Nopsense Firewall Owl

 nopsense

Owl

 is an open source firewall/router/IPS computer software distribution based on Debian , its prefer for cyber security engineers  but work in anomaly base in our labs we add a lot of script to make it more detective for new threat and cyber attacks is installed on a physical computer or a virtual machine to make a dedicated firewall/router for a network ,It can be configured and upgraded through a web-based interface, and requires no knowledge of the underlying FreeBSD system to manage.

redstoks

nopSense is commonly deployed as a perimeter firewall, router, wireless access point, DHCP server, DNS server, and as a VPN endpoint. nopSense supports installation of third-party packages like Snort or Squid through its Package Manager.

nopsense, open ,source, firewall,router ,computer,Debian , software ,distribution ,IDS ,IPS ,anomaly ,anomaly based


NOPSENSE ANOMALY BASED IDS

Anomaly detection software leverages a form of artificial intelligence known as machine learning
predictive analytics. The more modern offerings available feature “unsupervised” machine learning and do not require the learning of human-defined models (relationship definitions, rules or thresholds).
Predictive analytics describes the software’s ability to predict a likely value range for the next observed data point based on its corresponding learned baseline of behavior. Again, the more modern approachesactually compute a probability for each new data point based on the learned past behavior. In the best implementations, higher severity alerts are raised for lower probability events.
Machine learning predictive analytics are particularly adept at quickly handling Big Data sets and provide tremendous value by providing insight where human analysis or search-based techniques fall short or are simply impractical.(The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets.(nopsense, open ,source, firewall,router ,computer,Debian , software ,distribution ,IDS ,IPS ,anomaly ,anomaly based ) Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.)
Anomaly detection software can quickly cross-correlate tremendous volumes of security data, establish baselines for normal behaviors, and identify and score anomalous activities based on their rarity, severity or impact (number of related anomalies). The processing speed of the software is typically sufficiently fast
 nopsense is an open source firewall/router/IPS computer software distribution based on Debian , its prefer for cyber security engineers but work in anomaly base in our labs we add a lot of script to make it more detective for new threat and cyber attacks is installed

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.

nopsense is an open source firewall/router/IPS computer software distribution based on Debian , its prefer for cyber security engineers but work in anomaly base in our labs we add a lot of script to make it more detective for new threat and cyber attacks is installed

The constant increase of attacks against networks and their resources (as recently shown by the

The constant increase of attacks against networks and their resources (as recently shown by the CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.CodeRed worm) causes a necessity to protect these valuable assets. Firewalls are now a common installation to repel intrusion attempts in the first place. Intrusion detection systems (IDS), which try to detect malicious activities instead of preventing them, offer additional protection when the first defense perimeter has been penetrated. ID systems attempt to pin down attacks by comparing collected data to predefined signatures known to be malicious (signature based) or to a model of legal behavior (anomaly based).Anomaly based systems have the advantage of being able to detect previously unknown attacks but they suffer from the difficulty to build a solid model of acceptable behavior and the high number of alarms caused by unusual but authorized activities. We present an approach that utilizes application specific knowledge of the network services that should be protected. This information helps to extend current, simple network traffic models to form an application model that allows to detect malicious content hidden in single network packets. We describe the features of our proposed model and present experimental data that underlines the efficiency of our systems.