Biblio

Filters: Keyword is Automated Synthesis Framework for Network Security and Resilience  [Clear All Filters]
2018-10-15
Jiaqi Yan, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology, Cheol Won Lee, National Research Institute, South Korea, Ping Liu, Illinois Institute of Technology.  2018.  A Comparative Study of Off-Line Deep Learning Based Network Intrusion Detection. 10th International Conference on Ubiquitous and Future Networks.

Abstract—Network intrusion detection systems (NIDS) are essential security building-blocks for today’s organizations to ensure safe and trusted communication of information. In this paper, we study the feasibility of off-line deep learning based NIDSes by constructing the detection engine with multiple advanced deep learning models and conducting a quantitative and comparative evaluation of those models. We first introduce the general deep learning methodology and its potential implication on the network intrusion detection problem. We then review multiple machine learning solutions to two network intrusion detection tasks (NSL-KDD and UNSW-NB15 datasets). We develop a TensorFlow-based deep learning library, called NetLearner, and implement a handful of cutting-edge deep learning models for NIDS. Finally, we conduct a quantitative and comparative performance evaluation of those models using NetLearner.

Santhosh Prabhu, University of Illinois at Urbana-Champaign, Gohar Irfan Chaudhry, University of Illinois at Urbana-Champaign, Brighten Godfrey, University of Illinois at Urbana-Champaign, Matthew Caesar, University of Illinois at Urbana-Champaign.  2018.  High Coverage Testing of Softwarized Networks. ACM SIGCOMM 2018 Workshop on Security in Softwarized Networks: Prospects and Challenges.

Network operators face a challenge of ensuring correctness as networks grow more complex, in terms of scale and increasingly in terms of diversity of software components. Network-wide verification approaches can spot errors, but assume a simplified abstraction of the functionality of individual network devices, which may deviate from the real implementation. In this paper, we propose a technique for high-coverage testing of end-to-end network correctness using the real software that is deployed in these networks. Our design is effectively a hybrid, using an explicit-state model checker to explore all network-wide execution paths and event orderings, but executing real software as subroutines for each device. We show that this approach can detect correctness issues that would be missed both by existing verification and testing approaches, and a prototype implementation suggests the technique can scale to larger networks
with reasonable performance.

Christopher Hannon, Illinois Institute of Technology, Jiaqi Yan, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology, Chen Chen, Argonne National Laboratory, Jianhui Wang, Argonne National Laboratory.  2018.  Combining Simulation and Emulation Systems for Smart Grid Planning and Evaluation. CM Transactions on Modeling and Computer Simulation (TOMACS) – Special Issue on PADS. 28(4)

Software-defined networking (SDN) enables efficient networkmanagement. As the technology matures, utilities are looking to integrate those benefits to their operations technology (OT) networks. To help the community to better understand and evaluate the effects of such integration, we develop DSSnet, a testing platform that combines a power distribution system simulator and an SDN-based network emulator for smart grid planning and evaluation. DSSnet relies on a container-based virtual time system to achieve efficient synchronization between the simulation and emulation systems. To enhance the system scalability and usability, we extend DSSnet to support a distributed controller environment. To enhance system fidelity, we extend the virtual time system to support kernel-based switches. We also evaluate the system performance of DSSnet and demonstrate the usability of DSSnet with a resilient demand response application case study.

2019-01-10
Christopher Hannon, Illinois Institute of Technology, Nandakishore Santhi, Los Alamos National Laboratory, Stephan Eidenbenz, Los Alamos National Laboratory, Jason Liu, Florida International University, Dong Jin, Illinois Institute of Technology.  2018.  Just-In-Time Parallel Simulation. 2018 Winter Simulation Conference (WSC).

Due to the evolution of programming languages, interpreted languages have gained widespread use in scientific and research computing. Interpreted languages excel at being portable, easy to use, and fast in prototyping than their ahead-of-time (AOT) counterparts, including C, C++, and Fortran. While traditionally considered as slow to execute, advancements in Just-in-Time (JIT) compilation techniques have significantly improved the execution speed of interpreted languages and in some cases outperformed AOT languages. In this paper, we explore some challenges and design strategies in developing a high performance parallel discrete event simulation engine, called Simian, written with interpreted languages with JIT capabilities, including Python, Lua, and Javascript. Our results show that Simian with JIT performs similarly to AOT simulators, such as MiniSSF and ROSS. We expect that with features like good performance, userfriendliness, and portability, the just-in-time parallel simulation will become a common choice for modeling and simulation in the near future.
 

2018-07-13
Yangfend Qu, Illinois Institute of Technology, Xin Liu, Illinois Institute of Technology, Dong Jin, Illinois Institute of Technology, Yuan Hong, Illinois Institute of Technology, Chen Chen, Argonne National Laboratory.  2018.  Enabling a Resilient and Self-healing PMU Infrastructure Using Centralized Network Control. 2018 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization.

Many of the emerging wide-area monitoring protection and control (WAMPAC) applications in modern electrical grids rely heavily on the availability and integrity of widespread phasor measurement unit (PMU) data. Therefore, it is critical to protect PMU networks against growing cyber-attacks and system faults. In this paper, we present a self-healing PMU network design that considers both power system observability and communication network characteristics. Our design utilizes centralized network control, such as the emerging software-defined networking (SDN) technology, to design resilient network self-healing algorithms against cyber-attacks. Upon detection of a cyber-attack, the PMU network can reconfigure itself to isolate compromised devices and re-route measurement
data with the goal of preserving the power system observability. We have developed a proof-of-concept system in a container-based network testbed using integer linear programming to solve a graphbased PMU system model.We also evaluate the system performance regarding the self-healing plan generation and installation using the IEEE 30-bus system.