Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to malware classification using only TLB-related Hardware Performance Counters (HPCs), explicitly excluding any dependence on timing features such as task execution duration or memory access timing. Our methodology evaluates whether TLB data alone, without any timing information, can effectively distinguish between malicious and benign programs. We test this across three classification scenarios: (1) A binary classification problem involving distinguishing malicious from benign tasks, (2) a 4-way classification problem designed to improve separability, and (3) a 10-way classification problem with classes of individual benign and malware tasks. Our results demonstrate that even without execution time or memory access timing, TLB events achieve up to 81% accuracy for the binary, and 72% accuracy for the 4-class grouping, and 61% accuracy for the 10-class grouping. These findings demonstrate that time-independent TLB patterns can serve as robust behavioral signatures. This work expands the understanding of microarchitectural side effects by demonstrating that TLB-only features, independent of timing-based techniques, can be effectively used for real-world malware detection.