Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology
Abstract
:1. Introduction
1.1. Paper Organization
1.2. Research Contributions
- Virtual network function technology is used with software-defined networking to optimize network architecture and improve the overall network performance. IoT devices obtain faster response times, improved network security management, and high threat detection rates.
- VNFSDN incorporates threat filtration, collection, and decision-driven algorithms to avoid and reduce cyber risks for IoT devices and improve network performance. Furthermore, data integrity for IoT devices is protected by addressing three categories of risks (data deletion, data insertion, and data manipulation).
- Prioritized delegated proof of stake is an entirely novel consensus variant implemented to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to suit the changing demands of the organization, allowing IoT devices to efficiently utilize resources.
1.3. Problem Identification and Significance
2. Related Work
3. Proposed Efficient IoT Cyberattack Depletion Using VNFSDN and 6G
- Threat filtration process for IoT devices;
- Threat-capturing and decision-driven process for IoT devices;
- Modeling of a blockchain-enabled consensus algorithm for cyberattack depletion.
3.1. Threat Filtration Process for IoT Devices Using VNFSDN and 6G Technology
Algorithm 1: Threat filtration process using VNFSDN and 6G technology |
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3.2. Threat-Capturing and Decision-Driven Process for IoT Devices
Algorithm 2: Threat-capturing and -mitigating processes using VNFSDN |
|
- For each historical sample, a vector derived from the efficiency of the CPU, the main memory, and the number of VMs is achieved. All three parts have the same significance level.
- The PM on the cloud calculates the Euclidean distance between the current state and the best settings. The configuration is said to be optimal or acceptable when it is within the range of efficiency accepted by the PM of the cloud server. These settings are stored to meet efficiency requirements.
- The PM also calculates the distance of the total configuration to the total shutdown of the PM state for the cloud.
3.3. Modeling of the Blockchain-Enabled Consensus Algorithm for Cyberattack Depletion
- If the blockchain is operating during the crisis, then = ();
- If the blockchain functions in a secure manner, then = ();
- If the blockchain works under restricted conditions, then = ().
4. Experimental Setup and Results
4.1. Experimental Setup
4.2. Experimental Scenarios
- Scenario 1: A cyberattack is generated for IoT devices in the presence of SDN and a 6G network, and the network availability using the VNFSDN, VNFSDN with a firewall, and competing methods are measured.
- Scenario 2: Different network security measures are evaluated for DDoS attack mitigation and found that the VNFSDN approach is the most effective in improving the packet loss rate for IoT devices.
- Scenario 3: The impact of the VNFSDN approach is evaluated on traffic management in a 6G network by measuring the response to gradually increasing the number of IoT devices or user equipments (UEs) in the network.
- Scenario 4: A high-traffic 6G network is employed with a VNFSDN and PDPoS algorithm to manage the traffic flow for the IoT devices in the presence of potential attacks.
- Scenario 5: The threat detection rate of different security configurations is compared using a network without security, VNFSDN, VNFSDN with a firewall, IDS system, and a firewall.
- Scenario 6: The effectiveness of various network security solutions is evaluated for preventing data insertion, deletion, and alteration attacks by employing the PDPoS algorithm, VNFSDN and 6G technology and other competing methods (HADIDS, DINF, FSIDS, DeepVulSeeker approaches).
4.3. Testing Results
- Network availability;
- Packet loss;
- Response time;
- Average traffic management time;
- Threat detection with traffic intensity;
- Undetected data;
- Malicious threat detection accuracy.
4.3.1. Network Availability
4.3.2. Packet Loss
4.3.3. Response Time
4.3.4. Average Traffic Management Time
4.3.5. Threat Detection with Traffic Intensity
4.3.6. Undetected Data
4.3.7. Malicious Threat Detection Accuracy
5. Discussion
6. Conclusions and Future Work
6.1. Conclusions
6.2. Future Work
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Related Works | Solutions | Characteristics | Limitations |
---|---|---|---|
Rani et al. [20] | Machine learning-basedintrusion detection system forsoftware-defined networking | Predicts network traffic andoptimizes network performance | Requires a large amountof data to train the algorithms |
Ahmed et al. [21] | A survey on networkvirtualization techniques andchallenges | Provides a comprehensive overview ofnetwork virtualization techniques andtheir potential benefits inimproving network performance,flexibility, and management | The proposed solutions maynot be applicable to allnetwork virtualization scenarios |
Wang and Zhao [22] | A survey of mobile edgeComputing for the Metaverse:architectures, applications, and challenges | Reduces latency and bandwidthrequirements and improves networkperformance | Limited processing power andstorage capacity at thenetwork edge |
Karakus and Durresi [23] | Quality of service (QoS)in software-defined networkingsurvey | Prioritizes network traffic andallocates bandwidth more effectively | Complexity in designing andimplementing QoS policies |
Li et al. [24] | BlockCSDN:blockchain-based collaborativeintrusion detection insoftware-defined networking | Decentralized and tamper-proofrecord of network activity andimproved network security | High computational overheadand scalability issues |
Yang et al. [25] | Advances in resource allocationin network function virtualization | Generalized and examined fourtypical resource allocation issuesfor QoS improvement and delaycalculation | The proposed solutions maynot be applicable to allNFV scenarios |
Xu et al. [26] | Hybrid cloud computing:state-of-the-art, challenges,and future directions | Improves the security, privacy,and performance of hybridcloud systems | Requires additional resources andexpertise to implement andmaintain |
Basu et al. [27] | QoS-aware dynamic networkslicing and VNF embeddingin softwarized 5G networks | Offers significant benefits interms of energy-efficientservice delivery, low latency,and optimized network efficiency | The MILP-based optimizationapproach may pose computationalchallenges in larger networkscenarios, and the approachmay require further validationin real-world deployments |
Kim and Kim [28] | VNF placement methodbased on VNF Characteristics | Efficient VNF placement usinginformation about the resources of eachnode, which can lead to improvednetwork performance and resourceutilization | Increased complexity in termsof resource monitoring andupdating, as well as potentialscalability issues if thenumber of nodes and VNFsincrease significantly |
Taniguchfi and Shinomiya [29] | A method of service functionchain configuration to minimizecomputing and network resourcesfor VNF failures | Reduces the computing resourcesrequired for SFC configurationcompared to previous studies,which can lead to cost savingsfor network operators | The proposed method maynot be suitable for alltypes of virtualized networks,and further research isneeded to evaluate itseffectiveness in different contexts |
Yao et al. [30] | Anomaly-based approach forIoT | This approach improves the detectionaccuracy and reduces false positives.The main advantage of this approachis its ability to detect both knownand unknown attacks | Potential for higher resourceconsumption due to running multipledetection methods simultaneously |
Zheng et al. [31] | Network firewall for cybersecurity in an IoT environment | The approach improves securityand reduces the potential for falsepositives. The main advantage of thisapproach is its ability to adaptto changing network conditions | The potential for higher resourceconsumption due to continuousanalysis and rule updates |
Our Work | VNFSDN | Improved network performance andscalability and increased networkefficiency | Increased complexity andmanagement overhead due to theneed for specialized skills andtools to manage the virtualizednetwork functions |
Approaches | Response Time | Packet Loss Rate | Network Availability | Traffic Management Time | Threat Detection Rate |
---|---|---|---|---|---|
(FSIDS) [11] | 0.46 (ms) | 5.0% | 93.31% | 47.2% | 80.23% |
DeepVulSeeker [12] | 0.09 (ms) | 2.5% | 99.63% | 30.0% | 90.05% |
(HADIDS) [26] | 0.28 (ms) | 3.0% | 99.01% | 33.01% | 81.0% |
(DINF) [30] | 0.328 (ms) | 3.5% | 98.72% | 37.6% | 82.62% |
Proposed VNFSDN | 0.08 (ms) | 2.0% | 99.5% | 27.4% | 99.36% |
Approaches | MD Accuracy 1% | MD Accuracy 2% | MD Accuracy 5% | MD Accuracy 10% |
---|---|---|---|---|
(FSIDS) [11] | 97.32% | 96.88% | 94.49% | 93.41% |
DeepVulSeeker [12] | 97.88% | 97.62% | 96.89% | 96.28% |
(HADIDS) [26] | 97.45% | 96.74% | 94.71% | 94.19% |
(DINF) [30] | 97.45% | 96.74% | 95.78% | 93.61% |
Proposed VNFSDN | 99.77% | 99.57% | 99.36% | 99.22% |
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Share and Cite
Razaque, A.; Yoo, J.; Bektemyssova, G.; Alshammari, M.; Chinibayeva, T.T.; Amanzholova, S.; Alotaibi, A.; Umutkulov, D. Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology. Sensors 2023, 23, 9690. https://doi.org/10.3390/s23249690
Razaque A, Yoo J, Bektemyssova G, Alshammari M, Chinibayeva TT, Amanzholova S, Alotaibi A, Umutkulov D. Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology. Sensors. 2023; 23(24):9690. https://doi.org/10.3390/s23249690
Chicago/Turabian StyleRazaque, Abdul, Joon Yoo, Gulnara Bektemyssova, Majid Alshammari, Tolganay T. Chinibayeva, Saule Amanzholova, Aziz Alotaibi, and Dauren Umutkulov. 2023. "Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology" Sensors 23, no. 24: 9690. https://doi.org/10.3390/s23249690