Systematic Review of Blockchain for Malware Detection
Blockchain is a cryptographic-based ledger distributed over the internet to enable transactions among the untrusted and the trusted participants (Taylor et al., 2019). The technology was developed in 2008 and created major inventions such as Hyperledger and Ethereum fabric (Taylor et al., 2019). It is an open distributed ledger resistant to modification, making transactions efficient and variable (Yli-Huumo et al., 2019). The technology is now the intermediary between the electronic voucher systems and fiat currencies. As a result, the use of blockchain technology is constantly under scrutiny by renowned scientific researchers. Also, industry practitioners, developers, and researchers are raising significant interest in the technology on security and trust issues. No doubt blockchain is continuously making an impact and influencing the direction of the global currencies. For instance, blockchain has created a loophole for financially driven cyber –attacks. It has also facilitated the development of illicit dark web marketplaces (Yli-Huumo et al., 2019). Further, the world is witnessing rising cases of denial of service and ransomware attacks (Yli-Huumo et al., 2019). Nevertheless, blockchain is mounted with unique features that are continuously catapulting the interest in smart contracts, the pharmaceutical industry, logistics, and the banking sectors (Taylor et al., 2019). As a result, this paper focuses on the systematic review of blockchain applications with special interests in research covering malware detection. Deeper searches include methods studied before on improving malware detection.
1.2 Research Methodology
The systematic review aims at providing a reproducible and transparent scientific review of blockchain applications focusing on malware detection. Also, special focus examines methods of improving the malware detection process. Two main methodologies are adopted; the PRISMA statement by Moher et al. (2009) and the systematic review process suggested by Briner and Denyer (2012). Overall, the method used in the research applies the following steps.
- Identification of the research
- Selecting the studies
- Assessing the quality of the studies
- Compiling and synthesizing data
1.2.1 Identification of the Research
The following segment discusses how sources were generated, sample, and included in the review.
220.127.116.11 Inclusion and Exclusion Criteria
The inclusion criteria check the subject content features to attain its inclusion in a study (Torres-Carrión et al ., 2018). The exclusion criteria are the features that disqualify a subject for inclusion in the study(Torres-Carrión et al ., 2018). Generally, inclusion criteria mean that the subject contains the desirable characteristics it is in and vice versa. Examples of general characteristics in inclusion and exclusion criteria are subject stage and type of the diseases, ethnicity, race, sex, and age. In the context of the research, the inclusion criteria are based on the following research questions.
- What are the blockchain applications focusing on malware or malware detection
- How is blockchain used to improve malware detection?
For the general search, the above questions form the informing factor. For deeper search, the following keywords used ;
- Malicious software.
- Malware detection
The search process was facilitated by the use of the following search engines.
- Sage Publications
1.2.2 Selecting of the studies
A systematic review of the literature was the most appropriate regarding the research questions. A short timeframe was applied relative to the timelines provided. Google Scholar emerged as the most searched database in the research. The main term used was blockchain and malware. A group of journal articles was produced where relevancy was introduced using the search questions. A set of grey literature was also established that included publications from the institutions of learning and the government. The following is the PRISMA diagram that demonstrates the search and the findings in the review.
1.2.3Assessing the Quality of the Studies
A PRISMA diagram is a systematic review system to illustrate information flow (Xiong et al., 2016). It is a map containing the number of the files identified for the study. It also shows the files excluded in the study by failing to meet the criteria and the files included for meeting the criteria. The above diagram shows that 100 files were identified for the study (Xiong et al., 2016). The files fall under blockchain and malware. The additional recorded identified were over sites that are not very recommended in academic publications. Among the 1000 resources, some files were duplicated, and after synthesis, 100 files were clean. The next step was to screen the files to check whether they fall under the Blockchain applications for malware detection. Only 55 files were identified. The actual studies included in the qualitative analysis were 55 accurate sources. The differentiating aspect was sourced falling over 2010.
PRISMA Flow Diagram
1.2.4 Compiling and synthesizing data
The summary of the qualified data is provided in the table below.
|1.||Homayoun, S., Dehghantanha, A., Parizi, R. M., & Choo, K. K. R.||2019||Propose a mechanism for detecting applications created for malicious actions focusing on online mobile app stores.||Malware- malicious malware applications. Method – For the mobile device’s malware detection, a B2MDF model was created. The model architecture contains the feature extractor, third- party application, detection engine, consortium Blockchain, dedicated private Blockchain, dedicated consortium Blockchain, full access, and read-only access. The model combines the dynamic and statistical analysis to provide integrated solutions for detecting malware and reduces the possibility of a false positive. The malicious mobile applications are detected and blocks before being downloaded by the end-users.|
|2.||Malvankar, A., Payne, J., Budhraja, K. K., Kundu, A., Chari, S., & Mohania, M. Malware Containment in Cloud.||2019||To apply analytics for locating the affected nodes using a smart contract that is based on Blockchain technology. The technology aim at isolating and eliminating the affected zones.||Malware- malware in files stored over the cloud. Method – The codified policy that is programmed using smart contract executes policies to isolate and eliminate malware in files stored over the cloud. It is a real-time method that immediately detects every transaction Blockchain. The Blockchain infrastructure is combined with a provisioning system and analytic network service.|
|3.||Meng, W., Tischhauser, E. W., Wang, Q., Wang, Y., & Han, J.||2018||The author introduces the collaborative intrusion detection system. Also, more discussion on the background of Blockchain technology for intrusion detection.||Malware- data compromised in IP addresses and packet payloads Method -The authors proposed strong cryptography as a security measure against compromising data in packet payloads and IP addresses. Also, information can be secured through a Blockchain box that secures information by allowing the end-user to confirm agreement and ownership.|
|4.||Gu, J., Sun, B., Du, X., Wang, J., Zhuang, Y., & Wang, Z. (2018). Consortium blockchain-based malware detection in mobile devices. IEEE Access, 6, 12118-12128.||2018||A framework of Malware Detection and Evidence Extraction (CB-MDEE) targeting consortium Blockchain is detected. The framework detects malware using the public chain by users and the consortium chain by test members. The uniqueness of that no security detection using the consortium Blockchain was published before.||Malware – detecting in Android platforms. Method – A framework known as CB-MMDE for classifying and detecting malware for Android platforms through Blockchain technology is created.|
|5.||Fuji, R., Usuzaki, S., Aburada, K., Yamaba, H., Katayama, T., Park, M., … & Okazaki, N.||2019||The authors use Blockchain technology to design a system that involves sharing signatures of the suspected malware files. The users share the signatures making it easier to respond to malware.||Malware – anti-virus malware. Method- The users can detect and eliminate malware through the signatures without the mediation of anti-virus. It is a proactive method of responding to increasing cyber insecurities.|
|6.||Wu, B., Li, Q., Xu, K., Li, R., & Liu, Z.||2018||A proposed SmartRetro Blockchain technology.||Malware – detecting malware in installed devices. Method- Due to the growing use of IoT a SmartRetro technology is developed to address the growing insecurities by enabling the consumers to detect new vulnerabilities in the installed devices automatically.|
|7.||Mosby, J. K.||2019||The creation of Intrusion Detection System that uses Blockchain technology by detecting and alerting the user about botnet malware.||Malware – Android mobile devices. Method – Blockchain technology is used for classifying and detecting malware in Android mobile devices through the application of the CB-MMDE created framework.|
|8.||Du, Y., Liu, C., & Su, Z. (2019, April).||The purpose is malicious code detection through a consortium Blockchain framework.||Malware – malware in a software package. Method -The Blockchain technology involves the use of statistical analysis methods to draw data such as function call feature, application feature, permission feature and the software package feature.|
|9.||Hwang, S., & Lee, H. W.||2019||The study aims to venture into the Linux Foundation and assess the integrity and the stability of the function to register normal Android Apps using Blockchain Platform.||Malware- in the forgery of the mobile application. Method – Blockchain technology can be used in assessing the legitimacy of Android-based mobile applications. It is also possible to detect the forgery of the mobile application through designing and implementing verification and discrimination mechanism of Hypberledger Fabric.|
|10.||Qu, C., Tao, M., Zhang, J., Hong, X., & Yuan, R||2018||To analyse the credibility verification of the Internet of Things technology using blockchain. It is made possible by verifying devices important attributes such as the function and the location.||Malware – malware in IoT devices. Method – A credibility verification model is established that intercept communication between IoT devices.|
|11.||Mathew, A. R.||2019||Two main algorithms for blockchain technology.The proof of stake (PoS) and the Proof of Work (PoW).||Malware – configured and encrypted data. Method -Through the use of an algorithm, it is possible to enhance data security through encryption and security configurations.|
|12.||Kouzinopoulos, C. S., Spathoulas, G., Giannoutakis, K. M., Votis, K., Pandey, P., Tzovaras, D., … & Nijdam, N. A.||2018||How blockchain improves security for IoT ecosystems in smart environments.||Malware- smart home installations. method- GHOST- help in smart home installations|
|13.||Alotaibi, B.||2019||The study aimed at studying the current Blockchain technologies used to overcome security limitations posed by the IoT||Malware – malware in messages of online transactions. Method – Use of smart contract validates messages over devices that support online transactions. Blockchain is used in confirming and authenticating data during transit. Blockchain improves dependency on the servers in the IoT applications.|
|14.||Miraz, M. H., & Ali, M.||2018||The aim is to investigate the extent to which blockchain can be applied to improve IoT security.||Malware- Internet of Things devices.
Method-Blockchain enhances IoT’s privacy, reliability, security, and scalability by enabling tracking devices through search engine such as Shodan. Also, the technology enhances reliability by introducing a Single Point of Failure. The use of Cryptographic algorithms enhances security.
|15.||Hang, L., & Kim, D. H.||2019||The aim is to provide integrated IoT through the application of blockchain to enhance technology. The approach applies Hyperledger Fabric, Raspberry Pi devices, and realistic IoT scenarios.||Malware – integration of internet of things devices.
Method- Provides design for a novel approach used to improve data security, identify, and scalability used in the blockchain network.
|16.||Mylrea, M., & Gourisetti, S. N. G.||2018||Provides opportunities of increasing the security of cyber driven supply chain applying the concept of blockchain.||Malware – malware in the cyber supply chain.
Method-Use of a smart contract for executing transactions using blockchain infrastructure. Use of signed distributed ledger to increase optimization and enhance security.
|17.||Moubarak, J., Chamoun, M., & Filiol, E.||2018||To explore viral technology and develop new malware based on Blockchain technology.||Malware- key nodes applications/
Method-A new K-ary malware design was developed using Blockchain technology to identify the key nodes and resolve the malware problems.
|18.||Raje et al.,||2018||The aim is to design a decentralized firewall using Blockchain technology.|| Malware- malware in the decentralized firewall.
Method- A detection engine using neural network Blockchain technology is developed to classify Portable Executable (PE) as either benign or malicious.
|19.||Noyes, C.||2016||The presentation and the implementation of BitAV, an anti-malware environment using Blockchain technology.||Malware –|
|20.||Graf, R., & King, R.||2018||The aim is to evaluate the Blockchain deep autoencoder neural network.|| Malware – malware in dataset workflow
The autoencoder neural network is used for classifying and managing performance and insecurity incidences. The smart contract incident management is used for enrichment, classification, and automatic acquisition.
|21.||Rana, M. S., Gudla, C., Sung, A. H||2019||The development and implementation of the consortium Blockchain network to detect malware in dataset using machine learning models.||Malware- malware in a data set.
Method – use of machine learning method applications that are developed using consortium Blockchain mechanism. To detect security in datasets.
|22.||Tann, W. J. W., Han, X. J., Gupta, S. S., & Ong, Y. S.||2018||The aim is to propose sequential learning of a smart contract that detects malware quickly in a smart contract.||Malware- vulnerability in the bytecode of Ethereum smart contracts.
Method- the introduction of MAIAN blockchain technology to detect the various class of vulnerable present in an invoked private fork of Ethereum.
|23.||Wu, B., Xu, K., Li, Q., Liu, Z., Hu, Y. C., Zhang, Z., & Ren, S.||2019||The introduction of SmartCloud, a blockchain application for detecting security of systems connected in the IoT.|| Malware- IoT systems
Method- used of SmartCloud for high coverage and efficient detection of insecurities posed over the internet of things.
|24.||Kedziora, M., Gorka, A., Marianski, A., & Jozwiak, I||2019||Blockchain applications to detect cheating in online games.||Malware- cheating in online gaming
Method- Blockchain is applied to protect Unity engine and detect unauthorized interferences by the user.
|25.||Patsakis, C., & Casino, F.||2019||Examines the application of InterPlanetary File System (IPFS) for botmaster to employ in managing malicious content.||
Malware- malicious content in file systems.
Method- The IPFS method provides functionality for detecting malicious content in the file systems.
|26.||Firdaus, A., Anuar, N. B., Ab Razak, M. F., Hashem, I. A. T., Bachok, S., & Sangaiah, A. K. (||2018||Introduction of Practical Swarm Optimization method that is a bio-inspired root exploit malware.||Method- use of boosting entailing multiboot, logitboot, realadaboost, and adaboost for detecting malware at the root using machine learning.
Results- there was an accuracy of 93% using Logitboost boosting technology.
|27.||Meng, W., Wang, J., Wang, X., Liu, J., Yu, Z., Li, J., … & Chow, S. S. (2018,||2018||Classification of threat models using blockchain protocols in the internet of things||Methods analysis of blockchains models.
Results- use of a private key in cryptographic digital assets protection.
|28.||Golomb, T., Mirsky, Y., & Elovici, Y.||2018||CIoTA model that uses a lightweight framework for collaborative and distributed malware detection||Method – blockchain technology is used in self-consensus and attestation in IoT devices.
Results- CIoTA’s framework can enhance security in the internet of things devices.
|29.||Husain, S. M. A.||2018||Use of blockchain technology in ledger security acting at anti-virus distribution and detection for man in the middle attacks.||Methods- for updating security in the file signatures.
Results- the files are updated efficiently by preventing man in the middle attacks.
|30.||Raje, S., Vaderia, S., Wilson, N., & Panigrahi, R.||2018||Use of blockchain for decentralized malware detection in the internet of things.||Method- application of blockchain technology in building firewall.
Results- the detection engine classify Portable Executable files as either begin or malicious
|31.||Rana, M. S., Gudla, C., & Sung, A. H.||2019||A consortium blockchain technology for malware detection using machine learning.||Method- application of DREBIN dataset for encouraging initial results.
Results- the application helps reduce costs in the decentralized network by ensuring enhanced security and transparency without a man in the middle attack.
|32.||Sagirlar, G., Carminati, B., & Ferrari, E.||2018||The use of AutoBotCatcher for detecting malware in P2P botnets.||Methods- application of AutoBotCatcher.
Results- ability to perform dynamic and collaborative detection by auditing and collecting internet of things technologies.
|33.||Park, J. H., & Park, J. H.||2017||Analysis of generic blockchain applications for comprehensive security in cloud and internet of things.||Method- blockchain in cloud and internet of things.
Results, use of electronic wallet enhanced by blockchain technology in the cloud.
|34.||HaddadPajouh, H., Dehghantanha, A., Khayami, R., & Choo, K. K. R.||2018||Malware hunting in the internet of things using deep recurrent neural networks||Methods- application of execution OpCodes and recurrent neural networks.
The highest security is achieved by using 2-layer neurons.
|35.||Habtamu, A.||2019||The integration of blockchain technology and machine learning for android malware detection.|| The collection of benign and malicious malware.
Results- hybrid analysis has better functionality in malware detection
|36.||Alexopoulos, N., Vasilomanolakis, E., Ivánkó, N. R., & Mühlhäuser, M.||2017||Collaborative IDSs (CIDSs) approach.||Methods- improving CIDSs using blockchain.
Results- some properties of blockchain are significant in improving consensus and accountability.
|37.||Saad, S., Briguglio, W., & Elmiligi, H.||2019||Review of malware detection methods using machine learning.||Methods- literature review
Results- blockchain behavioural machine learning is set to dominate malware detection.
|38.||Ajayi, O., Cherian, M., & Saadawi, T.||2019||Cooperative intrusion detection system.||Method-architecture for introduction detection.
Results- the technologies leverages blockchain technology, data immutability and distributed ledger technology.
|39.||Tariq, N., Asim, M., Al-Obeidat, F., Zubair Farooqi, M., Baker, T., Hammoudeh, M., & Ghafir, I.||2019||Fog-based blockchain architectures||Method-interconnection of devices using fog-based architecture.
Results- Network monitoring for security and anomalies detection.
|40.||Talukder, S., Roy, S., & Al Mahmud, T.||2019||Blockchain-based distributed framework||Method- customized blockchain for anti-malware database management.
Results- blockchain performs better in the distributed functionality.
|41.||Wang, S., Chen, Z., Yan, Q., Yang, B., Peng, L., & Jia, Z.||2019||Android malware identification using a lightweight framework.||Method-combines machine learning, blockchain technology, and network traffic analysis.
Results- the combined results present 97.8% accuracy in detection.
|42.||Lohachab, A.||2019||Use of wireless sensor networks in the blockchain internet of things security.||Methods- wireless sensor networks.
Results- better security functionality
|43.||Gao, F., Jiang, F., Zhang, Y., & Doss, R.||2019||QuorumChain framework malware detection technology||Method- ensemble learning algorithms and traditional detection methods are combined to form the QuorumChain framework.
Results- the new model is better in Fi-measures, recall, and precision.
|44.||Singh, S. K., Rathore, S., & Park, J. H.||2019||Blockchain application for detecting a threat.||The method-detecting threat in ledger transactions.
Results- the unexpected behavior in forked chains is leveraged.
|45.||Signorini, M., Di Pietro, R., & Kanoun, W.||2019||Vehicular network (SDVN) and blockchain||Methods- the introduction of blockchain applications.
Results- blockchain enables data anonymity and certification of transactions.
|46.||Yahiatene, Y., Rachedi, A., Riahla, M. A., Menacer, D. E., & Nait‐Abdesselam, F.||2019||The introduction of micro-blockchain based on MBID and geographical dynamic intrusion Detection for V2X.||Method- local intrusion detection strategies.
Results the use of Vehicle-to everything V2X blockchain technology handles geographic intrusion and intelligent linking of moving vehicle.
|47.||Abdullah, A., & Hanapi, Z. M.||2018||Use of Software Defined Networking (SDN) for decentralized security architecture.||Method- SDN contains mobile edge, Fog and Block.
Results- SDN functions by frequently analysing and monitoring data traffic on the internet of things technology.
|48.||Liang, H., Wu, J., Mumtaz, S., Li, J., Lin, X., & Wen, M.||2019||Updating firmware using blockchain-based firmware.||Method- application smart contract file for enforcing and ensuring integrity for scanning malicious code.
Results- the application of batch verification the method is effective in enhancing the security for internet of things
|49.||Rathore, S., Kwon, B. W., & Park, J. H.||2019||Use of portable devices and insecure stationary blockchain technology in the internet of things.||Methods- use of Software Defined Networking decentralized security architecture.
Results- the process help in optimizing the performance of internet of things devices by detecting malicious activities.
|50.||Hu, J. W., Yeh, L. Y., Liao, S. W., & Yang, C. S.||2019||Blockchain-based firmware used to update the firmware.||Method- updating firmware using blockchain technology.
Results- use of smart contracts for detecting malicious codes.
|51.||Katragadda, R. B., Ramirez, J., Kumar, G. K., Karipineni, C., Vellanki, S., & Kolachalam, S.||2020||Use of smart and backend contracts for behaviour analysis for implementing and enforcing data.||Method- the use of self-enforced adaptable engine and embodiments directed to configure methods and systems in the processing, sharing and transmitting data.
Results- Leveraging smart contracts functionality help in sharing encrypted validates transactions and improves computational power.
|52.||Fröwis, M., Fuchs, A., & Böhme, R.||2019||Replacing smart contracts with token systems through examination of their bytecode.||Method – use of quantitative data to validate the functionality of ethereum blockchain.
Results- the quantitative data provides a 100% functionality using curated token systems.
|53.||Rahman, S. S. M. M., & Saha, S. K.||2018||To detect malware in android devices.||Method- the comparison of the effectiveness of Stochastic Gradient Descent (SGD), Multi-layer Perceptron (MLP), Random Forest (RF) and Extremely Randomized Tree (ET).
Results – the use of StackDroids provides a 99% detection rate and accuracy.
|54.||Brotsis, S., Kolokotronis, N., Limniotis, K., Shiaeles, S., Kavallieros, D., Bellini, E., & Pavué, C.||2019||Blockchain technology for smart homes dealing with preservation and collection of forensic evidence.|| Method- private forensic database and permissioned blockchain.
Results- the blockchain-based solution provides high-level technology for taking forensic evidence on the internet of things.
|55.||Noreddine Abghour, L. I. M. S. A. D., & FSAC, H. I.||2019||Using blockchain technology to manage android permissions.||Method- the introduction of the ANDROSCANREG framework for analyzing and extracting data requested in android platforms.
Results- the ANDROSCANREG consists of BTCBC that records the recovered permissions in registry and PERMBC used for preparation, validation and analysis of results.
1.3 Analysis and Discussion
The analysis and discussion take shape in response to the main research questions
1.3.1 What are the blockchain applications focusing on malware or malware detection
18.104.22.168 Application of Blockchain Technology in Mobile Phone Devices
The articles show a massive application of Blockchain technology to enhance the security, credibility, and reliability of numerous devices and platforms. A special milestone is witnessed in mobile devices. According to Homayoun et al. (2019), blockchain technology is now applicable in detecting malicious malware attacks in mobile technologies. The technology is known as Blockchain-Based Malware Detection Framework (B2MDF). It is a very important technique used for app store users to prevent downloading of malicious applications. Its workability is on detecting false-positive signs of malicious malware. Du, Liu & Su (2019) strengthened blockchain technology in suppressing malware attacks on mobile devices. The authors affirmed the use of consortium blockchain technology to control and detect the generation of malware that are continuously emerging due to innovation. In this respect, the malicious malware is detected by matching its algorithm with the self-created Aho-Corasick automata algorithm (Du, Liu & Su, 2019). Besides, researchers Hwang & Lee (2019) introduced Hyperledger Fabric Blockchain as a method of identifying counterfeit malware in Android applications. The authors affirm. Android applications’ susceptibility is due to its Java coded language (Hwang & Lee, 2019). It happens when the adversary de-compiles the process and reverse the application leading to repacking vulnerability. Hwang & Lee (2019) introduced Hyperledger blockchain as an immune system to mobile phones that could not adapt to the obfuscation technique.
22.214.171.124 Blockchain Applications in the Internet of Things Technology
Internet of Things is another decentralized technology with a centralized architecture easily prone to malware and the man in middle attacks. Therefore, advanced and high tech solutions will always be needed to integrate and adapt to things technology. As a result, Hang & Kim (2019) proposes blockchain technology as the ideal solution in resolving security, control, and monitoring of the Internet of Things devices. In this respect as a combination of blockchain technologies and the internet of things could help in refining sharing of services and resources by allowing time-sensitive information that flows cryptographically. Mathew (2019) confirms that blockchain technology is emerging as the most applied and studied cybersecurity form. The security is commissioned over the encrypted blocks. It is considered an efficient method since it hampers integration by allowing different blockchains application in defined data. Blockchain is continuously providing data-intensive and decentralized developments in a variety of devices. Qu et al. (2018) provide a framework known as Blockchain Structures. It consists of self-organization and intersecting layers of blockchain that enhance verification and the credibility of the internet of things devices. The integration is very rewarding in inducing efficiency and rapid response over the internet of things platforms. Miraz & Ali (2018) also studied blockchain in the Internet of Things to enhance security and reliability. The authors introduced hashing techniques and cryptographic algorithms as technical applications allowing security over the IoT ecosystems (Miraz & Ali, 2018). Further, the world is witnessing vast integration of smart devices that used heterogeneous communication platform to pass information between devices. Most are connected using Wi-Fi, which is an open-source to cyber insecurity. As a result, learning more sophisticated security measures will contain the constraints and expand the communication with the provided devices. According to Alotaibi (2019), the internet of things security measures can be enhanced by using smart contracts to validate the information flow among the interconnected devices. It prompted the European H2020 research project to develop a Ghost framework that helps secure smart home installations and the internet of things (Kouzinopoulos, 2018). The Ghost architecture is mounted with a data Inspection and Interception layer that helps analyze, aggregate, and gather data (Kouzinopoulos, 2018).
1.3.2. How is blockchain used to improve malware detection?
Blockchain help in mitigating vulnerabilities posed by cyber insecurity. Malvankar et al. (2018) proposed Network Analytics as a strategy to improve malware detection using blockchain technology. Also, malware detection is enhanced by applying blockchain client functionality in blockchain architecture (Malvankar et al., 2018). Moreover, smart contracts’ application is deemed effective in the automation of the recording and execution process of information(Malvankar et al., 2018). Blockchain is easily applied in the intrusion detection domain and enhances the stored data’s security (Meng et al.,2018). The intrusion detection systems offer timely responses and the ability to be integrated into various domains such as the financial and education sector (Meng et al., 2018). Blockchain improves the process of malware detection by sharing the signature of the malware files. The method is very efficient for known malware over the unknown. Therefore, there is a weakness of a high false-positive rate(Fuji et al., 2019). The method work by determining the availability of the signatures in the existing blockchain that worketh by eliminating and calculating the degree of maliciousness (Fuji et al., 2019). According to Wu et al. (2018), SmartRetro is another important platform developed from the blockchain architecture used in improving the process of detecting malware (Wu et al., 2018). It functions by offering comprehensive and consistent detection results. Smart Retro functions by automatically detecting new information in the blockchain applications (Wu et al., 2018).
In conclusion, the Internet of Things technology is set to overtake the future in military and civilian contexts. The internet of things is getting more integrated, raising the level of cyber insecurity. There is a pressing need to study the future security of the internet of things. A challenge to the internet of things security is the lack of datasets in their functionality. However, the blockchain can leverage the situation by deploying a self-fulfilling detecting approach. It is achieved by introducing good firmware through the use of blockchain technology. The goal is to detect the firmware and roll it back. As a result, blockchain is advantageous by providing an authenticated protected edge of computing.
Moreover, blockchain technology enhances data integrity and confidentiality without dependence on access controls. With full encryption is impossible to corrupt the data. As a result, firms are rising to use the blockchain framework to ensure secure private messaging. Private and personal information shared through social media, messaging applications are chats secured by blockchain applications. Besides, firms get the advantage of public key infrastructure that maintains security in websites, emails, and other messaging applications.
Moreover, blockchain architecture provides reliable security control over the entire domain name systems that allows the functionality of PayPal, Twitter, and other services. The technology functions by selecting aside any target that is easily compromised in a set of data. Also, blockchain security is enhanced in malicious attacks dumped diminished denial-of-service that cause service denial in the given resource. Finally, blockchain is the future of anti-virus and internet of things security. It is providing reliable and high-tech security, prompting further application and use in connected devices and communication.
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