This paper proposes a secure MIMO communication system that utilizes movable antennas (MAs) to enhance physical layer security (PLS). The system consists of a base station (BS) equipped with MAs, a legitimate information receiver (IR), and an eavesdropper (Eve). The goal is to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix, and the positions of the MAs, subject to constraints on maximum transmit power and minimum antenna spacing. To solve the non-convex optimization problem, a block coordinate descent (BCD) method combined with majorization-minimization (MM) is applied. The SR is reformulated using the minimum mean square error (MMSE) method, and the optimal TPC matrix and AN covariance matrix are derived using the Lagrangian multiplier method. The MM algorithm is then used to iteratively optimize the positions of the MAs while keeping other variables fixed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the MA-aided system significantly improves security performance compared to conventional fixed position antenna (FPA)-based systems. The results also indicate that increasing the size of the transmit region, the number of antennas, the number of paths, and the transmit power enhances the security of the MA-aided system. Additionally, accurate field-response information (FRI) is crucial for the MA-aided system to maintain its security performance. The proposed algorithm is based on a BCD-MM approach, where the variables are alternately optimized. The TPC matrix and AN covariance matrix are optimized in closed forms, while the positions of the MAs are optimized using the MM algorithm. The algorithm is shown to converge monotonically, ensuring the optimal solution is achieved. The computational complexity of the algorithm is analyzed, and it is shown to be efficient due to the closed-form solutions for some variables. The overall algorithm is summarized in Algorithm 2, which includes steps for optimizing the TPC matrix, AN covariance matrix, and MA positions. The algorithm is guaranteed to converge due to the monotonic decrease in the objective function and the upper bound on the power constraint.This paper proposes a secure MIMO communication system that utilizes movable antennas (MAs) to enhance physical layer security (PLS). The system consists of a base station (BS) equipped with MAs, a legitimate information receiver (IR), and an eavesdropper (Eve). The goal is to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix, and the positions of the MAs, subject to constraints on maximum transmit power and minimum antenna spacing. To solve the non-convex optimization problem, a block coordinate descent (BCD) method combined with majorization-minimization (MM) is applied. The SR is reformulated using the minimum mean square error (MMSE) method, and the optimal TPC matrix and AN covariance matrix are derived using the Lagrangian multiplier method. The MM algorithm is then used to iteratively optimize the positions of the MAs while keeping other variables fixed. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the MA-aided system significantly improves security performance compared to conventional fixed position antenna (FPA)-based systems. The results also indicate that increasing the size of the transmit region, the number of antennas, the number of paths, and the transmit power enhances the security of the MA-aided system. Additionally, accurate field-response information (FRI) is crucial for the MA-aided system to maintain its security performance. The proposed algorithm is based on a BCD-MM approach, where the variables are alternately optimized. The TPC matrix and AN covariance matrix are optimized in closed forms, while the positions of the MAs are optimized using the MM algorithm. The algorithm is shown to converge monotonically, ensuring the optimal solution is achieved. The computational complexity of the algorithm is analyzed, and it is shown to be efficient due to the closed-form solutions for some variables. The overall algorithm is summarized in Algorithm 2, which includes steps for optimizing the TPC matrix, AN covariance matrix, and MA positions. The algorithm is guaranteed to converge due to the monotonic decrease in the objective function and the upper bound on the power constraint.