93,118 research outputs found
Applying an unified access control for IoT-based Intelligent Agent Systems
IEEE 8th International Conference on Service-Oriented Computing and Applications (SOCA), 19/10/2015-21/10/2015, Roma, ItaliaThe rise of the Internet of Things (IoT) paradigm
has allowed the design and development of new services interconnecting
heterogeneous devices. However, the complexity
of these new systems hasn"t been followed by the increase of
intelligence and reasoning of the devices connected. On the
other hand, intelligent agent systems have developed precisely
these characteristics so the combination of both paradigms by
modelling intelligent agents in IoT devices is a very promising
approach that will enable a more powerful and smart IoT. The interconnection
of agents through a Internet-based network implies
addressing critical issues that affect all network communications,
such as security, privacy and access control, specially given the
sensitivity of the information exchanged by agents. In this paper,
we propose the application of User-Managed Access (UMA) to
provide an unified access control schema for an heterogeneous
hybrid architecture of IoT devices and intelligent agents.Ministerio de Economía y Competitivida
Sensor networks security based on sensitive robots agents. A conceptual model
Multi-agent systems are currently applied to solve complex problems. The
security of networks is an eloquent example of a complex and difficult problem.
A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion
Detection is introduced in the current paper. The proposed technique could be
used with machine learning based intrusion detection techniques. The new model
uses the reaction of virtual sensitive robots to different stigmergic variables
in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page
TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System
Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
- …