Visible to the public Biblio

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Lee, T., Chang, L., Syu, C..  2020.  Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

Buenrostro, E. D., Rivera, A. O. G., Tosh, D., Acosta, J. C., Njilla, L..  2019.  Evaluating Usability of Permissioned Blockchain for Internet-of-Battlefield Things Security. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :841—846.

Military technology is ever-evolving to increase the safety and security of soldiers on the field while integrating Internet-of-Things solutions to improve operational efficiency in mission oriented tasks in the battlefield. Centralized communication technology is the traditional network model used for battlefields and is vulnerable to denial of service attacks, therefore suffers performance hazards. They also lead to a central point of failure, due to which, a flexible model that is mobile, resilient, and effective for different scenarios must be proposed. Blockchain offers a distributed platform that allows multiple nodes to update a distributed ledger in a tamper-resistant manner. The decentralized nature of this system suggests that it can be an effective tool for battlefields in securing data communication among Internet-of-Battlefield Things (IoBT). In this paper, we integrate a permissioned blockchain, namely Hyperledger Sawtooth, in IoBT context and evaluate its performance with the goal of determining whether it has the potential to serve the performance needs of IoBT environment. Using different testing parameters, the metric data would help in suggesting the best parameter set, network configuration and blockchain usability views in IoBT context. We show that a blockchain-integrated IoBT platform has heavy dependency on the characteristics of the underlying network such as topology, link bandwidth, jitter, and other communication configurations, that can be tuned up to achieve optimal performance.

Al-Haj, Ali, Aziz, Benjamin.  2019.  Enforcing Multilevel Security Policies in Database-Defined Networks using Row-Level Security. 2019 International Conference on Networked Systems (NetSys). :1-6.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

Benamira, Elias, Merazka, Fatiha, Kurt, Gunes Karabulut.  2018.  Joint Channel Coding and Cooperative Network Coding on PSK Constellations in Wireless Networks. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT). :132–137.
In this paper, we consider the application of Reed-Solomon (RS) channel coding for joint error correction and cooperative network coding on non-binary phase shift keying (PSK) modulated signals. The relay first decodes the RS channel coded messages received each in a time slot from all sources before applying network coding (NC) by the use of bit-level exclusive OR (XOR) operation. The network coded resulting message is then channel encoded before its transmission to the next relay or to the destination according to the network configuration. This scenario shows superior performance in comparison with the case where the relay does not perform channel coding/decoding. For different orders of PSK modulation and different wireless configurations, simulation results demonstrate the improvements resulting from the use of RS channel codes in terms of symbol error rate (SER) versus signal-to-noise ratio (SNR).
Alcorn, J., Melton, S., Chow, C. E..  2017.  SDN data path confidence analysis. 2017 IEEE Conference on Dependable and Secure Computing. :209–216.

The unauthorized access or theft of sensitive, personal information is becoming a weekly news item. The illegal dissemination of proprietary information to media outlets or competitors costs industry untold millions in remediation costs and losses every year. The 2013 data breach at Target, Inc. that impacted 70 million customers is estimated to cost upwards of 1 billion dollars. Stolen information is also being used to damage political figures and adversely influence foreign and domestic policy. In this paper, we offer some techniques for better understanding the health and security of our networks. This understanding will help professionals to identify network behavior, anomalies and other latent, systematic issues in their networks. Software-Defined Networks (SDN) enable the collection of network operation and configuration metrics that are not readily available, if available at all, in traditional networks. SDN also enables the development of software protocols and tools that increases visibility into the network. By accumulating and analyzing a time series data repository (TSDR) of SDN and traditional metrics along with data gathered from our tools we can establish behavior and security patterns for SDN and SDN hybrid networks. Our research helps provide a framework for a range of techniques for administrators and automated system protection services that give insight into the health and security of the network. To narrow the scope of our research, this paper focuses on a subset of those techniques as they apply to the confidence analysis of a specific network path at the time of use or inspection. This confidence analysis allows users, administrators and autonomous systems to decide whether a network path is secure enough for sending their sensitive information. Our testing shows that malicious activity can be identified quickly as a single metric indicator and consistently within a multi-factor indicator analysis. Our research includes the implementation of - hese techniques in a network path confidence analysis service, called Confidence Assessment as a Service. Using our behavior and security patterns, this service evaluates a specific network path and provides a confidence score for that path before, during and after the transmission of sensitive data. Our research and tools give administrators and autonomous systems a much better understanding of the internal operation and configuration of their networks. Our framework will also provide other services that will focus on detecting latent, systemic network problems. By providing a better understanding of network configuration and operation our research enables a more secure and dependable network and helps prevent the theft of information by malicious actors.