Rajkumar Singh Rathore

2 months ago | nature.com | Shiv Prakash |Rajkumar Singh Rathore |Vivek Kumar Pandey |Tiansheng Yang |Priyanshu Sinha |Dinesh Sahu

The growing use of IoT has brought enormous safety issues that constantly demand stronger hide from increasing risks of intrusions. This paper proposes an Advanced LSTM-CNN Secure Framework to optimize real-time intrusion detection in the IoT context. It adds LSTM layers, which allow for temporal dependencies to be learned, and CNN layers to decompose spatial features which makes this model efficient in identifying threats. It is important to note that the used BoT-IoT dataset involves various cyber attack typologies like DDoS, botnet, reconnaissance, and data exfiltration. These outcomes present that the proposed LSTM-CNN model has 99.87% accuracy, 99.89% precision, and 99.85% recall with a low false positive rate of 0.13% and exceeds CNN, RNN, Standard LSTM, BiLSTM, GRU deep learning models. In addition, the model has 90.2% accuracy in conditions of adversarial attack proving that the model is robust and can be used for practical purposes. Based on feature importance analysis using SHAP, the work finds that packet size, connection duration, and protocol type should be the possible indicators for threat detection. These outcomes suggest that the Hybrid LSTM-CNN model could be useful in improving the security of IoT devices to provide increased reliability with low false alarm rates.

Jun 7, 2024 | nature.com | Venkatesan Ramakrishnan |Pradeep Vishnuram |Tiansheng Yang |Mohit Bajaj |Rajkumar Singh Rathore |Ievgen Zaitsev | +3 more

Wireless charging of Electric Vehicles (EVs) has been extensively researched in the realm of electric cars, offering a convenient method. Nonetheless, there has been a scarcity of experiments conducted on low-power electric vehicles. To establish a wireless power transfer system for an electric vehicle, optimal power and transmission efficiency necessitate arranging the coils coaxially. In wireless charging systems, coils often experience angular and lateral misalignments. In this paper, a new alignment strategy is introduced to tackle the misalignment problem between the transmitter and receiver coils in the wireless charging of Electric Vehicles (EVs). The study involves the design and analysis of a coil, considering factors such as mutual inductance and efficiency. Wireless coils with angular misalignment are modelled in Ansys Maxwell simulation software. The proposed practical EV system aims to align the coils using angular motion, effectively reducing misalignment during the parking of two-wheelers. This is achieved by tilting the transmitter coil in the desired direction. Furthermore, micro sensing coils are employed to identify misalignment and facilitate automatic alignment. Additionally, adopting a power control technique becomes essential to achieve both constant current (CC) and constant voltage (CV) modes during battery charging. Integrating CC and CV modes is crucial for efficiently charging lithium-ion batteries, ensuring prolonged lifespan and optimal capacity utilization. The developed system can improve the efficiency of the wireless charging system to 90.3% with a 24 V, 16 Ah Lithium Ion Phosphate (LiFePO4) battery at a 160 mm distance between the coils.