Resolving Security Issues in the IoT Using Blockchain
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
4. Implementation
4.1. Client-Side Implementation Code
4.2. Server-Side Implementation Code
4.3. GUI Implementation Code
Algorithm 1: Entry of the data |
Server While(1) i = 0, data = null createsocket() communicateSocketAddress() listeningIncommingConnection() ConnectClient() while(i < no. of client) { Data = listen (client [i]) If data ≠ Ø Generate hash() J = 0 While(i < no. of clients) If(i ≠ j) Share(data, client[i]) Exit() } Client While(1) { createClientSocket() ConnectServer(IP, port) Save(data) Share(data) } closeConnection() |
- Server
- Create a socket
- Share the socket address and continue to watch for incoming connection requests
- Link to client
- Data received
- Create a hash for each received string
- Share the received data with every other connected node
- Repeat steps 5 and 6 as desired by the user.
- Exit.
- Client/Nodes
- Make a distinct client socket for each node.
- Using the provided socket address, connect to the server (IP and port)
- Data sent and received.
- Repeat step 3 as configured.
- Close connection.
5. Analysis and Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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IoT | 2018 | 2024 | CAGR |
---|---|---|---|
Wide-area IoT | 1.1 | 4.5 | 27% |
Cellular IoT | 1.0 | 4.1 | 27% |
Short-range IoT | 7.5 | 17.8 | 15% |
Total | 8.6 | 22.3 | 17% |
Sr. No. | Security Problem | Result | Damage Layer | Proposed Solution |
---|---|---|---|---|
1 | Jamming entities | Denial of service | Physical | Determining signal strength, calculating packet delivery ratio [14], encoding packets and change in frequencies [15]. |
2 | Insecure initialization | Denial of service and compromised the privacy | Physical | Establishing data transmission rates between nodes [16] and inserting fake noise [17]. |
3 | Sybil and spoofing attacks | Denial of service and network disturbance | Physical | Signal strength capacities [18], and channel evaluation [19]. |
4 | Insecurity of physical level | Denial of service and privacy compromised | Physical | Preventing software access to USB and preventing testing/debugging devices [20]. |
5 | Sleep deprivation | Energy expenditure | Link | Multi-layer interruption detection system [21]. |
6 | The duplicate attack caused by fragmentation | Denial of service and disturbance | Network | Insertion of timestamp for protecting against replay attacks [22], and fragment authentication through hash [23]. |
7 | Vulnerable neighbor discovery | IP spoofing | Network | Elliptic Curve Cryptography (ECC)-based signatures [24]. |
8 | Attack on RPL routing | Man-in-the-middle attack and monitoring | Network | Hashing with signature-built verification and observing node behavior [25]. |
9 | Wormhole and sinkhole outbreaks | Denial of service | Network | Rank verification through a hash function, trust level supervision, communication behavior evaluation of nodes, anomaly finding through IDS, and measuring signal strength [26,27,28,29]. |
10 | Intermediate layer Sybil attack | Privacy breach, spamming, Byzantine errors, inaccurate broadcast | Network | Random social graphs, keeping lists of trusted/un-trusted users by analyzing user behavior [30,31]. |
11 | Authentic and secure communication | Privacy breach | Network | Compressed AH, IACAC using Elliptic Curve Cryptography, symmetric homomorphic mapping and distributed logs [32,33,34]. |
12 | Transport layer level security | Privacy compromised | Transport | DTLS-PSK with nonces, AES/SHA algorithm-based DTLS cipher, compressed IPSEC, and AES/CCM-based security [35,36,37,38]. |
13 | Session establishing and renewal | Denial of service | Transport | Verification with a prolonged secret key, and encryption-based symmetric key [39]. |
Techniques | Proposed Solutions | Challenges |
---|---|---|
Data Provenance | Provenance is a phenomenon in which the origin of the data along with the subsequent changes is traced in order to ensure the precision of data. Data provenance is about the creation of the propagation of the data process and where that data is serving, so this can be used in IoT to ensure confidentiality and integrity of data [40]. | A major challenge faced when implementing data provenance in IoT is that it is compulsory to also secure provenance data. An insecure data provenance means the exposure of sensitive data to unauthorized third parties [40]. |
Blockchain | Using a distributed, decentralized, shared ledger that is accessible to all parties, blockchain will make it possible to share important relevant data collected from the IoT [41]. | Scalability and availability of blockchain [42]. |
Fog Computing | Fog computing’s primary function is to locally manage the data produced by IoT devices for better administration, necessitating an architecture made up of various layers. The fog–device framework and the fog–cloud–device framework are two of their frameworks. The device and fog layer make up the former structure, whereas the device, fog, and cloud layer make up the latter framework. Layers are organized according to their capacity for storing and processing information. Wired or wireless communication is used for layer-to-layer communication [43]. | Fog computing inherits the security and privacy issues of cloud computing. The collection, transmission, processing, and exchange of users’ sensitive data make privacy a crucial concern in fog computing. Owners of data are reluctant to reveal their privacy to outside parties, but privacy leakage is unheeded [43]. |
Edge Computing | Edge computing, which places a small edge server between the user and the cloud or fog, is utilized as a solution to the issues with cloud computing. Instead of the cloud, some processing is undertaken at the edge server. The components of the edge computing architecture are edge devices, cloud servers, and fog nodes [44]. | Data security and user privacy are the two key issues with edge computing. The private information of a user may be exposed and used inappropriately if a home equipped with IoT devices is the target of cyberattacks [44]. |
Machine Learning | In order to prevent data loss or other problems, the purpose of machine learning is to apply and train algorithms to detect anomalies in IoT devices or to detect any undesired activity taking place in IoT systems [45]. | Raw data of IoT devices cannot be processed by machine learning algorithms. Machine learning requires data to be classified and clustered. The network of IoT devices produces huge amounts of data and, before it is processed by machine learning algorithms, it needs to be cleaned and preprocessed accurately. Failing to do so will result in producing “garbage” data [45]. |
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Malik, H.A.M.; Shah, A.A.; Muhammad, A.; Kananah, A.; Aslam, A. Resolving Security Issues in the IoT Using Blockchain. Electronics 2022, 11, 3950. https://doi.org/10.3390/electronics11233950
Malik HAM, Shah AA, Muhammad A, Kananah A, Aslam A. Resolving Security Issues in the IoT Using Blockchain. Electronics. 2022; 11(23):3950. https://doi.org/10.3390/electronics11233950
Chicago/Turabian StyleMalik, Hafiz Abid Mahmood, Asghar Ali Shah, AbdulHafeez Muhammad, Ahmad Kananah, and Ayesha Aslam. 2022. "Resolving Security Issues in the IoT Using Blockchain" Electronics 11, no. 23: 3950. https://doi.org/10.3390/electronics11233950
APA StyleMalik, H. A. M., Shah, A. A., Muhammad, A., Kananah, A., & Aslam, A. (2022). Resolving Security Issues in the IoT Using Blockchain. Electronics, 11(23), 3950. https://doi.org/10.3390/electronics11233950