# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Fuzzy Logic  
Ref. [24] presented a novel approach for detecting cross-layer anomalies in smart home sensor networks using fuzzy logic and mobile agents. Despite advancements in consumer electronics, anomalies in data generated by sensing devices in smart homes remain a concern, resulting from node failures, transmission issues, or malicious attacks. These anomalies can undermine the reliability of sensed data, leading to inaccurate decisions at both the local (smart house) and global (smart city) levels. To address this, the researchers proposed a cross-layer anomaly detection method that leverages mobile agents and fuzzy logic. The approach considers the stochastic variability in cross-layer data obtained from data packets and employs fuzzy logic-based soft bounds to characterize the behavior of sensor nodes. By adopting a cross-layer design, the method can identify anomalies in both nodes and communication links. It also considers the communication link-state before transmitting mobile agents, ensuring efficient transmission. The proposed method was evaluated on a real testbed, and modular application software was developed to control the smart home's anomaly detection system. The results demonstrated that the approach effectively identifies cross-layer anomalies and reduces the energy consumption associated with mobile agent transmission in environments with poor communication connectivity. In summary, [PERSON] et al. 's research introduces a novel approach for detecting cross-layer anomalies in smart home sensor networks. By combining fuzzy logic and mobile agents, the proposed method improves anomaly detection accuracy and reduces energy requirements, offering potential benefits for ensuring reliable and efficient smart home operations. Ref. [25] proposed an Intrusion Detection System (IDS) that utilizes multi-agent systems and artificial intelligence techniques, including Fuzzy Logic Controllers (FLCs), Multi-Layer Perceptrons (MLPs), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Network Intrusion Detection Systems (NIDS) play a crucial role in examining network traffic and identifying potential threats. The IDS system proposed by the authors consists of three agents: the accumulator, analyzer, and decision-maker. The accumulator agent is responsible for collecting and filtering network traffic data. The analyzer agent utilizes a decision tree to classify the data into different categories. Finally, the decision-maker agent employs a fuzzy logic controller (FLC) to make the final decision. To evaluate the effectiveness of the proposed technique, experiments were conducted using the KDDCup 1999 dataset. The results demonstrated an improvement in attack detection accuracy when compared to existing approaches. In summary, [PERSON] et al. 's research introduces an IDS system that combines multi-agent systems and artificial intelligence techniques. By utilizing FLCs, MLPs, and ANFIS, the proposed system enhances the accuracy of detecting network attacks. Ref. [26] proposed a unique approach to developing an anomaly-based network security cyber sensor by utilizing a novel learning technique and implementing it in hardware. Their recommended learning method involves creating a fuzzy logic rule architecture to mimic natural network activity. The system employs an online clustering approach to generate individual fuzzy rules from a continuous stream of incoming packets, enabling effective anomaly detection. On a different note, [PERSON]. [27] introduced a novel \"Knowledge-Based System (KBS)\" that combines Fuzzy Logic (FL), particle filtering, and anomaly detection to construct high-performing investment portfolios. Their FL approach selects portfolios from a pool of candidates based on multilateral performance criteria, ensuring favorable risk-return profiles. In summary, Ref. [26] focused on building an anomaly-based network security cyber sensor using a unique learning technique and hardware implementation, while [PERSON].