Modern Graph Theory

【臨床医学の展望 2012】 呼吸器病学

A Software Defined Network (SDN) is a new network architecture that provides central control over the network. Although central control is the major advantage of SDN, it is also a single point of failure if it is made unreachable by a Distributed Denial of Service (DDoS) Attack. To mitigate this threat, this paper proposes to use the central control of SDN for attack detection and introduces a solution that is effective and lightweight in terms of the resources that it uses. More precisely, this paper shows how DDoS attacks can exhaust controller resources and provides a solution to detect such attacks based on the entropy variation of the destination IP address. This method is able to detect DDoS within the first five hundred packets of the attack traffic.

http://cial.csie.ncku.edu.tw/presentation/group_pdf/Early%20Detection%20of%20DDoS%20Attacks%20against%20SDN%20Controllers.pdf

Early detection of DDoS attacks against SDN controllers

Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.

Hybrid SDN Networks: A Survey of Existing Approaches

Broadcast or flooding is a dissemination technique of paramount importance in wireless ad hoc networks. The broadcast scheme is widely used within routing protocols by a wide range of wireless ad hoc networks such as mobile ad hoc networks, vehicular ad hoc networks, and wireless sensor networks, and used to spread emergency messages in critical scenarios after a disaster scenario and/or an accidents. As the type broadcast scheme used plays an important role in the performance of the network, it has to be selected carefully. Though several types of broadcast schemes have been proposed, probabilistic broadcast schemes have been demonstrated to be suitable schemes for wireless ad hoc networks due to a range of benefits offered by them such as low overhead, balanced energy consumption, and robustness against failures and mobility of nodes. In the last decade, many probabilistic broadcast schemes have been proposed by researchers. In addition to reviewing the main features of the probabilistic schemes found in the literature, we also present a classification of the probabilistic schemes, an exhaustive review of the evaluation methodology including their performance metrics, types of network simulators, their comparisons, and present some examples of real implementations, in this paper.

/pdf/a-survey-on-probabilistic-broadcast-schemes-for-wireless-ad-7b3rmky4fl.pdf

A survey on probabilistic broadcast schemes for wireless ad hoc networks

The threat of Distributed Denial of Service (DDoS) has become a major issue in network security and is difficult to detect because all DDoS traffics have normal packet characteristics. Various detection and defense algorithms have been studied. One of them is an entropy-based intrusion detection approach that is a powerful and simple way to identify abnormal conditions from network channels. However, the burden of computing information entropy values from heavy flow still exists. To reduce the computing time, we have developed a DDoS detection scheme using a compression entropy method. It allows us to significantly reduce the computation time for calculating information entropy. However, our experiment suggests that the compression entropy approach tends to be too sensitive to verify real network attacks and produces many false negatives. In this paper, we propose a fast entropy scheme that can overcome the issue of false negatives and will not increase the computational time. Our simulation shows that the fast entropy computing method not only reduced computational time by more than 90% compared to conventional entropy, but also increased the detection accuracy compared to conventional and compression entropy approaches.

/pdf/an-efficient-and-reliable-ddos-attack-detection-using-a-fast-4q40el89bp.pdf

An efficient and reliable DDoS attack detection using a fast entropy computation method

Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level. What adds to the challenge is that cloud applications impose quality of service (QoS) requirements and have various resource demands requiring a customized scaling approach. For example, real-time applications require near real time response time as a QoS. Existing autoscaling technologies such as Kubernetes offer some customization to a set of threshold values for autoscaling. The challenge is identifying the right values for the different autoscaling parameters that will guarantee QoS in a changing dynamic environment. Advancements in machine learning and reinforcement learning (RL) provides a means for autoscaling in cloud applications with no domain knowledge. In this article, we introduce an intelligent autonomous autoscaling system for microservices autoscaling in the cloud with QoS constraints. The system consists of two modules. The first module identifies the microservice resource demand via a generic autoscaling algorithm deployed on the Google Kubernetes Engine (GKE). Our algorithm adapts the Kubernetes autoscaling paradigm based on the application resource requirements. The second module uses reinforcement learning agents to learn and identify the autoscaling threshold values based on the resource demand and QoS. Experimental results show an enhancement in the microservice response time up to 20% compared to the default autoscaling paradigm. In addition, the RL agents can identify the autoscaling threshold values while maintaining a response time below the QoS constraint. Our proposed work provides a customized autoscaling solution for microservices in cloud applications while adhering to QoS constraints with minimum user interaction.

/pdf/intelligent-autoscaling-of-microservices-in-the-cloud-for-2eunva0txm.pdf

Intelligent Autoscaling of Microservices in the Cloud for Real-Time Applications

Cloud applications are becoming more containerized in nature. Developing a cloud application based on a microservice architecture imposes different challenges including scalability at the container level. What adds to the challenge is that applications have different QoS requirements and different characteristics requiring a customized scaling approach. In this paper, we present an agnostic approach algorithm for microservices autoscaling deployed on the Google Kubernetes Engine. Our algorithm adapts the Kubernetes autoscaling paradigm based on the application characteristics and resource requirements. Initial testing of the algorithm on different microservices requirements show an enhancement in the microservice response time up to 20% compared to the default autoscaling paradigm.

Agnostic Approach for Microservices Autoscaling in Cloud Applications

Recently, the threat of DDoS (Distributed Denial-of-Service) attacks is growing continuously and acquiring attacking tools via Internet is getting easy. One of the researches introduced a fast method to detect attacks using modified information entropy (so called Fast Entropy). Fast Entropy shows the significant reduce of computational time compared to conventional entropy computation while it maintains detection accuracy. However, Fast Entropy needs the manual threshold settings during detection process which is not realistic in real detection facility. We introduce adaptive detector with dynamic detection window size and adaptive threshold shifting using Fast Entropy, called AFEA (Adaptive DDoS attack detection using Fast Entropy Approach). Our adaptive DDoS detector successfully demonstrates that its performance of the DDoS detection can be enhanced by the best result of Fast Entropy detection scheme without manual threshold setting and system training while it maintains the same computational time of Fast Entropy detection scheme. In addition, we found that Dynamic AFEA can enhance detection level more than fixed (non-dynamic) one when it is equipped with Fast Entropy

Adaptive DDoS Detector Design Using Fast Entropy Computation Method

A variety of ad hoc routing protocols based on a simple flooding routing method have been developed under the constraints of the limited transmission range of wireless network interfaces and other limited mobility resources in MANETs. In this paper we discuss the performance of a gossip-based routing and flooding protocol based on ad hoc on-demand routing protocols in order to identify the strengths and weaknesses of each protocol and suggest appropriate MANET environments for each routing protocol. To accomplish our goals, we selected six performance metrics: packet delivery fraction, average end-to-end delay of data packets, routing overhead, normalized routing load, throughput, and average node energy used. We performed simulations of three ad hoc routing protocols: AODV+G, AODV, and DSR, using the ns-2 network simulation tool.

/pdf/performance-study-of-ad-hoc-routing-protocols-with-gossip-43s23qc3hi.pdf

Ilkyeun Ra

Papers

An efficient and reliable DDoS attack detection using a fast entropy computation method

Intelligent Autoscaling of Microservices in the Cloud for Real-Time Applications

Agnostic Approach for Microservices Autoscaling in Cloud Applications

Adaptive DDoS Detector Design Using Fast Entropy Computation Method

Performance study of ad hoc routing protocols with gossip-based approach