Mobile ad-hoc network, one decentralized, infrastructure-less wireless system which provides communication services for military operation, disaster recovery operation and mobile computing. These networks offer abundant of feasibility for mobile ad-hoc networking, but meanwhile there are more issues existed such as dynamic topology, the sharing of the wireless medium, the limited battery energy. The common intrusion detection frameworks based on SVM classifiers often experience several problems like stationary decision boundaries, parameter sensitivity and huge energy consumption, which are unsuitable for the resource restricted mobile ad-hoc network nodes. This paper describes a hybrid X shaped Transfer Function (XTF) Modified Grey Wolf Optimization (GWO) and Energy Aware Support Vector Machine (SVM) model named XTF-GWO-SVM. It co-optimizes the feature subset selection and the hyper parameters of SVM classifier and imposes a penalty function for energy expensive feature subset in the objective function of optimization process. Moreover, an Information Gain weighted by energy residual energy is used in initial population seeding, a transfer function in X shape is used to realize the binary feature mapping, and a multi objective fitness function is introduced to balance the detection performance and processing cost. Then the model was implemented in MATLAB R2025a and compared with the conventional GWO-SVM method on the basis of the ITU-ML5G-PS-006 (2025) data, in four kinds of injected attack ratio (25%, 50%, 75%, 100%), the detection accuracies obtained for the developed method are 88.42%, 91.15%, 91.15% and 96.82% respectively, while for the conventional method are 82.10%, 84.45%, 87.30% and 89.15%. And meanwhile both of the precision, recall and F1-score had the corresponding improve compared to the traditional method for every injected ratio. This research shows that combining energy awareness in classification with an advanced binary optimizer result in a scalable and energy conscious mobile ad hoc network intrusion detection framework feasible for real time deployment. Keywords: Intrusion detection, Grey Wolf Optimization, Support Vector Machine, energy aware classification, Mobile Ad Hoc Network