Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current... more
Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging
In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse... more
In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.
Conditional Anomaly Detection ... However, in contrast to problems in supervised learning where studies of classification accuracy are the norm, little research has systematically addressed the issue of accuracy in general-purpose... more
Conditional Anomaly Detection ... However, in contrast to problems in supervised learning where studies of classification accuracy are the norm, little research has systematically addressed the issue of accuracy in general-purpose unsupervised anomaly detection meth-ods. ...
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep... more
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
