International Journal of Security and Networks (5 papers in press)

Regular Issues

• Secure Identity-Based Encryption: Overcoming the Key Escrow Challenge  
  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  
  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
   
  
• slackFS - Resilient and Persistent Information Hiding Framework  
  by Avinash Srinivasan, Christian Rose, Jie Wu 
  Abstract: The ever-expanding cyberspace, driven by digital convergence, inadvertently broadens the attack surface. Savvy modern cybercriminals have embraced steganography as a key weapon. This paper introduces slackFS, a novel steganographic framework utilising file slack space for covert data concealment. Unlike prior methods focusing on individual files, slackFS hides entire filesystems, offering a structured means for data exfiltration. It ensures persistence across system reboots, robust detection resistance, portability, and minimal performance impact. Incorporating erasure-code based fault-tolerance, slackFS enables recovery from partial loss due to accidental slack space overwriting. Prototype validation on Ubuntu 20.04 with ext4 filesystems as the cover medium and FAT16 as the hidden malicious filesystem is conducted. The study includes testing of three coding libraries and two Reed-Solomon erasure code implementations - VANDERMONDE and CAUCHY matrices highlighting slackFSs resilience and effectiveness.
  Keywords: Attacker; data-exfiltration; fault-tolerance; filesystem; information hiding; malicious; persistence and resilience; robust; security; system security and steganography.
  DOI: 10.1504/IJSN.2024.10064277
   
  
• A Network Traffic Classification and Anomaly Detection Method Based on Parallel Cross-Convolutional Neural Networks  
  by Bailin Zou, Tianhang Liu 
  Abstract: Network traffic anomaly detection, an effective means of network defence, can detect unknown attack behaviours and provide crucial support for network situation awareness. However, existing methods face challenges such as reliance on manually designed features, decreased classification accuracy, slow processing speeds, and loss of important information in traffic. To solve these problems, inspired by the binocular vision principle, we propose a parallel cross-convolutional neural network model. The model directly extracts original network traffic payload data as input, controlling depth. Utilising two deep convolutional neural network (CNN) data transformation streams undergoing three cross-blends, more feature information is extracted, enabling the capture of deeper traffic characteristics. Experimental results on the USTC-TFC2016 dataset demonstrate our model achieves 100% accuracy with only two epochs for 20-class classification, outperforming other similar models in detection performance.
  Keywords: parallel cross-convolutional neural networks; CNN; intrusion detection; deep learning; network security; traffic classification.
  DOI: 10.1504/IJSN.2024.10064782
   
  
• Generalized Secret Sharing Scheme for Non-monotone Access Structures using MDS Codes  
  by Shivakrishna Nallabothu, Rukma Rekha N, Subba Rao Y.V. 
  Abstract: A secret sharing scheme is a powerful tool for protecting sensitive data with applications in access control, key management schemes, blockchains, etc. A generalised secret sharing scheme realises any access structure that satisfies the monotone property. Such schemes are not practical to implement. A strict generalised scheme realises a general access structure that is non-monotone. We propose an efficient strict generalised secret sharing scheme based on maximum distance separable codes. The novelty of this scheme is that it allows new authorised subsets and participants to be added without modifying the shares of the existing participants and the time complexity of the proposed scheme is independent of the number of participants. Participants' shares can be chosen independently of the secret. The proposed scheme is ideal. This paper also carries out the proposed scheme's correctness, security, and complexity analysis.
  Keywords: Secret Sharing Scheme(SSS); Generalized Access Structure; Non-monotone Access Structure; Ideal Scheme; MDS Codes.
  DOI: 10.1504/IJSN.2024.10064832