RThreatDroid: A Ransomware Detection Approach to Secure IoT Based Healthcare Systems

Iqbal, Muhammad Junaid; Aurangzeb, Sana; Aleem, Muhammad; Srivastava, Gautam; Lin, Jerry Chun‐Wei

doi:10.1109/tnse.2022.3188597

Cited by 16 publications

(17 citation statements)

References 33 publications

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“…We implemented and tested these decoys against multiple sophisticated ransomware samples, managing to detect illegal data staging and prevent exfiltration in nearly all cases during our trials [24]. With average ransomware payments now exceeding $540,000, adopting advanced deception technology is no longer an option but an urgent necessity for organizations seeking to secure their most confidential data [25]. Our results demonstrate that all are at risk, from Fortune 500 giants to municipal bodies [31].…”

Section: Introductionmentioning

confidence: 98%

“…Decoy files planted across networks can act as traps to detect abnormal access attempts and raise early alarms [23]. However, contemporary ransomware variants conduct ex-tensive reconnaissance and often ignore decoys when seeking valuable data [4,24,25]. Moreover, they exfiltrate data before triggering encryption, leaving little window for response once decoys detect anomalies [26].…”

Section: Introductionmentioning

confidence: 99%

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Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Liu,

Chen

2023

Preprint

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This study introduces an advanced decoy file strategy utilizing Generative Adversarial Networks (GANs) to combat data exfiltration ransomware threats. Focused on creating highly realistic decoy documents, the system embeds these across enterprise networks, engaging ransomware threats effectively. It includes real-time monitoring for abnormal interactions and proactive response mechanisms for threat containment. Rigorous testing against various ransomware samples demonstrated high engagement rates and rapid response times, confirming the system's effectiveness. Integration across diverse network types was achieved with minimal operational overhead. This study highlights the system's adaptability in the face of evolving ransomware tactics and underscores the balance between decoy realism and deployability.

show abstract

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Liu,

Chen

2023

Preprint

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“…By utilizing past data gatheblack for a particular activity to automatically optimize performance without iteration, ML may be utilized to enhance the performance of various IoT devices. [15][16][17][18][19][20][21] Specifically, the following points are the primary reasons why ML is significant in IoT applications: • The configurations that IoT systems frequently screen have a dynamic structure. As a result, it is critical to develop IoT systems that can effectively respond to these changes as a whole.…”

Section: Iotmentioning

confidence: 99%

“…The IoT enables the connection of millions of objects, including sensors and mobile phones/devices, to carry out numerous functions. By utilizing past data gatheblack for a particular activity to automatically optimize performance without iteration, ML may be utilized to enhance the performance of various IoT devices 15–21 . Specifically, the following points are the primary reasons why ML is significant in IoT applications: The configurations that IoT systems frequently screen have a dynamic structure.…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of machine learning applications in IoT

Gherbi

Senouci

Harbi

et al. 2023

Int J Communication

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The Internet of Things (IoT) is a network of interconnected smart objects having capabilities that collectively form an ecosystem and enable the delivery of smart services to users. The IoT is providing several benefits into people's lives through the environment. The various applications that are run in the IoT environment offer facilities and services. The most crucial services provided by IoT applications are quick decision for efficient management. Recently, machine learning (ML) techniques have been successfully used to maximize the potential of IoT systems. This paper presents a systematic review of the literature on the integration of ML methods in the IoT. The challenges of IoT systems are split into two categories: fundamental operation and performance. We also look at how ML is assisting in the resolution of fundamental system operation challenges such as security, big data, clustering, routing, and data aggregation.

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“…This process has been fundamental in dissecting the lifecycle of a ransomware attack, from the initial infection vectors to the execution of the payload [6,20,16]. Researchers employing reverse engineering have made notable progress in understanding how ransomware evolves, adapts, and circumvents traditional cybersecurity measures [25,26]. This method has also been instrumental in revealing the multi-stage nature of modern ransomware attacks, which often involve complex processes like key generation and communication with command and control servers [11,29].…”

Section: Ransomware Static Analysismentioning

confidence: 99%

LLaMa Assisted Reverse Engineering of Modern Ransomware: A Comparative Analysis with Early Crypto-Ransomware

Vasconcelos,

Almeida

2023

Preprint

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The evolution of ransomware from crypto-ransomware to sophisticated data theft ransomware presents new challenges in cybersecurity. This study investigates the strategic shift in ransomware tactics, emphasizing covert communication and advanced data exfiltration methods. Utilizing the LLaMa-12B model and IDA Pro for reverse engineering, the research delves into the operational intricacies of contemporary ransomware, contrasting recent data theft variants like AlphV and Black Basta with early crypto-ransomware examples like TeslaCrypt and WannaCry. The findings highlight the necessity for adaptive cybersecurity strategies, incorporating advanced detection systems to recognize ransomware activities. The study underscores the importance of expanding research to a broader range of ransomware samples and integrating AI and machine learning technologies for a comprehensive understanding of these evolving threats. The limitations, primarily the research's focus on specific ransomware samples and the subjective interpretation of the LLaMa-12B model's analysis, are acknowledged. Future research should aim to refine AI-driven techniques and develop standardized analysis frameworks, enhancing the effectiveness of cybersecurity defenses against ransomware.

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RThreatDroid: A Ransomware Detection Approach to Secure IoT Based Healthcare Systems

Cited by 16 publications

References 33 publications

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

Mitigating Data Exfiltration Ransomware through Advanced Decoy File Strategies

A systematic literature review of machine learning applications in IoT

LLaMa Assisted Reverse Engineering of Modern Ransomware: A Comparative Analysis with Early Crypto-Ransomware

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