scholarly journals A Semistructured Random Identifier Protocol for Anonymous Communication in SDN Network

2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Yulong Wang ◽  
Junjie Yi ◽  
Jun Guo ◽  
Yanbo Qiao ◽  
Mingyue Qi ◽  
...  
Keyword(s):  
Binary Tree ◽  
Network Performance ◽  
Traffic Analysis ◽  
Transport Layer ◽  
Local Network ◽  
Anonymous Communication ◽  
Moving Target ◽  
New Paradigm ◽  
Network Defense ◽  
Attack Surfaces

Traffic analysis is an effective mean for gathering intelligence from within a large enterprise’s local network. Adversaries are able to monitor all traffic traversing a switch by exploiting just one vulnerability in it and obtain valuable information (e.g., online hosts and ongoing sessions) for further attacking, while administrators have to patch all switches as soon as possible in hope of eliminating the vulnerability in time. Moving Target Defense (MTD) is a new paradigm for reobtaining the upper hand in network defense by dynamically changing attack surfaces of the network. In this paper, we propose U-TRI (unlinkability through random identifier) as a moving target technique for changing the information-leaking identifiers within PDUs for SDN network. U-TRI is based on VIRO protocol and implemented with the help of OpenFlow protocol. U-TRI employs an independent, binary tree-structured, periodically and randomly updating identifier to replace the first part of the static MAC address in PDU, and assigns unstructured random values to the remaining part of the MAC address. U-TRI also obfuscates identifiers in the network layer and transport layer in an unstructured manner. Such a semistructured random identifier enables U-TRI to significantly weaken the linkage between identifiers and end-hosts as well as communication sessions, thus providing anonymous communication in SDN network. The result of analysis and experiments indicates that U-TRI dramatically increases the difficulty of traffic analysis with acceptable burdens on network performance.

scholarly journals Achieving Sender Anonymity in Tor against the Global Passive Adversary

2021 ◽  
Vol 12 (1) ◽  
pp. 137
Author(s):  
Francesco Buccafurri ◽  
Vincenzo De Angelis ◽  
Maria Francesca Idone ◽  
Cecilia Labrini ◽  
Sara Lazzaro
Keyword(s):  
Network Performance ◽  
Experimental Validation ◽  
Traffic Analysis ◽  
The Internet ◽  
Anonymous Communication ◽  
Threat Model ◽  
Wide Usage

Tor is the de facto standard used for anonymous communication over the Internet. Despite its wide usage, Tor does not guarantee sender anonymity, even in a threat model in which the attacker passively observes the traffic at the first Tor router. In a more severe threat model, in which the adversary can perform traffic analysis on the first and last Tor routers, relationship anonymity is also broken. In this paper, we propose a new protocol extending Tor to achieve sender anonymity (and then relationship anonymity) in the most severe threat model, allowing a global passive adversary to monitor all of the traffic in the network. We compare our proposal with Tor through the lens of security in an incremental threat model. The experimental validation shows that the price we have to pay in terms of network performance is tolerable.

Quantifying & minimizing attack surfaces containing moving target defenses

2015 ◽  
Author(s):  
Nathaniel Soule ◽  
Borislava Simidchieva ◽  
Fusun Yaman ◽  
Ronald Watro ◽  
Joseph Loyall ◽  
...  
Keyword(s):  
Moving Target ◽  
Attack Surfaces

scholarly journals Moving Target Network Defense Effectiveness Evaluation Based on Change-Point Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Lei ◽  
Duo-he Ma ◽  
Hong-qi Zhang ◽  
Li-ming Wang
Keyword(s):  
Change Point ◽  
Evaluation Method ◽  
Change Point Detection ◽  
Effectiveness Evaluation ◽  
Moving Target ◽  
Multilayer Network ◽  
Network Resource ◽  
Network Defense ◽  
Target Network ◽  
Point Detection

In order to evaluate the effectiveness of moving target network defense, a dynamic effectiveness evaluation approach based on change-point detection is presented. Firstly, the concept of multilayer network resource graph is defined, which helps establish the relationship between the change of resource vulnerability and the transfer of network node state. Secondly, a change-point detection and standardized measurement algorithm is proposed. Consequently, it improves the efficiency of evaluation by measuring the change-point dynamically and enhancing the accuracy of evaluation based on multilayer network resource graph. What’s more, in order to evaluate the defense effectiveness comprehensively, defense cost and benefits are set as evaluation indicators. Finally, experimental analysis, represented by MT6D and DNAT, proves the feasibility of the proposed evaluation method and the accuracy of the evaluation results.

Quality Evaluation of Cloud and Fog Computing Services in 5G Networks

2019 ◽  
pp. 1-36 ◽  
Author(s):  
Stojan Kitanov ◽  
Borislav Popovski ◽  
Toni Janevski
Keyword(s):  
Network Performance ◽  
Fog Computing ◽  
High Mobility ◽  
Network Control ◽  
5G Networks ◽  
New Paradigm ◽  
Network Function ◽  
Computing Services ◽  
5G Network ◽  
Intelligent Networking

Because of the increased computing and intelligent networking demands in 5G network, cloud computing alone encounters too many limitations, such as requirements for reduced latency, high mobility, high scalability, and real-time execution. A new paradigm called fog computing has emerged to resolve these issues. Fog computing distributes computing, data processing, and networking services to the edge of the network, closer to end users. Fog applied in 5G significantly improves network performance in terms of spectral and energy efficiency, enable direct device-to-device wireless communications, and support the growing trend of network function virtualization and separation of network control intelligence from radio network hardware. This chapter evaluates the quality of cloud and fog computing services in 5G network, and proposes five algorithms for an optimal selection of 5G RAN according to the service requirements. The results demonstrate that fog computing is a suitable technology solution for 5G networks.

scholarly journals Strengthening MT6D Defenses with LXC-Based Honeypot Capabilities

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Dileep Basam ◽  
J. Scot Ransbottom ◽  
Randy Marchany ◽  
Joseph G. Tront
Keyword(s):  
Research Effort ◽  
Network Activity ◽  
Local Network ◽  
Moving Target ◽  
Database Integration ◽  
Proof Of Concept ◽  
Traffic Pattern ◽  
On Demand ◽  
Previous Solution ◽  
Ipv6 Networks

Moving Target IPv6 Defense (MT6D) imparts radio-frequency hopping behavior to IPv6 networks by having participating nodes periodically hop onto new addresses while giving up old addresses. Our previous research efforts implemented a solution to identify and acquire these old addresses that are being discarded by MT6D hosts on a local network besides being able to monitor and visualize the incoming traffic on these addresses. This was essentially equivalent to forming a darknet out of the discarded MT6D addresses, but the solution presented in the previous research effort did not include database integration for it to scale and be extended. This paper presents a solution with a new architecture that not only extends the previous solution in terms of automation and database integration but also demonstrates the ability to deploy a honeypot on a virtual LXC (Linux Container) on-demand based on any interesting traffic pattern observed on a discarded address. The proposed architecture also allows an MT6D host to query the solution database for network activity on its relinquished addresses as a JavaScript Object Notation (JSON) object. This allows an MT6D host to identify suspicious activity on its discarded addresses and strengthen the MT6D scheme parameters accordingly. We have built a proof-of-concept for the proposed solution and analyzed the solution’s feasibility and scalability.

scholarly journals Reinforcement Learning for Generating Secure Configurations

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2392
Author(s):  
Shuvalaxmi Dass ◽  
Akbar Siami Namin
Keyword(s):  
Reinforcement Learning ◽  
Software Systems ◽  
Moving Target ◽  
Large Set ◽  
Software System ◽  
Secure Software ◽  
Attack Surface ◽  
Diversification Techniques ◽  
System Configurations ◽  
Attack Surfaces

Many security problems in software systems are because of vulnerabilities caused by improper configurations. A poorly configured software system leads to a multitude of vulnerabilities that can be exploited by adversaries. The problem becomes even more serious when the architecture of the underlying system is static and the misconfiguration remains for a longer period of time, enabling adversaries to thoroughly inspect the software system under attack during the reconnaissance stage. Employing diversification techniques such as Moving Target Defense (MTD) can minimize the risk of exposing vulnerabilities. MTD is an evolving defense technique through which the attack surface of the underlying system is continuously changing. However, the effectiveness of such dynamically changing platform depends not only on the goodness of the next configuration setting with respect to minimization of attack surfaces but also the diversity of set of configurations generated. To address the problem of generating a diverse and large set of secure software and system configurations, this paper introduces an approach based on Reinforcement Learning (RL) through which an agent is trained to generate the desirable set of configurations. The paper reports the performance of the RL-based secure and diverse configurations through some case studies.

Improving Action Recognition via Temporal and Complementary Learning

2021 ◽  
Vol 12 (3) ◽  
pp. 1-24
Author(s):  
Nour Eldin Elmadany ◽  
Yifeng He ◽  
Ling Guan
Keyword(s):  
Action Recognition ◽  
Network Performance ◽  
Recognition Performance ◽  
Information Modeling ◽  
Local Network ◽  
Temporal Representation ◽  
Learning Techniques ◽  
Temporal Learning ◽  
Relational Network

In this article, we study the problem of video-based action recognition. We improve the action recognition performance by finding an effective temporal and appearance representation. For capturing the temporal representation, we introduce two temporal learning techniques for improving long-term temporal information modeling, specifically Temporal Relational Network and Temporal Second-Order Pooling-based Network. Moreover, we harness the representation using complementary learning techniques, specifically Global-Local Network and Fuse-Inception Network. Performance evaluation on three datasets (UCF101, HMDB-51, and Mini-Kinetics-200) demonstrated the superiority of the proposed framework compared to the 2D Deep ConvNets-based state-of-the-art techniques.

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