The diversification of attacks jeopardizes cyberspace's normal operation. This paper proposes a new Harris Hawks Optimization Based on Multiple Strategies (MS-NHHO), inspired by humans' limited cognitive load, collective decision-making, and dynamic learning mechanisms for processing complex information. This paper utilizes the elite chaos reverse learning strategy to improve the algorithm's convergence speed and population diversity. Then, the dynamic adaptive weights are introduced into the escape energy decline mechanism to improve the algorithm's global exploration and local exploitation ability. Finally, the Gaussian random walk strategy enhances the algorithm's anti-stagnation ability. The experimental results confirm the usefulness of the three optimization strategies. Meanwhile, MS-NHHO exhibits satisfactory performance in terms of computational cost, detection performance, and efficiency in several scenarios.