This paper studies the problem of optimal deception attacks against remote state estimation, where the measurement data is transmitted through an unreliable wireless channel. A malicious agent is capable of intercepting and modifying raw data, with the goal to cause the maximum estimation quality degradation and deceive χ2 anomaly detectors. Contrary to existing studies that focused on greedy attack performance, we consider a more general scenario that the attacker aims to maximize the summation of estimation errors in a fixed interval. It is shown that the information-based optimal attack is a linear combination of the minimum mean-square error estimates of all historical prediction errors. The combination coefficients can be obtained by solving a convex optimization problem. Moreover, the proposed attack can be generalized to deceive interval χ2 detectors with different lengths by slightly modifying the stealthiness constraint. The effectiveness of the proposed method is verified with numerical examples and comparative studies.