Approaching Real-Time Intrusion Detection through MOVICAB-IDS

Navarro, Martí; Herrero, Álvaro; Corchado, Emilio; Julián, Vicente

doi:10.1007/978-3-642-13161-5_2

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…For example, in [34], the author considers the network as a set of nodes and opts for a multi-agent system as a solution to detect suspicious activities at the level of all nodes. The author in [35] highlights a hybrid intrusion detection system called MOViCAB IDS which is based on artificial neural networks and multi-agent architecture. In [36], a distributed intrusion detection system architecture is proposed and it is based on mobile agents allowing decision making and agent replication.…”

Section: State Of the Artmentioning

confidence: 99%

Towards a Multi-Agent based Network Intrusion Detection System for a Fleet of Drones

Ouiazzane¹,

Barramou²,

Addou³

2020

IJACSA

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The objective of this research work is to propose a new model of intrusion detection system for a fleet of UAVs deployed with an ad hoc communication architecture. The security of a drone fleet is rarely addressed by the scientific community, and most research has focused on routing protocols and battery autonomy, while ignoring the security aspect. The multi-agent paradigm is considered the most adequate and appropriate solution to model an effective intrusion detection system capable of detecting intrusions targeting a drone fleet. Multi-agent systems can perfectly address the security problem of a drone fleet, given the mobility, autonomy, cooperation and distribution characteristics present in the network linking the different nodes of the fleet. The proposed model consists of a set of cooperative, autonomous, communicating, learning and intelligent agents that collaborate with each other to carry out intrusion and suspicious activity detection missions that can target the network of a fleet of drones. Our system is autonomous and can detect known and unknown cyber attacks in real time without the need for human experts, who generally design the signatures of known attacks for conventional intrusion detection systems.

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Section: State Of the Artmentioning

confidence: 99%