A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles

The rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV

Evaluation of scFv protein recovery from E. coli by in vitro refolding and mild solubilization process

Abstract Background The production of therapeutically active single chain variable fragment (scFv) antibody is still challenging in E. coli due to the aggregation propensity of recombinant protein into inclusion bodies (IBs). However, recent advancement of biotechnology has shown substantial recovery of bioactive protein from such insoluble IBs by solubilization and refolding processes. In addition, gene fusion technology has also widely been used to improve the soluble protein production using E. coli. This study demonstrates that mild-solubilization and in vitro refolding strategies, both are capable to recover soluble scFv protein from bacterial IBs, although the degree of success is greatly influenced by different fusion tags with the target protein. Results It was observed that the most commonly used fusion tag, i.e., maltose binding protein (MBP) was not only influenced the cytoplasmic expression in E. coli but also greatly improved the in vitro refolding yield of scFv protein. On the other hand, mild solubilization process potentially could recover soluble and functional scFv protein from non-classical IBs without assistance of any fusion tag and in vitro refolding step. The recovery yield achieved by mild solubilization process was also found higher than denaturation–refolding method except while scFv was refolded in fusion with MBP tag. Concomitantly, it was also observed that the soluble protein achieved by mild solubilization process was better structured and functionally more active than the one achieved by in vitro refolding method in the absence of MBP tag or refolding enhancer. Conclusions Maltose binding protein tagged scFv has shown better refolding and solubility yields as compare to mild solubilization process. However, in terms of cost, time and tag free nature, mild solubilization method for scFv recovery from bacterial IBs is considerable for therapeutic application and further structural studies