researchers within the IoT/CPS environment to ind out the communication patterns signifying the presence of ransomware [14].…”

Section: Ransomware Analysis In Iot/cps Platformsmentioning

confidence: 99%

“…The extracted features are used by IoTSDN to train a Naive Bayes classiier with Principal Component Analysis. Via a Set of Behavioral Features: Al-Hawawreh and Sitnikova [14] proposed a DL-based ransomware detection system for the workstations that are used as host machines of Industrial IoT environments. Their system relies on classical and variational auto-encoders to select the most appropriate features from several behavioral features of API calls, registry keys, ile and directory operations.…”

Section: Ransomware Detection For Iot/cpsmentioning

confidence: 99%

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A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

et al. 2022

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In recent years, ransomware has been one of the most notorious malware targeting end-users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial loss of billions of dollars. Numerous studies were proposed to address the ransomware threat, including surveys that cover certain aspects of ransomware research. However, no study exists in the literature that gives the complete picture on ransomware and ransomware defense research with respect to the diversity of targeted platforms. Since ransomware is already prevalent in PCs/workstations/desktops/laptops, is becoming more prevalent in mobile devices, and has already hit IoT/CPS recently, and will likely grow further in the IoT/CPS domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative. In order to fill this gap and motivate further research, in this paper, we present a comprehensive survey on ransomware and ransomware defense research with respect to PCs/workstations, mobile devices, and IoT/CPS platforms. Specifically, covering 137 studies over the period of 1990-2020, we give a detailed overview of ransomware evolution, comprehensively analyze the key building blocks of ransomware, present a taxonomy of notable ransomware families, and provide an extensive overview of ransomware defense research (i.e., analysis, detection, and recovery) with respect to platforms of PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive list of open issues for future ransomware research. We believe this survey will motivate further research by giving a complete picture on state-of-the-art ransomware research.

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Section: Ransomware Analysis In Iot/cps Platformsmentioning

confidence: 99%

Section: Ransomware Detection For Iot/cpsmentioning

confidence: 99%

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…During the past few decades, a wide range of problems, including face recognition, speech recognition, font recognition, fraud detection, and disease diagnosis, have been addressed by machine learning methods [125][126][127][128][129][130][131]. In recent years, machine learning has also been investigated to combat spam, an issue that is expanding to various online applications such as SMS, email, and blogs [132][133][134][135].…”

Section: Literarture Review For Fake Review Detectionmentioning

confidence: 99%

Fake Reviews Detection: A Survey

et al. 2021

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In e-commerce, user reviews can play a significant role in determining the revenue of an organisation.Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions. INDEX TERMSFake review; Fake review detection; Feature engineering, Machine learning; Deep learning.

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“…Network Activity: Network-related features are captured by researchers within the IoT/CPS environment to find out the communication patterns signifying the presence of ransomware [14].…”

Section: Ransomware Analysis In Iot/cps Platformsmentioning

confidence: 99%

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

Oz,

Aris,

Levi

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years, ransomware has been one of the most notorious malware targeting end users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial loss of billions of dollars. Numerous studies were proposed to address the ransomware threat, including surveys that cover certain aspects of ransomware research. However, no study exists in the literature that gives the complete picture on ransomware and ransomware defense research with respect to the diversity of targeted platforms. Since ransomware is already prevalent in PCs/workstations/desktops/laptops, is becoming more prevalent in mobile devices, and has already hit IoT/CPS recently, and will likely grow further in the IoT/CPS domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative. In order to fill this gap and motivate further research, in this paper, we present a comprehensive survey on ransomware and ransomware defense research with respect to PCs/workstations, mobile devices, and IoT/CPS platforms. Specifically, covering 137 studies over the period of 1990-2020, we give a detailed overview of ransomware evolution, comprehensively analyze the key building blocks of ransomware, present a taxonomy of notable ransomware families, and provide an extensive overview of ransomware defense research (i.e., analysis, detection, and recovery) with respect to platforms of PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive list of open issues for future ransomware research. We believe this survey will motivate further research by giving a complete picture on state-of-the-art ransomware research. CCS Concepts: • Security and privacy → Malware and its mitigation.

show abstract

Leveraging Deep Learning Models for Ransomware Detection in the Industrial Internet of Things Environment

Cited by 31 publications

References 16 publications

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

Fake Reviews Detection: A Survey

A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

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