Sequential recommendation models are crucial for next-item prediction tasks in various online platforms, yet many focus on a single behavior, neglecting valuable implicit interactions. While multi-behavioral models address this using graph-based approaches, they often fail to capture sequential patterns simultaneously. Our proposed Multi-Behavioral Sequential Recommendation framework (MBSRec) captures the multi-behavior dependencies between the heterogeneous historical interactions via multi-head self-attention. Furthermore, we utilize a weighted binary cross-entropy loss for precise behavior control. Experimental results on four datasets demonstrate MBSRec's significant outperformance of state-of-the-art approaches. The implementation code is available here (https://drive.google.com/drive/folders/1EGRQutc9xtVYbsbXswUcTb9nvT1Tc9cZ?usp=sharing) during the review and will be added to GitHub upon acceptance.