International Journal of Intelligent Engineering Informatics (4 papers in press)

Regular Issues

• "Cloud Gaming: The Future of Gaming Infrastructure"  
  by Shrikant Harle, Pradeep Bhaduria, Amol Bhagat, Shrikant Bhuskade, RAJAN Wankhade, Milind Mohod 
  Abstract: This paper delves into the transformative impact of cloud gaming on the gaming industry. The problem statement outlined in the paper revolves around the changing landscape of gaming infrastructure, specifically focusing on the shift towards cloud-based platforms. The study emphasises the need to understand how cloud gaming affects various aspects of the gaming ecosystem, including accessibility, gameplay experience, and game development processes. The findings of the study highlight several key aspects of cloud gaming. Firstly, the paper identifies the significant benefit of reduced entry barriers for gamers, as they no longer need to invest in expensive gaming hardware. This increased accessibility has led to a broader gaming community and improved access to high-quality gaming experiences. Additionally, the study emphasises the advantages of cross-platform compatibility, allowing gamers to seamlessly switch between devices without losing progress.
  Keywords: cloud gaming; gaming industry; bandwidth; virtual reality; VR.
  DOI: 10.1504/IJIEI.2024.10064799
   
  
• Efficient Authentication Framework with Blake2s and a Hash-based Signature Scheme for Industry 4.0 Applications  
  by Purvi Tandel, Jitendra Nasriwala 
  Abstract: Industry 4.0, the new production standard, integrates small to medium-level devices for efficient automation. Secure communication among these devices requires protection from external attacks in IoT applications. The imminent threat of quantum attacks to prevailing public-key approaches necessitates a secure authentication architecture tailored for IoT devices. Furthermore, IoT devices' limited computing, storage, and energy resources demand a lightweight authentication mechanism. In response, hash-based signatures have been proposed as a post-quantum solution for Industry 4.0. Presented approach involves an in-depth analysis of collision-resistant hash functions for faster, memory-optimised authentication. Implementing a hash-based signature scheme through experiments, a 27.18% improvement has been achieved in key generation speed, particularly with Blake2s over popularly used SHA-256. These results affirm the efficiency of the proposed hash-based signature scheme, offering superior performance in time and memory utilisation for Industry 4.0 applications.
  Keywords: authentication mechanism; hash-based signature scheme; IoT applications; hash functions; SHA-256; Blake2s; SHA-3; collision resistant hash-function.
  DOI: 10.1504/IJIEI.2024.10064828
   
  
• Ensemble of Deep Features and Classifiers Approach for MRI Brain Tumor Classification  
  by Sathees Kumar  
  Abstract: Medical professionals identify and classify brain tumours to save lives. This innovative study applies prominent machine learning classifiers to varied deep brain imaging features extracted by a pre-trained convolution neural network. Several machine learning classifiers use a pre-trained deep convolutional neural network's deep features to classify MRI images. Famous pre-trained networks extract MRI brain imaging properties. Multiple machine learning classifiers validate extracted traits. The finest deep features from numerous ML classifiers are assembled into feature sets and fed into multiple classifiers to predict classification. Pre-trained deep feature mining, machine learning classifiers, and brain tumour categorisation ensemble features are tested on BraTS-19, Figshare, and Kaggle datasets. Classifying brain tumour images as malignant or benign is difficult. To speed up categorisation, use ensemble deep features and a pre-trained model. Extraction of deep features from MRI images using transfer learning (EfficientNet-B4, Inception-V3, and VGG-19) is applied to popular classifiers (SVM, AdaBoost, Na
  Keywords: deep learning; ensemble learning; transfer learning; machine learning; brain tumour classification; pre-trained deep convolutional neural network; recognition and categorisation.
  DOI: 10.1504/IJIEI.2024.10065363
   
  
• Channel Minimised Depth-Wise CNN with Node Weighted Tree-LSTM Model to Detect Nested Query based SQL Injection Attacks  
  by Meharaj Begum A, Michael Arock, Srinivasulu Reddy Uyyala 
  Abstract: With advanced communication technologies, the fully connected world is increasingly vulnerable to web attacks, particularly SQL injection attacks (SQLIAs). Attackers constantly discover new ways to exploit database vulnerabilities, necessitating that researchers stay updated on the latest attack vectors. While many current methods effectively detect SQLIAs in simple queries, they often struggle with complex nested sub-queries. This paper proposes a novel approach using a node-weighted parse tree to extract key tokens from SQL queries. The proposed method integrates a depth-wise convolutional neural network (DWCNN) for feature extraction and a tree-LSTM for classifying queries as either legitimate or injected. The proposed DWCNN reduces the node-weighted parse tree through custom filtering, and a class-based TF-IDF (C_TF_IDF) similarity score is assigned to the bigrams of the reduced parse tree, as these possess unique scores for two classes of queries based on their frequency in the dataset. Tree-LSTM's capability to recognise long-distance interactions within hierarchies then uses these scores to classify the query. The model achieves superior accuracy, with 98.9% for simple queries and 98.2% for complex queries, outperforming current state-of-the-art methods on standard benchmark datasets.
  Keywords: SQL injection attack detection; nested sub-query; node weighted parse tree; depth-wise CNN; Class_based TF_IDF.
  DOI: 10.1504/IJIEI.2025.10065585