This paper presents a comparative study of several anomaly detection schemes for identifying network intrusions. Intrusion detection involves identifying attacks against computers and network infrastructures, with anomaly detection being a key component that identifies deviations from normal behavior. The study evaluates various supervised and unsupervised anomaly detection schemes on the DARPA 1998 dataset and real network data using standard evaluation techniques and specific metrics for detecting attacks involving many connections. The results indicate that some anomaly detection schemes show promise in detecting novel intrusions in both the DARPA 98 data and real network data. The paper discusses two main types of intrusion detection techniques: misuse detection and anomaly detection. Misuse detection labels data as normal or intrusive and uses learning algorithms to retrain models on new data. Anomaly detection, on the other hand, builds models of normal data and detects deviations from this model. Anomaly detection is advantageous as it can detect new types of intrusions as deviations from normal usage, but suffers from a high rate of false alarms due to legitimate system behaviors being flagged as anomalies. The study evaluates several anomaly detection techniques, including outlier detection schemes and unsupervised support vector machines. The paper compares the performance of these techniques using metrics such as burst detection rate, surface area, and response time. The results show that the LOF (Local Outlier Factor) approach performs well in detecting both bursty and single-connection attacks, with high detection rates and low false alarm rates. The nearest neighbor (NN) approach also performs well, particularly for single-connection attacks. The Mahalanobis-based approach is less effective, especially for attacks with complex normal behaviors. The study also evaluates the performance of these techniques on real network data from the University of Minnesota. The results show that the LOF approach is effective in detecting novel intrusions, including those that are difficult to detect with other methods. The paper concludes that while the LOF approach is promising, further research is needed to improve the detection rate while reducing false alarm rates. The study highlights the importance of developing new anomaly detection algorithms that can handle the high volume, dimensionality, and heterogeneity of network traffic data.This paper presents a comparative study of several anomaly detection schemes for identifying network intrusions. Intrusion detection involves identifying attacks against computers and network infrastructures, with anomaly detection being a key component that identifies deviations from normal behavior. The study evaluates various supervised and unsupervised anomaly detection schemes on the DARPA 1998 dataset and real network data using standard evaluation techniques and specific metrics for detecting attacks involving many connections. The results indicate that some anomaly detection schemes show promise in detecting novel intrusions in both the DARPA 98 data and real network data. The paper discusses two main types of intrusion detection techniques: misuse detection and anomaly detection. Misuse detection labels data as normal or intrusive and uses learning algorithms to retrain models on new data. Anomaly detection, on the other hand, builds models of normal data and detects deviations from this model. Anomaly detection is advantageous as it can detect new types of intrusions as deviations from normal usage, but suffers from a high rate of false alarms due to legitimate system behaviors being flagged as anomalies. The study evaluates several anomaly detection techniques, including outlier detection schemes and unsupervised support vector machines. The paper compares the performance of these techniques using metrics such as burst detection rate, surface area, and response time. The results show that the LOF (Local Outlier Factor) approach performs well in detecting both bursty and single-connection attacks, with high detection rates and low false alarm rates. The nearest neighbor (NN) approach also performs well, particularly for single-connection attacks. The Mahalanobis-based approach is less effective, especially for attacks with complex normal behaviors. The study also evaluates the performance of these techniques on real network data from the University of Minnesota. The results show that the LOF approach is effective in detecting novel intrusions, including those that are difficult to detect with other methods. The paper concludes that while the LOF approach is promising, further research is needed to improve the detection rate while reducing false alarm rates. The study highlights the importance of developing new anomaly detection algorithms that can handle the high volume, dimensionality, and heterogeneity of network traffic data.