# A Secured Intrusion Detection System for Mobile Edge Computing
## 2 Literature Review
### Related Work  
The fundamental idea behind the Internet of Things (IoT) centers on the proliferation of intelligent nodes seamlessly integrated into our daily social interactions [9]. This underscores the imperative for cutting-edge intrusion detection methods specifically tailored to address the unique challenges posed by IoT and EDGE computing networks, emphasizing the importance of adopting approaches grounded in artificial intelligence. In the realm of IoT, digital devices interconnected via the internet aim to establish connections for individuals through smart IoT applications, resulting in network-distributed environments characterized by limited power, storage, and memory capacities. Intrusion detection systems (IDSs) assume a critical role in identifying and responding to intrusive actions and behaviors, prompting administrators to take automated actions [10]. Employing signature methods, IDSs detect intrusions by comparing signatures to predefined intrusive events stored in the database [11]. While this ensures swift detection and diminishes false alarms, a significant drawback is evident: only known intrusions can be identified. Anomaly detection treats all intrusive activities as anomalous, flagging any activity deviating from standard treatment as a potential intrusion. Anomaly-based detection offers a substantial advantage in detecting zero-day attacks and variations of known attacks. Numerous existing approaches leverage traditional machine learning environments for intrusion detection. Robust anomaly detection methods utilizing artificial neural networks (ANN) and deep learning surpass the limitations of conventional approaches [12; 13; 14; 15]. The adaptability of ANN features renders them applicable across diverse domains, with a specific focus on enhancing intrusion detection. These advanced approaches prove immensely beneficial in the realms of modern computing and EDGE computing. In [16], a deep belief network tailored for the Edge of Things (EoT) is presented, offering the capability to detect intrusive activities within the EoT network. The proposed framework comprises modules for data collection, feature extraction, and classification. However, the computational demands and associated costs of this model are notably high. Addressing the critical security concerns in the Internet of Things (IoT) network, ref. [17] introduces a robust intrusion detection system (IDS) incorporating a multi-agent system, blockchain, and deep learning algorithms. While this approach demonstrates high efficiency, the amalgamation of three diverse techniques introduces complexity and increases response time. For mobile edge computing, ref. [10] proposes a network IDS that captures tcpdump packets, extracts and analyzes features, and forwards legitimate packets into the network. The model employs a topic model to learn normal behavioral patterns, but its detection accuracy is compromised when new types of packets enter the network. In addition, ref. [18] introduces a data-driven mimicry and game theory-based IDS for edge computing networks, investigating new attacks based on game income and balance points. Efforts are made to reduce the IDS cost. Also, ref. [19] suggests a traffic inspection and classification-based distributed attack model for IoT applications, leveraging the flexibility of cloud-based architectures with edge computing. However, relying on a traffic classification-based mechanism may yield inaccurate results with new network traffic. Further, authors in [20] proposed the ZBIDS model, a security framework designed to enhance network protection by logically dividing the network into distinct zones, each with specific security requirements. This hierarchical architecture allows for tailored intrusion detection mechanisms in each zone, ensuring a more effective and flexible approach to network security. ZIDS employs pattern recognition and signature-based methods to identify known attack patterns or anomalies in network behavior.