International Journal of Information and Computer Security (3 papers in press)

Regular Issues

• IP obfuscation: a survey of methods and the introduction of transient key logic locking  
  by Arsalan Ali Malik, Neelam Nasir, Naveed Riaz, Naveed Ahmed, Mureed Hussain, Sajid Ali Khan, Ammar Masood 
  Abstract: Logic locking is a widely adopted hardware obfuscation technique which can be further sub-categorised into static and dynamic approaches based on the nature of the employed key. Besides being susceptible to SAT and fault injection attacks, static logic locking is vulnerable to widespread compromise from a single key exposure or device breach. On the other hand, dynamic logic locking introduces complexities in resource utilisation, key management, design, and adaptability. In this work, we provide a comprehensive and up-to-date overview of existing IP Obfuscation techniques, highlighting their strengths, and potential vulnerabilities. We also propose, a hybrid logic locking technique that capitalises on the positive attributes of both static and dynamic logic locking methodologies while minimising their inherent limitations. An initial proof-of-concept implementation/simulation has been performed on the Xilinx SP605 FPGA development board. The suggested transient key logic locking scheme is applicable to all type of IPs.
  Keywords: system-on-chip; SoC; field-programmable gate arrays; FPGAs; hardware security; IP protection; IP theft; obfuscation.
  DOI: 10.1504/IJICS.2025.10074686
   
  
• DMHDA: a model for real-time detection and mitigation of DDoS attacks in software defined networks  
  by Deepak Kumar, Jawahar Thakur 
  Abstract: Software-defined networks (SDN) offer significant flexibility, scalability, and dynamic management. However, these networks are increasingly vulnerable to distributed denial of service (DDoS) attacks. This study investigates the susceptibility of SDNs to such attacks and presents a DMHDA (real-time detection and mitigation of DDoS attacks). The model employs a unified capability for both detection and mitigation. It features a custom-developed script, ryu2m.js, for the real-time detection and mitigation, along with the elephant.py script to identify the route through which elephant flow occurs. The proposed model uses the sFlow tool to monitor the network traffic, and a virtual SDN environment consisting of virtual hosts, openvswitches, and a RYU controller. The sFlow-RT application provides visualisation of the topology used, the connection between switches, visualisation of traffic and topology. The findings emphasise its effectiveness in mitigating congestion caused by attacks, indicating a potential for significant improvements in security and performance within SDN environment.
  Keywords: distributed denial of service; DDoS; elephant flow; high-rate; intrusion detection systems; IDS; RYU; security; sFlow.
  DOI: 10.1504/IJICS.2026.10075570
   
  
• Deep learning driven fusion of iris biometrics for optimised multimodal authentication informative security  
  by S.V. Sheela, K.R. Radhika 
  Abstract: Secure authentication methods have been made possible by the high level of maturity obtained by biometric-based technologies. Artificial neural networks forecast non-parametrically using interconnected artificial neurons, like the biological nervous system. For verification, iris, hand geometry, handwriting, fingerprint, speech, retina, face, and typing rhythm were studied. Iris recognition is most popular because it accurately identifies people. This study uses iris biometric authentication. The method simulates CASIA-Thousand-Iris utilising deep convolutional neural network (DCNN) architectures EfficientNetB0.1, CNN, DenseNet, and ConvNeXt. The experiment used our retinal recognition method to accurately identify numerous retinal samples. The suggested study introduced an MSAGFF module to EfficientNetB0.1 for iris biometrics. The attention mechanism uses channel spatial attention (CSA) to reduce redundant information and improve discriminative features for accurate recognition. Adaptive fusion strategy dynamically integrates recovered features from different receptive fields to increase model durability and decision-making. For secure Iris-based identification, EfficientNetB0.1s multimodal authentication is reliable. This end-to-end strategy boosts system performance. CNN (98.79%), DenseNet (92.11%), and ConvNeXt (66.66%) had worse accuracy than EfficientNetB0.1 (99.33%). CNN architectures in biometric systems are extended by deep learning-based iris recognition for safe authentication.
  Keywords: retina; convolutional neural network; CNN; informative security; multi-factor authentication; biometric identification; EfficientNetB0.1; DenseNet; ConvNeXt.
  DOI: 10.1504/IJICS.2026.10075572