8,809 research outputs found
A Machine-Synesthetic Approach To DDoS Network Attack Detection
In the authors' opinion, anomaly detection systems, or ADS, seem to be the
most perspective direction in the subject of attack detection, because these
systems can detect, among others, the unknown (zero-day) attacks. To detect
anomalies, the authors propose to use machine synesthesia. In this case,
machine synesthesia is understood as an interface that allows using image
classification algorithms in the problem of detecting network anomalies, making
it possible to use non-specialized image detection methods that have recently
been widely and actively developed. The proposed approach is that the network
traffic data is "projected" into the image. It can be seen from the
experimental results that the proposed method for detecting anomalies shows
high results in the detection of attacks. On a large sample, the value of the
complex efficiency indicator reaches 97%.Comment: 12 pages, 2 figures, 5 tables. Accepted to the Intelligent Systems
Conference (IntelliSys) 201
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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