Coordinated attacks, such as large-scale stealthy scans, worm outbreaks and distributed denial-of-service (DDoS) attacks, occur in multiple networks simultaneously. Such attacks are extremely difficult to detect using isolated intrusion detection systems (IDSs) that monitor only a limited portion of the Internet. In this paper, we summarize the current research directions in detecting such atta...

Intrusion Detection Systems (IDSs) are one of the key components for securing computing infrastructures. Their objective is to protect against attempts to violate defense mechanisms. Indeed, IDSs themselves are part of the computing infrastructure, and thus they may be attacked by the same adversaries they are designed to detect. This is a relevant aspect, especially in safety-critical environm...

Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs), where different techniques and architectures applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database known intrusion experiences, as signature-based, or learning normal behavior system reporting whether anomalous occur, named anomaly-based. This work is d...

Intrusion Detection Systems (IDSs) for Mobile Ad hoc Networks (MANETs) are necessary when we deploy MANETs in reality. In this paper, we focus on the protection of MANET routing protocols. Therefore, we present a new intrusion detection architecture based on quantitative, agents, and clusters that is suitable for multi-hop mobile ad hoc networks. It detects nodes misbehavior and anomalies in pa...

Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed identify network anomalies. Most existing machine learning-based IDSs learn patterns from features extracted traffic flows, and deep approaches can data distribution raw differentiate normal anomalous flows. Although having used in real world widely, above methods are vulnerable some types of atta...

Current intrusion detection systems (IDSs) usually focus on detecting low-level attacks and/or anomalies; none of them can capture the logical steps or attack strategies behind these attacks. Consequently, the IDSs usually generate a large amount of alerts. In situations where there are intensive intrusive actions, not only will actual alerts be mixed with false alerts, but the amount of alerts...

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Intrusion detection has been studied for about twenty years since the Anderson’s report. However, intrusion detection techniques are still far from perfect. Current intrusion detection systems (IDSs) usually generate a large amount of false alerts and cannot fully detect novel attacks or variations of known attacks. In addition, all the existing IDSs focus on low-level attacks or anomalies; non...

As the capabilities of intrusion detection systems (IDSs) advance, attackers may disable organizations’ IDSs before attempting to penetrate more valuable targets. To counter this threat, we present an IDS architecture that is resistant to denial-of-service attacks. The architecture frustrates attackers by making IDS components invisible to attackers’ normal means of “seeing” in a network. Upon ...

Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security.There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artifi...