BGP Blackholing is an effective mitigation solution for networks to counter the frequent Distributed Denial of Service (DDoS) attacks. It enables to drop all network traffic that is directed towards a particular victim prefix under DDoS attack, ideally, as close to the source as possible. Despite its huge importance in the Internet, there is no tool available for the real-time visualization of BGP Blackholing activity. Visualization is one of the most powerful techniques for network operators to monitor network activity. From discovering successful network topology to expose anomalous behaviors in networks, easy-to-use visualizations are powerful weapons to capture important patterns on the Internet traffic[1, 5]. In this work, we propose a first real-time BGP Blackholing Visibility Tool (named as SmartX BGP-BVT) to detect and visualize community based BGP Blackholing on live BGP data. This tool will be helpful for network operators and researchers interested in BGP Blackholing service and DDoS mitigation in the Internet.

Accurate Internet traffic predictions can provide support to network operators for applications such as traffic engineering, bandwidth allocation, anomaly detection, etc. We apply and compare different forecasting techniques (traditional and machine learning-based techniques) on real BGP data that is collected from two well-known Internet exchange points (IXPs) to derive BGP future volume-based predictions. Our experimental evaluation shows that multivariate Bayesian Ridge outperforms all other forecasting techniques we consider. Through univariate LSTM, we are able to predict new BGP volume-based features. Furthermore, to study the impact of dataset size on BGP forecasting, we perform experiments on three BGP dataset sizes, i.e., Short (one-month), Medium (three-months), and Long (five-months) Periods. Our results show that the Short-Period BGP dataset seems to be sufficient for getting accurate predictions. We also present a use case study (forecast Google Leak anomaly) that supports our experimental evaluations. We provide our collected BGP datasets publically which will be helpful to perform further research experiments and analysis regarding BGP traffic predictions.

The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.

Forecasting network traffic is a challenging task for better network management. In this poster, we present a Border Gateway Protocol (BGP) traffic volume prediction framework that uses real BGP data from two famous Internet exchange points (IXPs) to train the LSTM network and generate future volume-based predictions. Our experimental evaluation shows that LSTM can indeed be used to predict BGP traffic volume with a very low prediction errors.