This paper provides a comprehensive survey of intrusion detection systems (IDS), focusing on the techniques, datasets, and challenges in the field. It begins by highlighting the increasing sophistication of cyber-attacks and the need for effective IDS to protect against these threats. The paper classifies IDS into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS), detailing their mechanisms and limitations. SIDS rely on pattern matching and signature databases to detect known intrusions, while AIDS use machine learning, statistical, and knowledge-based methods to identify anomalies in normal system behavior, making them better at detecting zero-day attacks. The paper also discusses the evaluation of IDS datasets, such as DARPA, KDD, NSL-KDD, and ADFA-LD, and their features and limitations. It addresses the challenges in using these datasets, including the presence of duplicate packets and the need for feature selection. Additionally, the paper explores various techniques for implementing AIDS, including statistics-based, knowledge-based, and machine learning-based approaches, and evaluates their performance using metrics like True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic (ROC) curves. Finally, the paper discusses future research challenges, such as improving the accuracy and reducing false positives in IDS, and suggests potential solutions to enhance the effectiveness of IDS in detecting sophisticated malware and zero-day attacks.This paper provides a comprehensive survey of intrusion detection systems (IDS), focusing on the techniques, datasets, and challenges in the field. It begins by highlighting the increasing sophistication of cyber-attacks and the need for effective IDS to protect against these threats. The paper classifies IDS into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS), detailing their mechanisms and limitations. SIDS rely on pattern matching and signature databases to detect known intrusions, while AIDS use machine learning, statistical, and knowledge-based methods to identify anomalies in normal system behavior, making them better at detecting zero-day attacks. The paper also discusses the evaluation of IDS datasets, such as DARPA, KDD, NSL-KDD, and ADFA-LD, and their features and limitations. It addresses the challenges in using these datasets, including the presence of duplicate packets and the need for feature selection. Additionally, the paper explores various techniques for implementing AIDS, including statistics-based, knowledge-based, and machine learning-based approaches, and evaluates their performance using metrics like True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic (ROC) curves. Finally, the paper discusses future research challenges, such as improving the accuracy and reducing false positives in IDS, and suggests potential solutions to enhance the effectiveness of IDS in detecting sophisticated malware and zero-day attacks.