Decentralized Ransomware Recovery Network: Enhancing Resilience

and Security Through Secret Sharing Schemes

Sijjad Ali

1 a

, Jia Wang

1 b

, Victor Chung Ming Leung

1 c

and Asad Ali

2 d

College of Computer Science and Software Engineering, Shenzhen University, Yuehai Campus, Guangdong, China

College of Electronics and information Engineering, Shenzhen University, Yuehai Campus, Guangdong, China

Keywords:

Ransomware, Cybersecurity Defense, Decentralized Ransomware Recovery Network (DRRN),

Secret Sharing Schemes, Resilience.

Abstract:

Ransomware attacks present multiple threats to individuals such as businesses and organizations, causing

data loss, ﬁnancial stress, and operational interruptions. Traditional measures to mitigate ransomware threats

usually include backups and secure applications. However, these countermeasures may not protect against

sophisticated attacks. The purpose of this article is to explore a decentralized approach for recovering from

multiple ransomware attacks. A decentralized secure approach is employed by the decentralized ransomware

recovery network (DRRN) as a platform for sharing data privacy. Backup and restoration of encryption keys

on shared domains are performed in the event of a ransomware attack. By paying for ransomware, users can

encrypt their ﬁles. Additionally, the technical design of the DRRN and its management, as well as ransomware

attacks are explored in our studies. A hybrid approach is utilized to evaluate its effectiveness and implications

for cybersecurity and data protection. Finally, we assert that our proposed scheme is more secure and effective

in the DRRN environment.

1 INTRODUCTION

In recent years, ransomware attacks have increased

signiﬁcantly 68%, posing a serious threat to cyberse-

curity around the world (Teichmann et al., 2023; Hu-

mayun et al., 2021; Ali et al., 2023). There are several

characteristics of these cyberattacks that can be sum-

marized as the decryption of sensitive data, the lack

of access to legitimate users, as well as the demand

for ransom for the key to decryption. Many instances

exist where businesses, individuals, and even critical

foundations are subjected to severe ﬁnancial losses,

data corruptions, and operational disruptions because

of cyber attacks (Kaﬁ and Akter, 2023). A number

of traditional methods have been used to prevent the

spread of ransomware in recent years. An example of

these would be the need for regular backups of data

and the use of endpoint security software, as well as

incident response protocols (Ilca et al., 2023; Chen

et al., 2021). While these measures may provide some

https://orcid.org/0000-0002-9141-8837

https://orcid.org/0000-0002-0861-2496

https://orcid.org/0000-0003-3529-2640

https://orcid.org/0009-0004-5399-0156

protection, they do not succeed in the face of the in-

creasing complexity and targeted nature of contempo-

rary ransomware attacks. This underscores the need

for more comprehensive and proactive cybersecurity

measures. A proactive approach is crucial against

ransomware attacks. We propose state-of-the-art De-

centralized Ransomware Recovery Network (DRRN)

as a decentralized solution, enhancing recovery pro-

cesses, reducing attackers’ ﬁnancial incentives, and

fortifying data security. DRRN decentralizes control

and utilizes secret distribution schemes, ensuring ro-

bust and permanent infrastructure for data protection

and recovery.

The Decentralized Ransomware Recovery Net-

work (DRRN) utilizes centralized storage approach

and use of secret distribution schemes to encrypt very

highly sensitive ﬁles and keys across networked edge

nodes, to making our data secure. In addition, we ap-

plied decentralized encryption approach to further se-

cure data and reduce risks of loss or damage during

ransomware attacks. The DRRN decentralized stor-

age and encryption keys enhance strength against in-

trusions, however, secret distribution approach like

secret sharing support single-point vulnerabilities.

This decrease unauthorized access within the net-

294

Ali, S., Wang, J., Leung, V. and Ali, A.

Decentralized Ransomware Recovery Network: Enhancing Resilience and Security Through Secret Sharing Schemes.

DOI: 10.5220/0012713500003705

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security (IoTBDS 2024), pages 294-301

ISBN: 978-989-758-699-6; ISSN: 2184-4976

work, enhancing data security against advanced ran-

somware attacks and minimizing failure risks of net-

work.

In a ransomware attack, the Decentralized Ran-

somware Recovery Network (DRRN) utilizes dis-

tributed shares stored across its nodes to dynamically

regenerate the encryption key if it is lost or inacces-

sible. These shares, fragments of the original key,

are reconstructed through secret sharing approach, en-

abling decryption of the encrypted data. This process

allows affected users to regain access to their ﬁles

without needing to fulﬁll ransom demands, ensuring

fast and reliable recovery. Additionally, the DRRN

offers the option to withdraw ﬁles from the decryp-

tion process if both parties agree, providing ﬂexibil-

ity in recovery. By neutralizing the impact of ran-

somware attacks through decentralized key regenera-

tion, the DRRN removes the ﬁnancial incentive for at-

tackers and strengthens network security against such

threats.

In our proposed work, we perform a deep analy-

sis of the Decentralized Ransomware Recovery Net-

work (DRRN), which maintain its technical archi-

tecture, application scope, and practical signiﬁcance.

We explore into decentralization and secret sharing

approaches, In addition, emphasizing their function

in DRRN defensive solutions against ransomware at-

tacks. Besides, we analyze DRRN efﬁciency through

challenges and simulations, assessing its performance

and broader impacts on cybersecurity and data pro-

tection. The goal of our analysis is to provide read-

ers with the knowledge to understand DRRN per-

formance and the potential challenges it encounters

when combating ransomware attacks in this dynamic

cyberspace ecosystem by offering evidence-based in-

sights for informed decision-making.

DRRN is a crucial initiative in ransomware

defense establishing proactive and sustainable ap-

proaches to prevent breaches and protect critical

data. By utilizing a decentralized data distribution

approach, it will build a strong defense against ran-

somware attacks, and the consequent effects will be

mitigated to a certain level. By highlighting the cru-

cial role of innovation, this scheme tends to more ef-

ﬁciently deal with recent hacker upgrades and even

proactively protect the networks from the threats

which may be likely to occur in the future.

2 MAJOR CONTRIBUTIONS

• The DRRN is a decentralized network of nodes all

over the network that uses InterPlanetary File Sys-

tem (IPFS) and other decentralized storage meth-

ods to offer automatic backups of ﬁles that have

been decrypted by ransomware. It ensures data

integrity and removes the risk of data loss or cor-

ruption and single point of failure network.

• The Decentralized Ransomware Recovery Net-

work (DRRN) is divided into multiple contexts by

using secret sharing principles, such as Shamar’s

secret sharing principles, which are distributed

across different nodes in the network, so that no

single entity contributes signiﬁcantly to the man-

agement of the corruption process as a whole.

• When a victim of ransomware attacks is affected

by the Decentralized Ransomware Recovery Net-

work (DRRN), it restores settings in order to

relieve them from the ﬁnancial burden of ran-

somware attacks, thus allowing affected users to

restore their ﬁles without making any tact to the

attackers.

3 DEFINITION OF SECRET

SHARING SCHEME

A secret sharing scheme can be mathematically de-

ﬁned using polynomial interpolation over a ﬁnite ﬁeld

(Shamir, 1979). Let’s denote;

• S as the secret to be shared.

• n as the total number of participants.

• k as the threshold, i.e., the minimum number of

participants required to reconstruct the secret.

A secret sharing scheme involves the following steps;

(1) Polynomial Generation: A polynomial f (x) of

degree k

is constructed over a ﬁnite ﬁeld F such that;

f (x) = a

k−1

+ a

k−2

+ . . . + a

x + a

(1)

(2) Secret Allocation: Each participant is assigned

a unique x value from the ﬁnite ﬁeld F, and their

is obtained by evaluating the polynomial at

that point;

= f (x

) (2)

(3) Distribution of Shares: The shares s

are dis-

tributed among the participants.

(4) Reconstruction: To reconstruct the secret S,

any subset of k shares can be used with polyno-

mial interpolation. Suppose we have a set of shares

{

, s

), (x

, s

), . . . , (x

, s

)

}

. We can construct the

Lagrange interpolation polynomial L(x) as follows;

L(x) =

∑

i=1

· l

(x) (3)

Decentralized Ransomware Recovery Network: Enhancing Resilience and Security Through Secret Sharing Schemes

295

Where l

(x) is the Lagrange basis polynomial

given by;

(x) =

∏

j=1, j̸=i

x − x

− x

(4)

The secret S can then be reconstructed as S = L(0).

4 PROPOSED MODEL

The proposed Decentralized Ransomware Recovery

Network (DRRN) leverages decentralized storage, a

secret sharing scheme, and dynamic key reconﬁgu-

ration to mitigate ransomware attacks. In the DRRN

framework, N decentralized storage nodes are respon-

sible for storing conﬁdential backups of critical ﬁles.

Employing Shamer’s secret sharing scheme with a

threshold T , a private message M is divided into

shares such that T lower shares are required to reset

the key (T ≤ M ≤ N). The key reset process, which

is T -independent, retrieves shares from a subset of

nodes post-ransomware attack. Bernoulli’s principle

is utilized to compute the overall success probability

success

= 1 − (1 − p)

, where p represents the prob-

ability of success for each share. File recovery, facil-

itated by the reset key, ensures data retrieval with a

success probability P

recovery

= P(K) × P

success

However, the mechanism for detecting ran-

somware attacks may not be apparent. In the DRRN,

all data are encrypted by the user. Consequently, even

if an attacker gains access to the encrypted data, their

ability to manipulate or utilize it is severely limited.

The distributed nature of data storage and the en-

cryption mechanism signiﬁcantly mitigate the impact

of ransomware attacks, ensuring robustness, reducing

data loss, and facilitating recovery without compro-

mising data security. This approach effectively mini-

mizes the ﬁnancial impact on both consumers and or-

ganizations. Further details of the proposed model are

explained in Figure 1.

5 PROPOSED SCHEME

To enhance reliability and recovery from ransomware

attacks, we propose a decentralized DRRN approach

that integrates cryptographic principles and mathe-

matical modeling.

5.1 Node Selection Strategy

We selects nodes N for decentralized network backup

and secret sharing based on probabilistic values p

Figure 1: Proposed Model.

considering factors like node location, network capac-

ity, and security level. This aims to maximize redun-

dancy and diversity, reducing data loss risk and en-

hancing network resilience against threats.

5.2 Secret Sharing Process

Shamir’s secret distribution method divides a key K

into M shares, where T is the minimum number of

shares required to reconstruct K. Shares are dis-

tributed among nodes based on probabilities p

. Each

share holds no information about the original key until

a minimum threshold of shares is gathered, ensuring

security against individual node monitoring.

5.3 Probability Modeling for Key

Reconstruction

In the context of key reconstruction within Shamir’s

Secret Sharing scheme, the probability of success-

fully reconstructing a key, denoted by P(K), is intri-

cately linked to the distribution and accessibility of

shares among nodes. Employing binomial distribu-

tions B(M, p) proves advantageous in this scenario,

with M representing the total number of shares and

p delineating the range of access to share edges. As

Shamir’s Secret Sharing scheme distribute M shares

among nodes, the efﬁcacy of edge reconstruction

hinges upon the presence of these shares. The bi-

nomial distribution adeptly captures this essence by

accounting for the minimum number of edges (i.e.,

total shares M required for successful reconstruction

amidst a speciﬁed number of accessible shares. This

algorithmic approach not only facilitates the estima-

tion of success probability in reconstruction using

the available shares but also accommodates poten-

tial access restrictions imposed on each share. Con-

sequently, it ensures a robust level of key security

by systematically assessing the viability of key re-

IoTBDS 2024 - 9th International Conference on Internet of Things, Big Data and Security

296

construction under various share availability scenar-

ios and access constraints.

Data: M, p, availableShares

Result: Probability of successful key

reconstruction

Input : Total number of shares M,

probability of access to an edge of

each share p, number of available

shares availableShares

Output: Probability of successful key

reconstruction

Calculate Probability: probability ← 0.0;

for k ← availableShares to M do

probability ← probability +

k!·(M−k )!

× (1 − p)

M−k

;

end

return probability;

Algorithm 1: Probability Modeling for Key Reconstruction.

5.4 Dynamic Key Reconstruction

Mechanism

In this section, the heuristic key construction mech-

anism is primarily based on the probabilistic distri-

bution of shares. This mechanism prioritizes the re-

turn of shares from nodes with a higher probability

. After detecting a ransomware attack, the device

initiates the release of shares from a ﬁxed number of

nodes. This is aimed at joining the threshold T re-

quired for key reconstruction. This reconstruction’s

performance is set according to different factors and

considering competing users. This process simpliﬁes

and streamlines the initiation of restructuring mea-

sures to reduce internal and external risks to share-

holders.

5.5 File Restoration Process

The importance of this proposed scheme lies in

its ability to optimize the ﬁle restoration process,

denoted P. This functionality extends beyond just

restoration information; it also prevents damages

from data modiﬁcations or updates. By efﬁciently

restoration data, the system not only restores lost

information but also prevents possible changes by

adversary to the data. This aspect of operational

efﬁciency is very critical for a security system as it

reduces the risk of future attacks that can cause more

damage, thereby ensuring revenue and total sys-

tem resilience. As a result, the ability to restoration

data with conﬁdence and accuracy plays a crucial role

Data: p

, Ransomware attack detection

Result: Reconstructed key

Input : Probability distribution of shares p

Ransomware attack detection

Output: Reconstructed key

Key Construction: Initialize priority queue

for each node i do

Add node i to Q with priority p

;

end

Initialize empty list selectedShares;

while not ransomware attack detected do

Remove node with highest priority from

Add shares from this node to

selectedShares;

if threshold T reached then

Break loop;

end

Reconstruction: Reconstruct key using

shares from selectedShares;

Algorithm 2: Dynamic Key Reconstruction Mechanism.

in the stability and effectiveness of security mea-

sures. This strengthens the protection against various

threats.

Utilizing probabilistic modeling approach in de-

centralized ransomware recovery involves statisti-

cally assessing the likelihood of successfully recov-

ering encrypted data. This includes factors like the

availability and reliability of distributed shares, the

chance of each share being retrievable, and the effec-

tiveness of key reconstruction. By evaluating these

probabilities, organizations can allocate resources ef-

fectively, focusing on areas with the highest chance of

successful recovery while minimizing costs. More-

over, this approach enables adaptability to evolving

threats and infrastructure changes, reﬁning recovery

strategies based on real-world experiences.

6 RESULTS ANALYSIS AND

COMPARISON

The data Table 1, presents the different rates of dif-

ferent parameters of the secret distribution scheme,

including share size, threshold, success rate, key re-

construction rate, and success rate.

Decentralized Ransomware Recovery Network: Enhancing Resilience and Security Through Secret Sharing Schemes

297

Data: shares[], num shares

Result: key

Input : Shares array shares[], number of

shares num shares

Output: Decrypted key key

Reconstruct Key: sum ← 0;

count ← 0;

for i ← 0 to num shares do

if shares[i].retrieved then

sum ← sum + shares[i].value;

count ← count + 1;

end

if count ≥ T HRESHOLD then

return sum;

end

else

return −1 ; // Threshold not met

end

Restore Files: if k ey ̸= −1 then

print ”Files successfully restored using

key: key”;

end

else

print ”Error: Key reconstruction

threshold not met”;

end

Algorithm 3: Decentralized Ransomware Recovery Algo-

rithm.

6.1 Probability of Key Reconstruction

vs. Share Size

Figure 2, shows the relationship between the part size

S and the key reconstruction probability P(K) of a se-

cret distribution scheme. In secret sharing schemes,

a secret is divided into different parts and distributed

among the participants. They can reconstruct the orig-

inal secret only if a sufﬁcient number of parts, de-

termined by an intermediate parameter called T , are

collected. The key reconstruction probability, P(K),

predicts the probability of successfully reconstructing

a secret in the presence of a speciﬁed number of par-

titions and speciﬁed cracks. Mathematically, P(K) is

calculated using joint probability, assuming binomial

spreading, to calculate the probability of the occur-

rence of at least T valid segments out of at least S

segments. As shown in the ﬁgure, the x-axis repre-

sents the segment size, which is the number of seg-

ments distributed, while the y-axis shows the key re-

construction probability. Each numerical point on the

ﬁgure represents a speciﬁc segment size and key re-

construction probability. In general, we expect that

P(K) will show an increasing trend as the segment

size increases, indicating the probability of success-

fully reconstructing the original secret with a larger

number of segments, if the old condition fulﬁlls. This

ﬁgure and mathematical model provide us with a re-

source in light of the relationship between part size

and key reconstruction probability.

The probability for a share size of 5 being larger

than for a share size of 7 might seem unreasonable

at ﬁrst glance. However, in certain secret sharing

schemes, the relationship between share size and key

reconstruction probability can vary based on the spe-

ciﬁc parameters and assumptions of the scheme. One

possible explanation for this phenomenon could be re-

lated to the threshold parameter T . If the threshold for

successful reconstruction is lower for the share size

of 5 compared to 7, it means that fewer shares are re-

quired to reconstruct the secret. Consequently, with

fewer shares needed, there might be a higher proba-

bility of successfully reconstructing the secret with 5

shares compared to 7 shares, even though the share

size is smaller. The formula for binomial probability

is typically expressed as;

P(X ≥ T ) =

∑

k=T





· p

· (1 − p)

S−k

(5)

Figure 2: Probability of Key Reconstruction vs. Share Size.

6.2 Probability of Successful Recovery

vs. Share Size

Figure 3, shows the relationship between portion

size (S) and probability of successful recovery

P(recovery) in a secret distribution scheme. In such

a scheme, a secret is divided into different parts. The

original secret can be recovered if sufﬁcient parts, de-

termined by an intermediate parameter, are collected.

Probability of success recovery shows the probability

that the secret can be successfully recovered with a

given part size and crack. Mathematically, we denote

the portion size as S and the probability of success

P(recovery) as the probability of success. The rela-

tionship between these variables can be modeled on a

probabilistic basis. For example, consider the proba-

bility of success in production and distribution of each

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298

Table 1: Collected data from experiments.

Share Size (S) Threshold (T) Success Probability (p) Key Reconstruction Successful Recovery

5 3 0.8 0.85 0.70

7 2 0.6 0.75 0.55

9 4 0.7 0.90 0.80

10 5 0.9 0.95 0.85

12 3 0.8 0.90 0.75

15 6 0.5 0.60 0.45

part. Generally, as the part size increases, the proba-

bility of restoration success decreases due to possible

causes, such as increasing the number of parts to be

taken into account. This is evident in the ﬁgure, where

the x-axis shows the portion size and the y-axis shows

the probability of successful recovery. Each numeri-

cal point on the graph represents a speciﬁc portion

size and its corresponding probability of successful

recovery. Analysis of this ﬁgure and its mathemati-

cal model provides a resource for how the probability

of successful recovery changes with different segment

sizes.

Figure 3: Probability of Successful Recovery vs. Share

Size.

6.3 Probability of Key Reconstruction

and Successful Recovery vs. Share

Size (Combined)

Figure 4, shows a relationship between portion size

S and the probability of secret distribution rearrange-

ment and recovery is shown in Figure 4. It is common

in such schemes to divide a secret into different parts.

An intermediate parameter determines the number of

parts needed to recover the original secret. Reorder-

ing the keys and recovering the secret is highly likely

to be successful, based on the success recovery proba-

bilities. In this case, we assume the partition size and

crack size are speciﬁed. P(recovery) is the probability

of successful recovery, P(K) is the probability of key

reordering, and S is the chunk size. Probabilistic real-

ism can be applied to the relationship between these

variables. The probability that each part will succeed

in production and distribution, for example, is an im-

portant consideration. A decrease in key reordering

and restoration success is generally associated with

increasing part size because of factors, such as the

increased number of part geometries. According to

the graph, the x-axis represents share size, while the

y-axis represents the probability of successful recov-

ery and key reordering. There are numerical points on

the graph that represent speciﬁc segment sizes and the

probability of success for reset and recovery.

Figure 4: Probability of Key Reconstruction and Successful

Recovery vs. Share Size (Combined).

6.4 Comparison

The DRRN model and the SSS method, both intro-

duced in Table 2, are characterized by the robustness

and reliability of our proposed scheme, which excels

among all the listed research approaches in terms of

robustness and reliability. We demonstrate superior

resilience and integrity in our scheme, which is differ-

ent from those used in other articles to address similar

issues. It is evident from the high ratings it received

in both categories. Our approach is more effective

and trustworthy if we use the DRRN model and SSS

method.

6.4.1 Robustness Assessment

• The proposed scheme stands out for its high ro-

bustness level, utilizing the DRRN model and SSS

method, indicating a strong foundation and thor-

ough analysis.

• Notably, it surpasses other studies in robustness,

which range from low to medium levels.

• High robustness suggests a well-deﬁned method-

ology, comprehensive analysis, and reliable ﬁnd-

ings, providing a solid basis for the proposed

scheme’s credibility.

6.4.2 Integrity Assessment

• The proposed scheme also demonstrates high in-

tegrity, aligning with its high robustness level.

Decentralized Ransomware Recovery Network: Enhancing Resilience and Security Through Secret Sharing Schemes

299

Table 2: Comparison of proposed work.

Authors Models Methods Robustness Integrity

teichmann et al.(Teichmann

et al., 2023)

Economic models Analysis Medium High

fadziso et al.(Fadziso et al.,

2023)

Threat modeling frameworks Overview Low Medium

humayun et al.(Humayun

et al., 2021)

Ransomware propagation models Case Studies low High

duong et al.(Duong et al.,

2023)

Resilience models Assessment low High

kaﬁ et al.(Kaﬁ and Akter,

2023)

Risk assessment frameworks Case Studies Medium High

amoah et al.(Amoah and

Steyn, 2023)

Behavioral models Analysis Medium High

ilca et al.(Ilca et al., 2023) Threat intelligence models Analysis High low

chen et al.(Chen et al., 2021) Incident response frameworks Case Studies Medium High

bajpai et al.(Bajpai and En-

body, 2023)

Risk management frameworks Framework low High

Our scheme DRRN model SSS method High High

• High integrity implies trustworthiness, consis-

tency, and transparency in reporting, minimizing

potential biases and ensuring data reliability.

• Compared to other studies with varying levels of

integrity, the proposed scheme maintains a consis-

tent and trustworthy approach, enhancing its cred-

ibility and reliability.

7 SECURITY ANALYSIS

A decentralized cryptographic scheme can be math-

ematically recovered from ransomware attacks by

demonstrating resistance to cyber attacks, ensuring

that combined attackers cannot reset the shared secret

without the necessary information.

Let’s denote:

• n. Total number of participants.

• t. Threshold value (minimum number of shares

required to reconstruct the secret).

• S. Set of shares held by colluding adversaries.

• K. Set of honest participants.

A scheme for defense against closure attacks can be

mathematically modeled as follows.

7.1 Probability of Successful Collusion

Attack

Using these parameters, we calculate the success

probability of attackers who optimally conﬁgure the

secret without the desired crack t. This can be ex-

pressed as calculating the key rearranging probability

that |S| ≥ t given the total number of participants n

and the number of slots t. Using combined analysis,

we can estimate the probability of success rate, by de-

termining the key of |S| probabilities.

7.2 Threshold Attack

In threshold cryptography, the secret is denoted as S,

with derivatives S

, S

, . . . , S

. S is accessible only

when realizations meet a ﬁxed threshold t. Subsets

below t yield no information about S, as they are sta-

tistically independent.

P(S | S

, S

, . . . , S

t−1

) = P(S) (6)

The equation suggests independence between

, S

, . . . , S

and S, ensuring adversaries with gains

¡t gain no info. With knowledge of S entropy and re-

alizations, total probabilities of secret and distributed

components can be estimated. Security relies on

the inability of combined adversaries to breach the t

threshold, ensuring secret protection. Thus, thresh-

old cryptography remains robust against tampering,

forming a strong basis for its functionality.

7.3 Entropy Analysis Attack

We explore deeper into the entropy of the secret re-

arranged by opposing adversaries, which belongs to

them. Entropy, denoted H(S), measures the uncer-

tainty or randomness associated with a random vari-

able, in this case, the rearranged secret S

′

. Consider-

ing the entropy of S

′

, denoted as H (S

′

| S

, S

, . . . , S

we can determine the extent of invisibility in the ar-

ranged secret. Mathematically, this can be expressed

IoTBDS 2024 - 9th International Conference on Internet of Things, Big Data and Security

300

as follows:



′

| S

, S

, . . . , S



≤ H



′



(7)

The fault arises when overlapping shares exist in

rearranged secret S

′

. This constrains the fault range of

′

, preventing oppressive opponents from breaching

absolute secrecy. Mathematical proof shows oppos-

ing adversaries lack meaningful info about the secret,

requiring access to shares.

8 CONCLUSION AND FUTURE

AVENUES

We presented a decentralized ransomware recovery

networks utilize mesh networks and secret sharing

schemes for heightened security and robustness. By

employing layers of security and distributed key man-

agement techniques, our solution effectively safe-

guards sensitive data against ransomware attacks and

malicious sharing. Through rigorous mathematical

modeling and analytics, we have demonstrated its ef-

fectiveness against cyberattacks. In ransomware sce-

narios, our approach ensures data recovery by decen-

tralizing trust and distributing encryption keys among

network nodes.

DRRN future avenues involves enhancing pri-

vacy with ICRC technologies, integrating ML and

human brain algorithms for ransomware detection,

partnering with industries for deployment and assess-

ment. Long-term studies on ransomware risk, adapt-

ing DRRN, and legal tasks are included.

ACKNOWLEDGEMENTS

This work was supported in part by the Na-

tional Key R&D Program of China under Grant

2020YFA0908700, the National Nature Science

Foundation of China under Grants 62073225,

62203134, 61972263, 62072315, the Natural Science

Foundation of Guangdong Province-Outstanding

Youth Program under Grant 2019B151502018,

the Natural Science Foundation of Guangdong

Province under Grant 2021A1515011153, the

Guangdong Pearl River Talent Recruitment Pro-

gram under Grant 2019ZT08X603, the Guang-

dong ”Pearl River Talent Plan” under Grant

2019JC01X235, the Shenzhen Science and

Technology Innovation Commission under Grant

20200805142159001, JCYJ20220531103401003,

JCYJ20210324093808021.

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