A Statistical Model for Early Detection of DDoS Attacks on Random Targets in SDN

Wireless Personal Communications - Software Defined Networks (SDNs) have accelerated and simplified the management, configuration and error detection in today’s networking systems. However,...     

A Lightweight Image Encryption Algorithm for Secure Communications in Multimedia Internet of Things

Wireless Personal Communications - Devices in the Internet of Things (IoT) have resource constraints in terms of energy, computing power, and memory that make them vulnerable to some security...     

Game theoretic bandwidth procurement mechanisms in live P2P streaming systems

With the dramatic growth of Internet video streaming applications, resource provisioning for video streaming systems to satisfy their upload bandwidth deficit is a challenging task. The design of incentive mechanisms for taking advantage of unused upload capability of helper peers is proven to be a viable, cost-effective solution for this problem. The existing incentive mechanisms for video streaming systems do not consider the hierarchical nature of helper-server interactions, the limited budget of server to procure the needed bandwidth, and limited information of helpers about the other parties in the system. To address these issues, we designed cooperation mechanisms for two different cases: in the full-information case where the server has the full control over the amount of payments to each helper, a Stackelberg helping game is formulated in which the server as leader determines the amount of payment for each helper and then, helpers as followers decide on their amount of contributed bandwidth accordingly. We characterize the Stackelberg Equilibrium (SE) point of game in which the server shares the benefits of bandwidth sharing with the helpers through a market mechanism. In the partial information case where the helpers’ cost and utility functions are private and unknown to the server, we propose a budget-limited reverse auction in which the helpers, in contrast to the former case, announce the lowest price at which they are willing to sell their upload bandwidth first and the server then selects a subset of the helpers and pays them proportional to their contributions. The results of extensive simulations reveal that the mechanisms are truthful and result in lower server workload and higher peers’ streaming rate and delivery ratio.     

A Stochastic Approximation Approach for Foresighted Task Scheduling in Cloud Computing

Wireless Personal Communications - With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to...     

Game‐theoretic auction design for bandwidth sharing in helper‐assisted P2P streaming

Video traffic offloading through the helper peers can significantly alleviate the server workload and enhance users' quality of experience. In this paper, the complex interactions of a number of helpers seeking to trade their bandwidth surplus in a peer‐to‐peer market are studied using non‐cooperative game theory and double auction. In particular, a non‐cooperative game is formulated among the helpers in which each helper can make a decision on the maximum amount of upload bandwidth surplus that it is willing to sell so as to maximize its utility function that reflects the tradeoff between the benefits from bandwidth sharing and its associated costs. Then, the price governing the bandwidth trading market between the helpers and peers is determined via a truthful double auction mechanism. To solve the game, an algorithm based on the best response dynamics is proposed using which helpers can reach a Nash equilibrium point. Simulation results exhibit that the proposed algorithm achieves significant performance improvements in terms of utility per selling helpers. Copyright © 2015 John Wiley & Sons, Ltd.     

A hybrid machine learning approach for detecting unprecedented DDoS attacks

Service availability plays a vital role on computer networks, against which Distributed Denial of Service (DDoS) attacks are an increasingly growing threat each year. Machine learning (ML) is a promising approach widely used for DDoS detection, which obtains satisfactory results for pre-known attacks. However, they are almost incapable of detecting unknown malicious traffic. This paper proposes a novel method combining both supervised and unsupervised algorithms. First, a clustering algorithm separates the anomalous traffic from the normal data using several flow-based features. Then, using certain statistical measures, a classification algorithm is used to label the clusters. Employing a big data processing framework, we evaluate the proposed method by training on the CICIDS2017 dataset and testing on a different set of attacks provided in the more up-to-date CICDDoS2019. The results demonstrate that the Positive Likelihood Ratio (LR+) of our method is approximately 198% higher than the ML classification algorithms.