## 1 Introduction  
With the rapid development of smart homes and voice-assistant technologies, home environments have been established in which loudspeakers and microphones are deployed as sensors or are built-in and distributed through home appliances. The aim of this research was to develop an acoustic-based home security system in the aforementioned environment. An example is shown in Figure 1.  
Smart home technology has been evolving to provide proactive services through the monitoring of residents. Therefore, accurately recognizing the scenario in a home environment through a combination of various sensors is important. In [1], studies on context awareness for indoor activity recognition using binary sensors, cameras, radio-frequency identification, and air pressure sensors were reviewed. A study proposed to recognize each living activity of a user by combining the power meters of appliances with an ultrasonic sensor [2]. In [3], a study was conducted to recognize the complex activities of a kitchen using one module with various sensors. In such a smart home environment, microphones are used for context awareness and health monitoring owing to their advantage of operating with low power [4]. [PERSON] et al. explained that a microphone can be used to identify the scenario of a home environment based on unusual loud noise and the sound of a human falling [5]. In addition, a study explored the possibility of personal identification through footsteps [6]. Automated home security systems have been developed using smart home technologies. Recent home security systems protect residents and their properties, making them safe from intruders as conventional security systems, and they enable the detection of risks to the residents in advance through context awareness of the home environment [5,7]. Microphones in a home security system are primarily used for two purposes: event detection and the classification of unusual sounds, and intrusion detection. In [8], related studies were reviewed through a comprehensive survey of background surveillance, event classification, object tracking, and situation analysis, and the detection of events in a highly noisy environment was proposed [9]. In [10, 11, 12], a microphone array and security camera were combined to detect the sound from an intruder and tilt the security camera in the direction of the sound. Research has been conducted to predict the state of a control space by recognizing the type of sound, analyzing and classifying the sound, and estimating the angular position of the unusual sound using a microphone array [13,14]. A method to identify human behavior in a control space by applying a microphone array to a sound-steered camera was proposed [15]. Intrusion detection using microphones is as effective as the use of security cameras in terms of detecting moving objects [4], and the related studies are summarized below. Studies on intrusion detection have been conducted to determine an intrusion in a security zone based on the change in the room transfer function [16], the sound field variation according to the acoustical transmission path of distributed microphones [17], and the coherence responses in low-frequency environments [18]. However, the conventional methods for event detection have the disadvantage of operating only when a loud noise is generated because the position is determined in the direction of the generated sound, and current techniques for intrusion detection have the disadvantage of only detecting intrusion but not providing the location. To overcome these disadvantages, we propose an acoustic-based active localization and analysis method to estimate a silent intruder. This study provides a link between localization and intrusion detection techniques using an acoustic-based security system. The reason is that if a person's position can be estimated and tracked using microphones and loudspeakers, the entry of an unauthorized person into the security space can be known.