Forthcoming and Online First Articles

International Journal of Security and Networks

International Journal of Security and Networks (IJSN)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Security and Networks (2 papers in press)

Regular Issues

  • Secure Identity-Based Encryption: Overcoming the Key Escrow Challenge   Order a copy of this article
    by Khaleda Afroaz, Subba Rao Y.V., Rukma Rekha N 
    Abstract: Identity-based encryption (IBE) simplifies public key encryption overhead by eliminating the need for certificate authorities (CAs) to issue public keys. However, IBE suffers from the key escrow problem, where the private key generator (PKG) can access private keys. Existing solutions require additional trusted authorities or certificates. This paper presents a novel scheme that overcomes key escrow without certificates or extra trusted authorities. The scheme incorporates the receiver's public parameter during encryption, along with identity and public parameters from the PKG. To decrypt, the receiver needs the private key generated by the PKG and their private parameter, which is unknown to the PKG. This approach prevents PKG from decrypting messages. The proposed scheme is secure in the selective identity model and applicable in healthcare, MANETS, IoT, and M2M communications.
    Keywords: identity-based encryption; IBE; key escrow problem; private key generator; PKG.
    DOI: 10.1504/IJSN.2023.10060961
     
  • Multiclassification of DDoS Attacks using Machine and Deep Learning Techniques   Order a copy of this article
    by Rashmi Bhatia, Rohini Sharma 
    Abstract: There are very few studies to detect different classes of DDoS attacks. Multiclassification helps network administrators to study individual behaviour. In this study, 82 flow-based features are used to detect 13 types of DDoS attacks using seven machine learning techniques namely naive Bayes, decision tree, multinomial logistic regression, random forest, k-nearest neighbour, AdaBoost and one hidden layer multi-layer perceptron (MLP) and two deep learning techniques namely multiple hidden layers MLP and long short-term memory (LSTM). Different variants of deep learning techniques are compared while fine-tuning hyperparameters. Their performance is analysed using 5-fold cross-validation and compared with existing studies. The experimental results show that random forest performed best with the highest accuracy of 0.7677 followed by one hidden layer MLP with accuracy of 0.7485 and improvements in them can give better results. It is also concluded that appropriate selection of features is important to get higher accuracy with lesser classification time.
    Keywords: machine learning; deep learning; multilayer perceptron; MLP; long short-term memory; LSTM; intrusion detection; DDoS attacks.
    DOI: 10.1504/IJSN.2023.10063264
     

 

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