Lithium is a chemical element on the cutting edge due to its lithium-ion batteries used in both electronics and electric vehicles. The emerging use of lithium-ion batteries in electric vehicles comes as a promising solution to sustain green transportation. The implications of green transportation could be understood by exploring lithium production and its application concepts. This article expands on those concepts by discussing the lithium supply and how vital lithium is to green technology. Statistical analysis has been applied to determine: (1) The degree of balance and interdependence between lithium raw materials and electric vehicle production, (2) the influence of electric vehicle demand on lithium production, and (3) the contribution of electric vehicles to reducing carbon emissions from road transport. This study provides necessary information on the availability and demand for lithium, which could be the basis for drawing up policies for electric vehicle expansion and lithium supply efficiency.

This paper proposes a hybrid approach called XTS that uses a combination of techniques to analyze highly imbalanced data with minimum features. XTS combines cost-sensitive XGBoost, a game theory-based model explainer called TreeSHAP, and a newly developed algorithm known as Sequential Forward Evaluation algorithm (SFE). The general aim of XTS is to reduce the number of features required to learn a particular dataset. It assumes that low-dimensional representation of data can improve computational efficiency and model interpretability whilst retaining a strong prediction performance. The efficiency of XTS was tested on a public dataset, and the results showed that by reducing the number of features from 33 to less than five, the proposed model achieved over 99.9% prediction efficiency. XTS was also found to outperform other benchmarked models and existing proof-of-concept solutions in the literature. The dataset contained data related to DNS-over-HTTPS (DoH) tunnels. The top predictors for DoH classification and characterization were identified using interactive SHAP plots, which included destination IP, packet length mode, and source IP. XTS offered a promising approach to improve the efficiency of the detection and analysis of DoH tunnels while maintaining accuracy, which can have important implications for behavioral network intrusion detection systems.

This paper proposes an innovative approach capitalized on the distinctive characteristics of command and control (C&C) beacons, namely, time intervals and frequency between consecutive unique connections, to compress the network flow dataset. While previous studies on the same matter used single technique, we propose a multi-technique approach for efficient detection of DoH tunnels. We use a baseline public dataset, CIRA-CIC-DoHBrw-2020, containing over a million network flow properties and statistical features of DoH, tunnels, benign DoH and normal browsing (HTTPS) traffic. Each sample is represented by 33 features with a timestamp. Our methodology combines star graph and bar plot visualizations with supervised and unsupervised learning techniques. The approach underscores the importance of C&C beacon characteristic features in compressing a dataset and reducing a flow dimension while enabling efficient detection of DoH tunnels. Through compression, the original dataset size and dimensions are reduced by approximately $95\%$ and $94\%$ respectively. For supervised learning, RF emerges as the top-performing algorithm, attaining precision and recall scores of 100% each, with speed increase of $\approx 6796$ times faster in training and $\approx 55$ in testing. For anomaly detection models, OCSVM emerges as the most suitable choice for this purpose, with precision (88.89) and recall (100). Star graph and bar graph models also show a clear difference between normal traffic and DoH tunnels. The reduction in flow sample size and dimension, while maintaining accuracy, holds promise for edge networks with constrained resources and aids security analysts in interpreting complex ML models to identify Indicators of Compromise (IoC).