Communications on Applied Nonlinear Analysis
ISSN: 1074-133X
Vol 32 No. 5s (2025)
346
https://internationalpubls.com
Optimizing IoT Security with Blockchain: Overcoming Computational
Challenges
Hitesh Gehani1, Shubhangi Rathkanthiwar2, Siddhant Jaiswal3, Arti Buche4
1Research Scholar, Department of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India.
1hkgehani@gmail.com
2Professor, Department of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India.
2svr_1967@yahoo.com
3,4Assistant Professor, School of Computer Science & Engineering, Ramdeobaba University, Nagpur, India.
3jaiswalsj@rknec.edu, 4artibuche@gmail.com
Article History:
Received: 09-10-2024
Revised: 27-11-2024
Accepted: 07-12-2024
Abstract:
The Internet of Things (IoT) has made its way into business and home applications,
allowing for automation, monitoring, control, and analysis. Blockchain-based solutions
use encryption, hashing, permanence, credibility, transparency, trustworthiness, and other
security measures to protect IoT networks from numerous attacks. However, integrating
blockchains involves complex computational tasks such as creating hashes, verifying
hashes, mining blocks, etc, which adds extra computational burden on the system. This
increased computational burden often reduces the quality of service for the system, making
it less suitable for real-time and high-performance applications. To address this issue, this
text introduces an AI algorithm for generating side chains. These side chains offer high
security performance like regular blockchains but are generally less computationally
complex. As a result, they can be used for secure real-time IoT applications.
Keywords: Blockchain, Security, IOT, Attack probability.
1. Introduction
The system will select an IoT application and begin with Keen Contract-based Ethereum blockchain
usage for the application. It Store the data around the blockchain utilized, and its parameters on the
chain itself. The system will analyse the length of the chain, complexity of mining, and traceability
and alter the blockchain algorithm. Use profound nets and other AI procedures to memorize from the
arrange structure and re-configure the blockchain. Perform the chaining and side-chaining based on
the application chosen and assess its execution stack on the system. Apply an AI layer which is able
assess on the off chance that the calculation must alter, and alter the framework parameters
appropriately.
2. Literature Review
While navigating Online Social Networks (OSN) through suggestion engines. In addition, it increases
a number of privacy-related issues. To guarantee the confidentiality and anonymity of user data, two
smart contracts, SCSGI and SCSTI are created [1]. This study explores the use of sketches, such as
Bloom Filter and HyperLog, to identify suspicious accounts without requiring the examination of the
entire blockchain data. reduce the amount of memory used by the detection process by 90%-96% and
reduce the time complexity by 86%[2]. Here author proposes a platform using federated learning and
mailto:svr_1967@yahoo.com
mailto:3jaiswalsj@rknec.edu,%204artibuche
Communications on Applied Nonlinear Analysis
ISSN: 1074-133X
Vol 32 No. 5s (2025)
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private blockchain technology within a fog-IoT network. According to experimental results, the
introduced implementation can effectively preserve a patient’s privacy and a predictive service’s
integrity[3]. As blockchains have different types named public and private blockchains, we
recommend private blockchains be implemented where no anyone can make transactions and mine the
transactions. Patients, doctors, and hospital organizations care for their privacy, and authorized access
to others’ data, is the reason for the choice of the private blockchain[4]. Many proposals are
recommended for advancement of decentralized blockchain applications. The work in [1] uses these
rules to convey a decentralized cross breed on-and-off blockchain dependent on Ethereum-based
shrewd agreements for further developed security and high QoS execution.
Here, diverse IoT applications that use sidechains for high velocity, high straightforwardness, and low
energy are talked about[6] Resource trade can be performed on both permissioned and permissionless
blockchains, however this work proposes a model for the previous one by means of the utilization of
a compelling interruption location framework dependent on AI[7]. This work proposes the utilization
of savvy contracts joined with cross breed blockchain model. The mixture model consolidates
agreement calculations for public and union chains [8]. This work proposes the utilization of such a
simultaneous mining calculation that utilizes repetitive calculation to track down measuring data about
the shrewd agreement [9] The work in this paper proposes the utilization of sidechains for vehicular
organizations, wherein a nuclear cross-chain trade-based administration framework a.k.a. ACSMS is
characterized [10]. An exceptionally issue lenient organization can be practically reached out for
performing activities like resource trade [11], token administration and information provenance [12].
The work in [12] proposes a n/2 shortcoming lenient component utilizing blockchain sharding, wherein
the organization can recuperate information regardless of whether half of hubs are defective.
Arrangement overheads of these numerous chain frameworks should be assessed as far as energy,
postponement and cost required for access, stockpiling, move and token administration tasks. This
expense assessment is done in [13], and should be utilized for any sort of blockchain organization that
utilizes numerous chains for compelling overhead investigation. The work in [14] and [15]
recommends utilization of AI models like Opti Shard, DAG, interleaving and OptChain separately.
These models target assessing ideal size for the sidechains to further develop generally speaking
framework execution while keeping up with undeniable degree of safety in the organization. Here,
specialists have used equal handling on DAGs to additionally lessen postponement of mining,
subsequently further developing framework throughput [16]. Ethereum based shrewd agreements are
utilized alongside heuristics based sharding for dealing with the blockchain [17]. Here, various access
tokens are overseen on various blockchains to improve interoperability [18]. Here, specialists have
proposed the utilization of exchange history for planning information to shards with compelled
memory limits, which permits evacuation of redundancies in the framework [19]. The work in [20]
and [21] proposes conventions for compelling shard portion. Issue identification and expulsion from
blockchain organizations can be performed by powerful shard designation, and trust foundation.
The work in [22] proposes a convention for trust the board to configuration shortcoming open minded
organizations. Mix of these models can help with further developing the adaptation to internal failure
capacities of the framework, and subsequently improving QoS execution for the organization. Here,
examined DAG utilizes verification of-work (PoW) as an agreement calculation for approval [23]. The
Communications on Applied Nonlinear Analysis
ISSN: 1074-133X
Vol 32 No. 5s (2025)
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security execution of these blockchain executions can be additionally reached out by option of
protection upgrade procedures like Garlic steering and onion directing [24]. Here, author proposes a
shortened hashing engineering that assesses security level of square fields, and hashes just those fields
which require significant degree of protection [25] The work in [26] examines distinctive agreement
conventions like Proof-of-Stake (PoS), Proof-of-Space (PoSp), Proof-of-Authority (PoA), and so forth
These conventions can be utilized with the current DAG-based sidechain model to assess its continuous
exhibition [26]. This work proposes the utilization of Two-Phase Cooperative Bargaining Game
Approach to lessen postponement of confirmation through choosing restricted arrangement of
sidechains for block check [27]. This idea is utilized [28] for conveying elite execution and enhanced
security banking applications with sidechains. It is seen that blockchain QoS execution is improved
through the utilization of sidechaining, this work proposes different essential and optional execution
compromises which should be dealt with while planning sidechaining applications [29].
3.Methods
Fig 1: Blockchain security Model
Blockchain is the starting point of the process. Network Monitoring will be performed by the
blockchain network. It Leads to Transaction Analysis and Consensus Monitoring. After computing the
results from Transaction Analysis, it Evaluates transactions for irregularities or inconsistencies.
According to the outcome the Feedback loop is given to Network Monitoring if issues are detected. At
the last step it ensures the network’s consensus mechanism operates correctly. Which Leads to Smart
Contract Audit and again checks smart contracts for vulnerabilities. If issues are detected, leads to
Double Spending Detection and if no issues then proceed to Block Analysis. The Double Spending
Detection is used to identify and prevents duplicate spending of digital assets. The Feedback loop is
then sent to Transaction Analysis and then to Block Analysis for Validating the block data that Leads
to Success upon completion. The Node Monitoring is used to track the health and performance of
network nodes which Leads to Fork Detection. After fork detection the system detects splits in the
blockchain network to maintain integrity and finally it concludes the process.
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Calculation Of Delay:
Time stamp 2(ts2) = Time taken at instant 1. Time taken at instant 2
(D) = (Time stamp 2 (ts2) - Time stamp (ts))
AD = Delay / Len(blockchain)
Where, ts2: Ideal time stamp, D: Delay calculation, AD: Average Calculation, Len(blockchain):
Length of current Blockchain
By adjusting the difficulty target, the Nakamoto consensus protocol helps regulate the rate at which
new blocks are added to the blockchain, contributing to the security and stability of the blockchain
network.
The difficulty adjustment algorithm works as follows:
Target Block Time (Tb): The network sets a target block time, which is the desired time interval
between the creation of consecutive blocks. In Bitcoin, the target block time is approximately 10
minutes.
Current Block Time (Tc): The time taken to mine the last few blocks is measured, and the average
block time (Tc) is calculated.
Difficulty Adjustment: The difficulty target is adjusted based on the difference between the current
block time (Tc) and the target block time (Tb). The adjustment is made approximately every 2016
blocks (approximately every two weeks in Bitcoin).
If Tc > Tb: The network assumes that blocks are being mined too quickly, and the difficulty target is
increased. This makes mining more challenging and, in turn, increases the time it takes to find a valid
block, helping to bring the block time closer to the target.
If Tc < Tb: The network assumes that blocks are being mined too slowly, and the difficulty target is
decreased. This makes mining easier and reduces the time it takes to find a valid block, aiming to bring
the block time closer to the target.
Different types of attacks on security:
Malware Attacks: Malicious software that includes viruses, worms, Trojans, ransomware, spyware,
adware, and more. Malware is designed to harm or exploit computer systems and data.
Phishing Attacks: Social engineering attacks that trick users into revealing sensitive information like
login credentials or personal data through fake websites or emails that appear legitimate.
Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attacks: These attacks overload a
target's server or network with excessive traffic, rendering it unavailable to legitimate users.
Man-in-the-Middle (MitM) Attacks: An attacker intercepts and potentially alters communication
between two parties without their knowledge, allowing them to eavesdrop or manipulate data.
SQL Injection: Attackers inject malicious SQL code into input fields to manipulate or access
unauthorized data in a database.
Communications on Applied Nonlinear Analysis
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Password Attacks: Techniques like brute-force, dictionary attacks, or rainbow table attacks to guess or
crack passwords.
4.Results
Module 1: Design a blockchain based system for different number of blocks.
Number of blocks
Fig 2: Graphical representation of Delay for mining different number of blocks
Module 2: Design a blockchain based system for testing the security of the application If number of
blocks mine are 1000 then results is as shown below, for different value of attack probability.
Fig 3: Graphical Analysis of Delay with attacks and after removing attacks
If number of blocks mine are 2000 then results is as shown below , for different value of attack
probability.
Fig 4: Graphical Analysis of Delay with attacks and after removing attacks
By implementing these steps, blockchain administrators and security teams can enhance their ability
to detect and respond to potential attacks on blockchain security effectively.
Early Detection of Threats: Continuous monitoring and analysis of network activities, transactions,
and consensus mechanisms enable early detection of potential security threats or attacks.
Timely Response: With real-time monitoring and analysis, security teams can respond quickly to
emerging threats, minimizing the impact and reducing the window of vulnerability.
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Protection Against Double Spending: Implementing double-spending detection mechanisms protects
against the fraudulent use of cryptocurrency within the blockchain.
Data Integrity Assurance: Analysing block data ensures that the blockchain's data remains tamper-
resistant and maintains its integrity.
Collaboration and Community Support: Engaging with the blockchain community and collaborating
with other stakeholders fosters knowledge-sharing, helping to collectively address security challenges.
Module 3: Design a blockchain based system for testing the security of the application
Updated
If number of blocks mine are 2000 then results is as shown below , for different value of attack
probability.
Fig 5: Graphical Analysis of Reduced Delay with attacks and after removing attacks
5.Discussion
An improvement in overall security of the IoT based blockchain network. Due to addition of AI, there
might be a reduction in overall complexity of the network, as the network’s dependency on a single
blockchain algorithm will be reduced, therefore the rule satisfaction process of the algorithm (which
is the most complex portion), will be relaxed, thereby reducing the complexity of the network. An
increase in the overall QoS of the secure network
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