Securing IoT Networks: A Post-Quantum Blockchain and Deep Learning Approach for Enhanced Cyber Defense

The term AIoT (Artificial Intelligence of Things) encompasses the Internet era that has dramatically changed people’s lives. This ecosystem ranges from consumer electronics IoT to industrial IoT, smart home appliances, healthcare, smart cities, and transportation devices, all of which are well-connected and capable of intelligently communicating. As this enforces creativity and convenience, it also invites the problem of security. With billions of IoT devices expected by 2020, the risk vector has significantly increased, imposing safety risks and thus requiring effective risk mitigation strategies. Therefore, conventional security solutions have been seen to be inadequate when facing today’s and tomorrow’s complex forms of cyber threats. This is made more complex by the fact that IoT devices have to run on lower privileged hardware resources, including computation and memory; hence, traditional security protocols will not work. Providing such massive and heterogeneous network demands solution approaches different from conventional networking solutions due to the inherent limitations of IoT networks. As much as machine learning and artificial intelligence put tremendous pressure on IoT, quantum computing remains one of the most formidable threats to the security of IoT. These make classical cryptographic algorithms such as the RSA and ECC susceptible to attack by quantum cryptography. This highlights why there is a need to come up with better post-quantum cryptographic solutions that will enable IoT device security against such threats. One of the most exciting solutions for the IoT security challenge is blockchain technology which provides safe and reliable communication and data protection. Thus, blockchain reduces the number of single points of failure while keeping the messages secret. But there are definite drawbacks of this method, including, but not limited to, scalability and how resource hungry the technology is. Proof-of-concept implementations found in IoT devices are still distant from implementing hardened blockchain solutions that would be capable of resisting quantum attacks, which is thus of paramount importance to develop lighter, efficient protocols. Artificial intelligence, with specific reference to deep learning, is one of the brilliant approaches to solving the troubles with cyber threats and recognizing the underlying patterns. IoT security systems can then use a deep learning model to study the behavior of the system, analyze the traffic, and gain insights on prompting early detection of threats. Iteratively integrating blockchain and deep learning for IoT security results in a powerful real-time system that can handle sophisticated security threats. The developed project incorporates a post-quantum blockchain and deep learning to build an IoT security model. The second part of the blockchain component addresses the post-quantum demand and establishes the cryptographic strongness of IoT networks against quantum attacks and interferences. At the same time, the deep learning component is mostly prevention-oriented as it seeks to train models for normal network traffic, allowing it to immediately identify traffic patterns that may be signs of threats. This combination solves the cryptographic threats caused by quantum computing and improves the security of IoT systems against new-generation security threats. When implemented, the proposed framework will enable post-quantum cryptography to protect data and integrate threat intelligence into deep learning, thereby creating an optimized IoT ecosystem. Although issues like scale and resource constraints remain present, there are also opportunities, as embedding these sophisticated technologies offers a way of addressing these challenges and enhancing the reliability of IoT networks. To this end, this study points to the necessity for continued growth and the advancement of new approaches to security systems that are suitable to the current and future reality of ever-changing threats in cybersecurity and for IoT-specific requirements. Our research objective is the following:

• To build a viable IoT security model incorporating post-quantum blockchain to mitigate quantum-influenced cyber hazards in IoT but rare with IoT devices’ limits on space and computing power.
• To improve the effectiveness of the proposed deep learning-based methods for threat recognition in IoT networks by dynamically detecting suspicious actions in a network in real-time to prevent probable cyber threats.

Organize the paper as follows: Section 2 to describe the related work; Section 3 to proposed methodology; Section 4 to results analysis; Section 5 to conclusion, and future work.

In a bid to protect IoT networks, post-quantum cryptography with blockchain tech and deep learning have been proposed. This approach presents an abstract structure that aims to enhance cybersecurity on IoT ecosystems, using powerful cryptographic technologies for reliable [14]. identification and insulation of authorized entities from erroneous ones, combined with record-immutable techniques (e.g., blockchain) and intelligent anomaly detection. The first step of this approach deals with the incorporation of post-quantum cryptographic algorithms. Since quantum computing is evolving, traditional encryption methods become vulnerable risking the security of IoT networks [15]. Therefore, to mitigate this problem the method advocates for first creating post-quantum cryptographic schemes of quantum inaccessibility. These will be top-level algorithms-lattice-based, hash-based cryptographic methods for securing the IoT data transmissions → storage within an IoT network. This period consists of choosing proper post-quantum cryptographic algorithms and integrating them with already existing IoT infrastructure so that integrity and confidentiality remain protected even during quantum computing progression. As a solution to the complexity, we proposed our methodology for post-quantum cryptography and further integrating blockchain technology into IoT network transactions to aid in providing immutability and transparency. Figure 1 shows the Basic steps of cybersecurity in IoT ecosystems.

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Figure 1. Basic steps for cybersecurity in IoT ecosystems

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Figure 2. Working on the proposed system

Figure 2 shows the working of the proposed system. The distributed ledger that is inherent in blockchain ensures a transparent and impossible-to-hack record of all transactions or interactions across the network. This is important for secure and verifiable data exchange between IoT devices. The approach being considered or the one that is almost finalized involves setting up a private/consortium blockchain customized just for the IoT network only [16]. Security policies like access controls and data validation checks will be automated using Smart contracts. By recording blockchain transactions, this methodology tries to accomplish trust and accountability among network participants by producing an immutable audit trail. The third phase of the methodology uses Deep learning methods for more complex types of anomaly detection with threat mitigation. The network traffic will be monitored using deep learning algorithms, especially neural networks, to detect security threats as closely as in real-time scenarios. It is a way to train deep learning models with historical network data and help the model automatically through such identifiable patterns of malicious activity or anomalies. These models will be pushed down to specific parts of the IoT network (such as gateways and edge devices) to continuously monitor and detect threats. By capitalizing on deep learning's pattern recognition and adaptable nature, the method aims to increase how well a network is able to both detect and adapt when faced with new threats. In order to evaluate the validity of the proposed approach, a multi-tiered evaluation framework will be utilized. The images show a pipeline that features simulation, testing, and validation stages. During the simulation phase, synthetic data and attack scenarios will be used to benchmark post-quantum cryptographic algorithms, blockchain incorporating cryptosystems that make use of deep learning [17]. The next step for testing would be to deploy it in a controlled environment and track its results on performance/network security. Finally, real-world case studies and pilot projects will validate from all angles possible to ensure that the proposed method addresses practically existing security needs where proven operational requirements are met. You will need continuous monitoring and iterative refinement while implementing this methodology. Since IoT networks are constantly changing and there will be new threats attacking on a daily basis, this model should learn to adapt to the day-in-day-out changes happening in an organization. These will seed feedback loops that learn from network operations and security incidents to continuously enhance cryptographic algorithms, blockchain protocols, and deep learning models.

Proposed System Architecture

In Figure 3, a new mechanism for blockchain blockchains (quantum-resistant) is secured in post-quantum using deep learning.

This type of system architecture is aimed at strong cybersecurity protection and the effective management of large amounts of data while implementing a new mechanism for blockchain blockchains (quantum-resistant) secured in post-quantum using deep learning. Architecture starts with IoT devices, which are assets equipped with sensors and actuators that collect sensor data/information from their environment [18]. The data is sent to a gateway for basic data filtering and analytics. The device gateway is an intermediate hop that forwards collected data to a cloud server. A so-called cloud server for data storage, processing and advanced analytics. In architecture, this is very important hence the security module. This includes post-quantum cryptography to address future quantum computing issues, making all communication and data exchange completely secure. The security module also uses blockchain technology to ensure the immutability and decentralization of a ledger for safe transactions, increasing data integrity and transparency. This data is analyzed through deep learning models that make it possible to even detect some potential threats in real-time. A user interface connects the cloud server and security module together, offering a holistic visibility dashboard. The interface permits users to receive signals and alarms, control their safety standards, to set up the overall security strategy [19]. The user interface allows administrators to monitor IoT network health, drill in on analytics, and respond quickly as new incidents arise.

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Figure 3. New mechanism for blockchain blockchains (quantum-resistant) secured in post-quantum using deep learning

Equations

Step 1.1: Post-Quantum Cryptographic Security for Blockchain Transactions

${{E}_{PQ}}~\left( {{T}_{i}} \right)=PQEncrypt\left( {{T}_{i}},{{K}_{PQ}} \right)$

• ${{T}_{i}}$ represents the transaction data.
• ${{K}_{PQ}}$is the post-quantum cryptographic key.
• ${{E}_{PQ}}~\left( {{T}_{i}} \right)~$is the encrypted transaction using a post-quantum cryptographic algorithm.

Decryption Function:

${{D}_{PQ}}\left( {{E}_{PQ}}\left( {{T}_{i}} \right) \right)=PQDecrypt\left( {{E}_{PQ}}\left( {{T}_{i}}\right),{{K}_{PQ}} \right)$

Step 1.2: Blockchain Integrity with Post-Quantum Cryptography

Blockchain Hashing:

${{H}_{PQ}}\left( {{B}_{I}} \right)=PQHash\left( {{B}_{i}} \right)$

• ${{B}_{i}}$ represents a block in the blockchain.
• ${{H}_{PQ}}\left( {{B}_{I}} \right)$ is the post-quantum secure hash of the block.

Step 1.3: Deep Learning-Based Anomaly Detection in Blockchain Networks

Training Phase:

$D{{L}_{model}}=Train\left( D{{L}_{model}},{{D}_{train}} \right)$

• $D{{L}_{model}}$ is the deep learning model
• ${{D}_{train}}$ is the training dataset.

Anomaly Detection:

$A\left( {{t}_{i}} \right)=D{{L}_{model}}\left( {{t}_{i}} \right)$

• ${{t}_{i}}$ represents real-time transaction data.
• $A\left( {{t}_{i}} \right)$ is the anomaly score or classification output from the deep learning model.

Alert Generation:

   $Alert\left( {{t}_{i}} \right)=~\left\{ \begin{matrix}   True,~~if~A\left( {{t}_{i}} \right)>Threshold  \\   False,~~otherwise  \\ \end{matrix} \right.$

• An alert is generated if the anomaly score $~A\left( {{t}_{i}} \right)$ exceeds a predefined threshold.

Step 1.4: Integration of Post-Quantum Security and Deep Learning

Secure and Intelligent Transaction:

$T_{i}^{secure}=Encrypt ( {{T}_{i}},{K}_{PQ} ),A\left( {{T}_{i}} \right)<Threshold$

• $T_{i}^{secure}$ is a transaction that is both encrypted using post-quantum cryptography and verified as non-anomalous by the deep learning model.

Overall System Security Evaluation:

${{S}_{overall~}}=f\left( {{E}_{PQ}},{{H}_{PQ}},D{{L}_{model}},A\left( {{t}_{i}} \right) \right)$

• ${{S}_{overall~}}$ represents the overall security score or evaluation function that combines post-quantum cryptographic encryption, blockchain integrity, and deep learning-based anomaly detection.

Step 1.5: PQDL Chain

$PQ{{B}_{-}}DL={\mathop \sum }_{n=1}^{{N}}\,.\left[ {{S}_{n}}=\left( PQC\left( {{x}_{n}} \right)+{\mathop \sum }_{t=1}^{{T}}\,B\left( t \right).R\left( n,t \right) \right)+{\mathop \sum }_{y=1}^{{Y}}\,DL\left( yn \right).A\left( DL\left( yn \right) \right) \right]$

• PQC(x): Post-Quantum Cryptographic function applied to data xxx.
• B(t): Blockchain function for transaction ttt, incorporating PQC for secure transactions.
• DL(y): Deep Learning model applied to input data yyy for anomaly detection and prediction.
• S_n: Security level at network node nnn, considering both PQC and DL.
• R(n, t): Record function for transaction ttt at node nnn on the blockchain.
• A(DL(y)): Anomaly detected by the DL model.
• U(t): Update function for the blockchain-based on anomaly detection.

Step 1.6: Post-Quantum Cryptography-Encryption Strength:

 $E=P\times K$

1. Blockchain - Hash Function Output:

$H=Hash\left( T \right)$

2. Blockchain - Transaction Verification:

$V=Verify\left( T,H \right)$

3. Deep Learning - Anomaly Detection Output:

 $A=f\left( X \right)$

4. Post-Quantum Cryptography - Data Integrity Check:

$I=Hash\left( E \right)$

Proposed algorithm 1.1

This research initiates a new Blockchain and deep learning-based method for IoT networks in post-quantum networks that could detect/categorize the DoS attacks at the pre-level of execution, thus empowering both cyber defense services acting as an additional information source to implement robust countermeasures. The long-term solution is to expose Post-Quantum Cryptography algorithms that can be used for ensuring data transportation and storage, starting with Psqqra. This means that known cryptographic algorithms may need to be replaced, which cannot be decrypted by a "classical" computer but require a future quantum machine. Begin by analyzing and implementing the use of post-quantum cryptographic algorithms across your IoT ecosystem to ensure data can securely travel in a quantum-safe manner. This will help the IoT transactions by making them impervious, transparent, and immutable using blockchain. This method makes a record of all transactions over the whole network on an immutable private or consortium blockchain.

Proposed algorithm 1.2

Proposed algorithm 1.3

Proposed algorithm 1.4

Proposed algorithm 1.5

Flow Chart

Modern defence in depth for IoT networks using Blockchain Post Quantum and Deep Learning Techniques are represented on a flowchart shown in Figure 4. Before wrapping up, do you know how IoT network exposure searches for it? To this end, we need to install post-quantum cryptography after vulnerability analysis, deploy blockchain technology for secure transactions, and use AI in deep learning.

Models of anomaly detectors. When we combine these non-ingrained ways, Raghu can bypass them for his cyber hacking. They perform frequent security audits to ensure that their network is safe when there are no remaining vulnerabilities. The network needs to be monitored, and deep learning-based defenses should be provisioned if anomalies are detected. That process continues in case the monitor task has still not identified any peculiar activity. At the highest level, organizations would need advanced threat intelligence systems and updated security policies to defend against new threats challenging their existing defenses.

With the growth of IoT devices advancing at a very high rate, IoT industries and human life have been transformed by IoT in smart homes, healthcare, industrial IoT, and intelligent city applications. But with this vast interconnected ecosystem comes the unprecedented security risks that come with billions of internet-connected devices that have exponentially expanded the attack surface area for threats. Most of these threats could not be dealt with effectively by conventional security solutions since they are designed mostly for resource rich systems in the IoT. Also, new technological advancement witnessed in quantum computing has exposed classical cryptographic algorithms such as RSA and ECC to quantum attacks.

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Figure 4. Flow chart of the proposed approach

This work provides new solutions to protect Io T networks through the approach of the post-quantum blockchain and deep learning techniques for anomaly detection. The blockchain post-quantum framework allows for preserving confidentiality and data integrity against quantum-attack kinds, and at the same time, it has overcome IoT challenges, including low computer power and memory. The decentralized and tamper-proof nature of blockchain only adds to the level of trust by providing for no single failure point and by offering secure broadcast and exchange of information between IoT devices.

As an extension, the deep learning subfield involves itself in preventing threats, where the model identifies typical network activity and shouldn’t-be-there activity. Incorporating the stalwart aspects of deep learning, this system provides real-time detection and threat handling, including real-time response to new threats that IoT networks face. Integration of these technologies provides a strong defense mechanism that covers quantum attacks and fortifies cybersecurity standards in IoT settings [20, 21].

By overcoming some of the existing Internet security deficiencies and applying a scalable resource-saving approach to secure the IoT networks of the next generation, this work helps to create the basis for their further development.

The exposed approach is the complete solution for IoT vulnerabilities and is the only one that guarantees a return on investment in current and future information security risks. It provides protection against data leakage by combining post-quantum cryptography with blockchain technology, allowing algorithms to learn how to develop efficient entry and exit strategies. To this, the solution will be to use post-quantum cryptography algorithms for quantum computer security in the future. This means that quantum positive cannot access IoT data protected with these methods. This latest approach fortifies the network against post-quantum computational threats. Intelligently so, it is backed by a tamper-proof ledged in blockchain, making It legitimate and transparent to the exchange. Security compliance gets automatically enforced by smart contracts together with private, or consortium blockchains to validate all the data transactions. This immutability and audit trail layer make IoT networks both secure & trustworthy. Deep Learning: Better anomaly detection and instant threat identification. These labelled training data likely reinforce the ability of these neural networks to reliably alert and defend against security breaches across many network patterns. This provides the network it acts like an approach to stay proactive and more resistant in its advance against new threats. As part of future work, researchers should work towards further refining the framework proposed in this paper with the aim of eliminating computational overhead and promoting better scalability for a broad IoT adoption. However, the research work might benefit from the integration of other lightweight cryptographic protocols and more efficient deep-learning models. Furthermore, the real massive-scale experiments and consistent improvements in the system will enhance its efficiency and stability.