–Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgrad...

In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative Machine (ML). FL does not share local data but ML models, offering applications in diverse domains. This paper focuses on application to Intrusion Detection Systems (IDSs). There, common criteria compare existing solutions are missing. particular, this survey shows: (i) how FL-based IDSs used different dom...

Intrusion Detection (ID) is the process of monitoring events occurring in a system and signalling responsible parties when interesting (suspicious) activity occurs. Intrusion Detection Systems (IDSs) consist of 1) an agent that collects the information on the stream of monitored events, 2) an analysis engine that detects signs of intrusion, and 3) a response module that generates responses base...

In Ref. [2] some reasons that justify the necessity of a definition of a standard architecture and communication for the intrusion detection systems are described. Some of these reasons are the even increasing amount of intrusion detection systems available in the market, and the fact that intrusions usually involve nets of various companies or various nets of the same company. These nets will ...

Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. In this paper, we propose a new data-mining based technique for intrusion detection using an ensemble of binary clas...

Soft computing techniques are increasingly being used for problem solving. This paper addresses using ensemble approach of different soft computing techniques for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challen...

As the computer environment changes significantly, several mechanisms such as firewall are developed and intrusion detection system (IDS) is the representative among them. However, because many IDSs are modeled and evaluated with known intrusion patterns, they have inertia not to detect the intrusions which are unknown or transformed. In this paper, we propose an IGA-based method to generate th...

In last decades there have been many proposals from the machine learning community in the intrusion detection field. One of the main problems that Intrusion Detection Systems (IDSs) mainly anomaly-based ones have to face are those attacks not previously seen (zero-day attacks). This paper proposes a mutation technique to test and evaluate the performance of several classifier ensembles incorpor...

Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Intrusion detection systems can be misuse-detection or anomaly detection based. Misuse-detection based IDSs can only detect known a...

This paper presents a comprehensive method for evaluating intrusion detection systems (IDSs). It integrates and extends ROC (receiver operating characteristic) and cost analysis methods to provide an expected cost metric. Results are given for determining the optimal operation of an IDS based on this expected cost metric. Results are given for the operation of a single IDS and for a combination...