We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions

While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.

A New Framework for DDoS Attack Detection and Defense in SDN Environment

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-net.2018.5082

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

Vulnerability retrospection of security solutions for software-defined Cyber–Physical System against DDoS and IoT-DDoS attacks

Distributed Denial of Service (DDoS) attack is one of the most long-lasting problems in network security. Recently, although the DDoS attack mechanisms are widely understood, the problems are becoming more frequent due to the similarity between DDoS attack and normal traffic. In this work, we propose two approaches of DDoS attack detection which are based on the Self-Organizing Map (SOM). The proposed algorithms with their detection architecture are implemented in the Software-Defined Networking (SDN) technology which has the flexibility and programmable abilities. The SDN controller allows us to quickly perform complex classification and detection algorithm. By deploying a testbed environment, we successfully evaluate our proposed algorithms in terms of both accuracy and computational overhead. The experimental results show that these algorithms can reduce the processing time while maintain the suitable accuracy rate.

Luong Xuan Thang

Papers

Self-organizing map-based approaches in DDoS flooding detection using SDN