The hybrid approach used in this paper aims to improve online application security. Homographic encryption and squirrel search algorithm (SSA) are combined to create this hybrid. The cloud computing model's security improved with the use of this technique. In order to improve the security of cloud data using an encryption method, this study suggests a hybrid algorithm. Utilising encryption methods is primarily done to protect or keep vast amounts of data in cloud. To improve secure data storage in cryptography, the SSA is designed in a cloud computing environment. Performance measures, including uploading time, downloading time, decryption time and encryption time, are measured in MATLAB to test the success of suggested methods. To evaluate the proposed methodology, tweet data is considered in the proposed methodology. The proposed methodology is contrasted with current approaches, like Rivest-Shamir-Adleman (RSA) and ECC-based cryptography, in order to evaluate it.
The evolutions in information and communication technology (ICT) devices have led to the rise in cyber-attacks in network systems. Ensuring cybersecurity in these devices is one of the most critical and complex issues. Besides, with technological improvement, hackers have been introducing complex and malicious malware attacks into the network system, making intrusion detection a complicated task. However, various conventional IDS face severe challenges to discern and counteract such risks. Statistical research and an enhanced autoencoder classifier for the network intrusion detection system are introduced in this paper (NIDS). This work uses an optimised autoencoder feature extraction process to extract correlated features to simplify the classification process. The proposed method is estimated using the NSL-KDD, UNSW-NB15, and CIC-DDoS2019 datasets, and its performance is compared to that of current machine learning algorithms. The results indicate that the proposed AE-NIDS outperforms shallow machine learning classifiers regarding detection accuracy and false positive rate.
Consensus algorithms play the significant role in any blockchain system since it has to prove its performance and security. Hence, there is a need to identify the curbs of various consensus algorithms. The features of several kinds of blockchain systems rely on the consensus techniques. A detailed analysis of the algorithms has been done which might throw the limelight to researchers to devise a novel consensus algorithm. Issues due to the adoption of the consensus and solutions to overcome have been discussed. The main contribution of this work is to provide a deep insight into the consensus algorithms and its properties. There are several metrics considerations for evaluating the performance of consensus protocols. Additionally, the energy consumption of Bitcoin and Ethereum of several countries has also been presented in this work. Finally, the most commonly used consensus algorithm has been analysed along with its variant information.
With the rapid development of smart devices, communication technologies, and network elements, vehicles become more autonomous, more connected, and more intelligent. Internet of vehicles (IoV) is a new area of the internet of things (IoT) equipped with powerful multi sensors platform, connectivity and communication technologies. The communication and the cooperation between vehicles and different IoT objects can increase security risks and make vehicles more vulnerable to serious attacks (authentication attacks, confidentiality attacks, availability attacks, routing attacks, and data authenticity attacks). In this paper, we propose security schemes to detect and prevent attacks in IoV system using software defined network (SDN) and elliptic curve cryptography (ECC); first, we identify IoV concepts, then we design a network model which illustrates the global IoV architecture, after that we propose an authentication and confidentiality schemes. Finally, we provide formal and informal proof to demonstrate that our schemes can resist against various attacks.
In new technologies developing tremendously, and with this, the usage of online commodities has also seen a surge with a drastic daily increase in the number of users. Online support has also led to increased online attacks on the user's confidential data, leading to financial and social loss. Phishing is one such online attack using which an attacker impersonates any authentic organisation to deceive and take advantage of users to steal various private information like passwords, payment card details, etc. These days, attackers have easy access to new tools and techniques which can easily evade many existing anti-phishing techniques. To enhance the security of the user's confidential information and protect them from such phishing attacks, this paper proposes a new approach that uses visual cryptography and steganography. This approach will help the user keep its identity and login credentials confidential unless the website proves its trustworthiness.
The internet of things (IoT) employs a cloud network, and the data stored in the cloud servers are highly vulnerable to various attacks. As per the current analysis report, around 23% of IoT devices are prone to attack. The data stored in the cloud storage are highly vulnerable to attacks leading to a pullback factor of 15% in economic growth. Considering the above security of the IoT devices, this paper proposes a framework integrating the Jaya algorithm and genetic algorithm to achieve an optimal detection of intrusion in the IoT network. The JA is a parameter less algorithm that does not require any precise control parameters. In contrast, the GA is a meta-heuristic approach that produces reasonable quality solutions for complex functions. The extensive analysis of the proposed algorithm yield better performance in vital parameters like accuracy, recall and F-score.
Employment contracts recorded on blockchain have the strength to make it impossible to falsify or modify the agreements between employers and employees. The fact is that the work input through the application remains on the blockchain, and the manager shares it, which is expected to significantly improve the disadvantaged position of part-time workers. As a way to recognise and solve recent problems with employment contracts and wage problems, this paper proposes to store employment contracts in blocks using blockchain technology, enable accurate wage payments using smart contracts, and also store wage information in blocks. In addition, it can be proved by using the contents stored in the block when problems occur later.
In contemporary society, networked computers are playing a pivotal role in dissemination of knowledge and critical data in information systems; ensuring its security has become a challenging task for the network administrators and researchers. Machine learning techniques are extensively used in intrusion detection systems to mine out the extensive network data and extrapolate attack patterns. This paper proposes an intrusion detection framework with a combination of diverse attribute selection algorithms and machine learning algorithms to provide effective intrusion detection. Firstly, the model extracts the most relevant attributes using a hybrid meta-heuristic feature selection algorithm and then applies supervised machine learning algorithms to detect the several attack classes with improved detection accuracy, execution time and error rate. This study used NSL-KDD dataset on the proposed CS-CFSMHA framework with AdaBoost ensemble technique. Cost-sensitive classification was applied which improved the minority class accuracy and the overall accuracy to 81.1%.
Attribute-based encryption (ABE) is a very efficient way of authorising users to access confidential data in organisations without public-key validation from external trusted authorities or complex login processes. Ciphertext-policy attribute-based encryption (CP-ABE) is an improvement over ABE in which access is granted to users based on their attributes if they satisfy the access policy of the ciphertext defined by the data owner. CP-ABE poses many challenges like tracing the malicious user, revoking the access of users, collusion attacks: where users may combine keys to gain access to unauthorised data, and key escrow: when authorities have complete access to protected data due to saved private keys. Our proposed solution is an added security layer on top of the underlying CP-ABE which solves the four security concerns mentioned above. The TRO-CP-ABE system also gives great flexibility to the system designer on the use of key generation algorithms and access structures as required.
Today, IoT systems have become prominent, especially in the fields of healthcare, agriculture, and manufacturing. IoT systems encompass numerous devices that become targets for attacks and exploitation, thereby making these systems prone to security vulnerabilities. While IoT has become a widespread technology by virtue of its scalability, dependability, and ease of access, its shortcomings should also be addressed. Currently, client-server model of networking is employed in IoT devices which use a single gateway for transferring data and connecting through a cloud server. This model demands a high cost for cloud maintenance and network equipment. Furthermore, a single gateway is not secure as one failed node can compromise the entire network. To improve security, we propose the inclusion of blockchain technology; a distributed ledger where data is stored across several nodes, thereby eliminating single point failure. This project proposes the benefits of blockchain technology in conjunction with security of IoT systems.
Blockchain technology has evolved to solve the complexity and privacy issues associated with online digital content distribution in the last decade. Several studies have centred on different implementations of blockchain technology, and there is no comprehensive survey of the technology from both a technical and an application viewpoint. To close this gap, we conducted a detailed blockchain technology survey. This paper gives an in-depth look at blockchain technology. It discusses the blockchain fundamentals, taxonomy, characteristics, architecture of blockchain, and accessible consensus mechanisms used in the various blockchain systems. This paper also gives blockchain applications in multiple domains, including finance, medical, banking, etc. In addition, this paper presents an overview of the various problems that currently exist in blockchain technology and future research directions.
CCF can destabilise economies, reduce confidence between customers and banks, and severely affect other people and businesses. The primary objective of banks and businesses is to identify fraudulent transactions with a high level of accuracy and to also reduce false alerts and the costs of manual investigation activities. When identifying CCF in large datasets, feature selection is very important to improve accuracy performance and rapid detection of fraud. One of the most widely used methods of feature selection is the random forest classifier (RFC), which is well suited for large datasets. The RFC works well; it tends to identify more predictive features, which can significantly improve the classification performance for a CCF detection model. In this paper, we suggest a CCF detection method based on feature selection using random forest classifier and machine learning algorithms such as support vector machines (SVM), isolation forest (IF) to detect fraudulent transactions. The proposed model is applied to a large real-world dataset to study the accuracy of its fraud detection performance. A comparison is made between the proposed model and other machine learning methods.
Smart contract technology plays a significant role in various fields for achieving secure and automated transactions without the interference of third parties. There arise certain issues of security and privacy issue during the transaction with the assistance of a smart contract. In this present work, a comparative analysis is carried out between various existing techniques in blockchain-based smart contract techniques. First, analysis is done on the security issue. Second, on the basis of smart contract design. Finally, on privacy issue in smart contract platform. To analyse the performance, metrics such as consumption cost, transaction cost, time overhead and processing time are validated. The cost consumption and time overhead attained for the Auditable Access Control System (AACS) technique is 30,125,894 gas and 3,285 sec. The processing time attained for Online Auto Update Smart Contract (OAUSC) is 2,653 sec. This analysis suggested AACS technique functions better in comparison to other existing techniques.
The security vulnerabilities and flaws in the existing biometric fingerprint authentication system (FAS) are effectively overcome using the one time finger code (OTFC). Based on this OTFC technique, a new verification model is proposed in this work which is capable of evaluating and analysing the susceptibility of system to common security attacks. For verification and validation compliance, an integrated security framework with novel security protocol along with generation of a unique encryption key named terminal key is conceptualised and implemented. The exposure of the system to various security attacks are experimentally computed by measuring avalanche effect, which fetches an average value of 63.52%. Also, the severity of attack is verified by calculating base score obtained as 3, which is less in comparison with conventional systems. Hence it concludes that proposed FAS with new verification framework yields better results on exposure to common security threats than the conventional one.
As the electric vehicle market has significantly grown, the needs to install the charging station infrastructure for electric vehicles (EVs) have also been increased. Therefore, it is required for the efficient searching scheme for the appropriate charging station to enhance user experience. However, existing EV charging station searching schemes have low user convenience because they just provided the location information of the charging stations. Also, they did not provide user-friendly location information as well as voice recognition functionality. To overcome such limitations, this paper shows the voice recognition based EV charging station search scheme using Bixby platform to enhance user experience.
Routing protocol for low power lossy network (RPL) faces many challenges such as high energy consumption and high congestion and so on. Mainly IoT environment suffers by various attacks such as rank, Sybil and DDoS because of insufficient security provisions. The proposed BlockTrust-RPL focuses on detecting security threats without increasing energy consumption and congestion. For that, the proposed BlockTrust-RPL consists of three phases: 1) distributed authentication; 2) valid trust-based parent selection; 3) trickle timer optimisation and mobile sink movement. The overall network is connected with blockchain thorough block gateway. Blockchain is constructed with TrustBlock and AuthBlock where, TrustBlock records trust values and Authblock records authentication credentials. The simulation is done by using NS3.26 network simulator, which evaluates the performance of the proposed BlockTrust-RPL in terms of packet delivery ratio, delay, attack detection accuracy and average energy consumption with respect to number of nodes and number of malicious nodes.
