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2021-07-08
Chandavarkar, B. R., Gadagkar, Akhilraj V..  2020.  Mitigating Localization and Neighbour Spoofing Attacks in Underwater Sensor Networks. 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—5.
The location information of a node is one of the essential attributes used in most underwater communication routing algorithms to identify a candidate forwarding node by any of the sources. The exact location information of a node exchanged with its neighbours' in plain text and the absence of node authentication results in some of the attacks such as Sybil attack, Blackhole attack, and Wormhole attack. Moreover, the severe consequence of these attacks is Denial of Service (DoS), poor network performance, reduced network lifetime, etc. This paper proposes an anti-Spoof (a-Spoof) algorithm for mitigating localization and neighbour spoofing attacks in UASN. a-Spoof uses three pre-shared symmetric keys to share the location. Additionally, location integrity provided through the hash function. Further, the performance of a-Spoof demonstrated through its implementation in UnetStack with reference to end-to-end packet delay and the number of hops.
2021-06-30
Lu, Xiao, Jing, Jiangping, Wu, Yi.  2020.  False Data Injection Attack Location Detection Based on Classification Method in Smart Grid. 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM). :133—136.
The state estimation technology is utilized to estimate the grid state based on the data of the meter and grid topology structure. The false data injection attack (FDIA) is an information attack method to disturb the security of the power system based on the meter measurement. Current FDIA detection researches pay attention on detecting its presence. The location information of FDIA is also important for power system security. In this paper, locating the FDIA of the meter is regarded as a multi-label classification problem. Each label represents the state of the corresponding meter. The ensemble model, the multi-label decision tree algorithm, is utilized as the classifier to detect the exact location of the FDIA. This method does not need the information of the power topology and statistical knowledge assumption. The numerical experiments based on the IEEE-14 bus system validates the performance of the proposed method.
2021-05-18
Intharawijitr, Krittin, Harvey, Paul, Imai, Pierre.  2020.  A Feasibility Study of Cache in Smart Edge Router for Web-Access Accelerator. 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). :360–365.
Regardless of the setting, edge computing has drawn much attention from both the academic and industrial communities. For edge computing, content delivery networks are both a concrete and production deployable use case. While viable at the WAN or telco edge scale, it is unclear if this extends to others, such as in home WiFi routers, as has been assumed by some. In this work-in-progress, we present an initial study on the viability of using smart edge WiFi routers as a caching location. We describe the simulator we created to test this, as well as the analysis of the results obtained. We use 1 day of e-commerce web log traffic from a public data set, as well as a sampled subset of our own site - part of an ecosystem of over 111 million users. We show that in the best case scenario, smart edge routers are inappropriate for e-commerce web caching.
2021-05-13
Fei, Wanghao, Moses, Paul, Davis, Chad.  2020.  Identification of Smart Grid Attacks via State Vector Estimator and Support Vector Machine Methods. 2020 Intermountain Engineering, Technology and Computing (IETC). :1—6.

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

2021-05-03
Marechal, Emeline, Donnet, Benoit.  2020.  Network Fingerprinting: Routers under Attack. 2020 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :594–599.
Nowadays, simple tools such as traceroute can be used by attackers to acquire topology knowledge remotely. Worse still, attackers can use a lightweight fingerprinting technique, based on traceroute and ping, to retrieve the routers brand, and use that knowledge to launch targeted attacks. In this paper, we show that the hardware ecosystem of network operators can greatly vary from one to another, with all potential security implications it brings. Indeed, depending on the autonomous system (AS), not all brands play the same role in terms of network connectivity. An attacker could find an interest in targeting a specific hardware vendor in a particular AS, if known defects are present in this hardware, and if the AS relies heavily on it for forwarding its traffic.
2021-04-27
Matthews, I., Mace, J., Soudjani, S., Moorsel, A. van.  2020.  Cyclic Bayesian Attack Graphs: A Systematic Computational Approach. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :129–136.
Attack graphs are commonly used to analyse the security of medium-sized to large networks. Based on a scan of the network and likelihood information of vulnerabilities, attack graphs can be transformed into Bayesian Attack Graphs (BAGs). These BAGs are used to evaluate how security controls affect a network and how changes in topology affect security. A challenge with these automatically generated BAGs is that cycles arise naturally, which make it impossible to use Bayesian network theory to calculate state probabilities. In this paper we provide a systematic approach to analyse and perform computations over cyclic Bayesian attack graphs. We present an interpretation of Bayesian attack graphs based on combinational logic circuits, which facilitates an intuitively attractive systematic treatment of cycles. We prove properties of the associated logic circuit and present an algorithm that computes state probabilities without altering the attack graphs (e.g., remove an arc to remove a cycle). Moreover, our algorithm deals seamlessly with any cycle without the need to identify their type. A set of experiments demonstrates the scalability of the algorithm on computer networks with hundreds of machines, each with multiple vulnerabilities.
2021-04-08
Dinh, N., Tran, M., Park, Y., Kim, Y..  2020.  An Information-centric NFV-based System Implementation for Disaster Management Services. 2020 International Conference on Information Networking (ICOIN). :807–810.
When disasters occur, they not only affect the human life. Therefore, communication in disaster management is very important. During the disaster recovery phase, the network infrastructure may be partially fragmented and mobile rescue operations may involve many teams with different roles which can dynamically change. Therefore, disaster management services require high flexibility both in terms of network infrastructure management and rescue group communication. Existing studies have shown that IP-based or traditional telephony solutions are not well-suited to deal with such flexible group communication and network management due to their connection-oriented communication, no built-in support for mobile devices, and no mechanism for network fragmentation. Recent studies show that information-centric networking offers scalable and flexible communication based on its name-based interest-oriented communication approach. However, considering the difficulty of deploying a new service on the existing network, the programmability and virtualization of the network are required. This paper presents our implementation of an information-centric disaster management system based on network function virtualization (vICSNF). We show a proof-of-concept system with a case study for Seoul disaster management services. The system achieves flexibility both in terms of network infrastructure management and rescue group communication. Obtained testbed results show that vICSNF achieves a low communication overhead compared to the IP-based approach and the auto-configuration of vICSNFs enables the quick deployment for disaster management services in disaster scenarios.
2021-03-17
Soliman, H. M..  2020.  An Optimization Approach to Graph Partitioning for Detecting Persistent Attacks in Enterprise Networks. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1—6.
Advanced Persistent Threats (APTs) refer to sophisticated, prolonged and multi-step attacks, planned and executed by skilled adversaries targeting government and enterprise networks. Attack graphs' topologies can be leveraged to detect, explain and visualize the progress of such attacks. However, due to the abundance of false-positives, such graphs are usually overwhelmingly large and difficult for an analyst to understand. Graph partitioning refers to the problem of reducing the graph of alerts to a set of smaller incidents that are easier for an analyst to process and better represent the actual attack plan. Existing approaches are oblivious to the security-context of the problem at hand and result in graphs which, while smaller, make little sense from a security perspective. In this paper, we propose an optimization approach allowing us to generate security-aware partitions, utilizing aspects such as the kill chain progression, number of assets involved, as well as the size of the graph. Using real-world datasets, the results show that our approach produces graphs that are better at capturing the underlying attack compared to state-of-the-art approaches and are easier for the analyst to understand.
2021-03-16
Freitas, M. Silva, Oliveira, R., Molinos, D., Melo, J., Rosa, P. Frosi, Silva, F. de Oliveira.  2020.  ConForm: In-band Control Plane Formation Protocol to SDN-Based Networks. 2020 International Conference on Information Networking (ICOIN). :574—579.

Although OpenFlow-based SDN networks make it easier to design and test new protocols, when you think of clean slate architectures, their use is quite limited because the parameterization of its flows resides primarily in TCP/IP protocols. Besides, despite the many benefits that SDN offers, some aspects have not yet been adequately addressed, such as management plane activities, network startup, and options for connecting the data plane to the control plane. Based on these issues and limitations, this work presents a bootstrap protocol for SDN-based networks, which allows, beyond the network topology discovery, automatic configuration of an inband control plane. The protocol is designed to act only on layer two, in an autonomous, distributed and deterministic way, with low overhead and has the intent to be the basement for the implementation of other management plane related activities. A formal specification of the protocol is provided. In addition, an analytical model was created to preview the number of required messages to establish the control plane. According to this model, the proposed protocol presents less overhead than similar de-facto protocols used to topology discovery in SDN networks.

2021-03-04
Wang, L..  2020.  Trusted Connect Technology of Bioinformatics Authentication Cloud Platform Based on Point Set Topology Transformation Theory. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :151—154.
The bioinformatics features are collected by pattern recognition technology, and the digital coding and format conversion of the feature data are realized by using the theory of topological group transformation. Authentication and Signature based on Zero Knowledge Proof Technology can be used as the trusted credentials of cloud platform and cannot be forged, thus realizing trusted and secure access.
2021-03-01
Tan, R., Khan, N., Guan, L..  2020.  Locality Guided Neural Networks for Explainable Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.
Zhang, Y., Groves, T., Cook, B., Wright, N. J., Coskun, A. K..  2020.  Quantifying the impact of network congestion on application performance and network metrics. 2020 IEEE International Conference on Cluster Computing (CLUSTER). :162–168.
In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.
2021-02-16
Poudel, S., Sun, H., Nikovski, D., Zhang, J..  2020.  Distributed Average Consensus Algorithm for Damage Assessment of Power Distribution System. 2020 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT). :1—5.
In this paper, we propose a novel method to obtain the damage model (connectivity) of a power distribution system (PDS) based on distributed consensus algorithm. The measurement and sensing units in the distribution network are modeled as an agent with limited communication capability that exchanges the information (switch status) to reach an agreement in a consensus algorithm. Besides, a communication graph is designed for agents to run the consensus algorithm which is efficient and robust during the disaster event. Agents can dynamically communicate with the other agent based on available links that are established and solve the distributed consensus algorithm quickly to come up with the correct topology of PDS. Numerical simulations are performed to demonstrate the effectiveness of the proposed approach with the help of an IEEE 123-node test case with 3 different sub-graphs.
IBRAHIMY, S., LAMAAZI, H., BENAMAR, N..  2020.  RPL Assessment using the Rank Attack in Static and Mobile Environments. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). :1—6.
Routing protocol running over low power and lossy networks (RPL) is currently one of the main routing protocols for the Internet of Things (IoT). This protocol has some vulnerabilities that can be exploited by attackers to change its behavior and deteriorate its performance. In the RPL rank attack, a malicious node announces a wrong rank, which leads the neighboring’s nodes to choose this node as a preferred parent. In this study, we used different metrics to assess RPL protocol in the presence of misbehaving nodes, namely the overhead, convergence time, energy consumption, preferred parent changes, and network lifetime. Our simulations results show that a mobile environment is more damaged by the rank attack than a static environment.
Başkaya, D., Samet, R..  2020.  DDoS Attacks Detection by Using Machine Learning Methods on Online Systems. 2020 5th International Conference on Computer Science and Engineering (UBMK). :52—57.
DDoS attacks impose serious threats to many large or small organizations; therefore DDoS attacks have to be detected as soon as possible. In this study, a methodology to detect DDoS attacks is proposed and implemented on online systems. In the scope of the proposed methodology, Multi Layer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN), C-Support Vector Machine (SVC) machine learning methods are used with scaling and feature reduction preprocessing methods and then effects of preprocesses on detection accuracy rates of HTTP (Hypertext Transfer Protocol) flood, TCP SYN (Transport Control Protocol Synchronize) flood, UDP (User Datagram Protocol) flood and ICMP (Internet Control Message Protocol) flood DDoS attacks are analyzed. Obtained results showed that DDoS attacks can be detected with high accuracy of 99.2%.
Wei, D., Wei, N., Yang, L., Kong, Z..  2020.  SDN-based multi-controller optimization deployment strategy for satellite network. 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS). :467—473.
Due to the network topology high dynamic changes, the number of ground users and the impact of uneven traffic, the load difference between SDN-based satellite network controllers varies widely, which will cause network performance such as network delay and throughput to drop dramatically. Aiming at the above problems, a multi-controller optimized deployment strategy of satellite network based on SDN was proposed. First, the controller's load state is divided into four types: overload state, high load state, normal state, and idle state; second, when a controller in the network is idle, the switch under its jurisdiction is migrated to the adjacent low load controller and turn off the controller to reduce waste of resources. When the controller is in a high-load state and an overload state, consider both the controller and the switch, and migrate the high-load switch to the adjacent low-load controller. Balance the load between controllers, improve network performance, and improve network performance and network security. Simulation results show that the method has an average throughput improvement of 2.7% and a delay reduction of 3.1% compared with MCDALB and SDCLB methods.
Mujib, M., Sari, R. F..  2020.  Performance Evaluation of Data Center Network with Network Micro-segmentation. 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). :27—32.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.

2021-01-28
Collins, B. C., Brown, P. N..  2020.  Exploiting an Adversary’s Intentions in Graphical Coordination Games. 2020 American Control Conference (ACC). :4638—4643.

How does information regarding an adversary's intentions affect optimal system design? This paper addresses this question in the context of graphical coordination games where an adversary can indirectly influence the behavior of agents by modifying their payoffs. We study a situation in which a system operator must select a graph topology in anticipation of the action of an unknown adversary. The designer can limit her worst-case losses by playing a security strategy, effectively planning for an adversary which intends maximum harm. However, fine-grained information regarding the adversary's intention may help the system operator to fine-tune the defenses and obtain better system performance. In a simple model of adversarial behavior, this paper asks how much a system operator can gain by fine-tuning a defense for known adversarial intent. We find that if the adversary is weak, a security strategy is approximately optimal for any adversary type; however, for moderately-strong adversaries, security strategies are far from optimal.

2021-01-25
Zhang, T.-Y., Ye, D..  2020.  Distributed Secure Control Against Denial-of-Service Attacks in Cyber-Physical Systems Based on K-Connected Communication Topology. IEEE Transactions on Cybernetics. 50:3094–3103.
In this article, the security problem in cyber-physical systems (CPSs) against denial-of-service (DoS) attacks is studied from the perspectives of the designs of communication topology and distributed controller. To resist the DoS attacks, a new construction algorithm of the k-connected communication topology is developed based on the proposed necessary and sufficient criteria of the k-connected graph. Furthermore, combined with the k-connected topology, a distributed event-triggered controller is designed to guarantee the consensus of CPSs under mode-switching DoS (MSDoS) attacks. Different from the existing distributed control schemes, a new technology, that is, the extended Laplacian matrix method, is combined to design the distributed controller independent on the knowledge and the dwell time of DoS attack modes. Finally, the simulation example illustrates the superiority and effectiveness of the proposed construction algorithm and a distributed control scheme.
2020-12-21
Nasution, A. P., Suryani, V., Wardana, A. A..  2020.  IoT Object Security towards On-off Attack Using Trustworthiness Management. 2020 8th International Conference on Information and Communication Technology (ICoICT). :1–6.
Internet of Things (IoT) can create the world with the integration of the physical things with the seamlessly network of information purposely to give a sophisticated and smart service for human life. A variety of threats and attacks to IoT object, however, can lead to the misuse of data or information to the IoT objects. One of the attacks is On-off Attack in which the attacker acts not only as an object with a good manner by sending the valid trust value but also sometimes as a bad object by sending invalid one. To respond this action, there is a need for the object security to such attacks. Here the writer used the Trustworthiness Management as a method to cope with this attack. Trustworthiness Management can use the aspect of trust value security as a reference for detecting an attack to the object. In addition, with the support of security system using the authentication provided by MQTT, it is expected that it can provide an additional security. The approach used in this research was the test on On-Off Attack detection directly to the object connected to the network. The results of the test were then displayed on the webpage made using PHP and MySQL database as the storage of the values sent by the object to the server. The test on the On-off Attack detection was successfully conducted with the success level of 100% and the execution to detection took 0.5518318 seconds. This then showed that Trustworthiness Management can be used as one of the methods to cope with On-off Attack.
2020-12-14
Pilet, A. B., Frey, D., Taïani, F..  2020.  Foiling Sybils with HAPS in Permissionless Systems: An Address-based Peer Sampling Service. 2020 IEEE Symposium on Computers and Communications (ISCC). :1–6.
Blockchains and distributed ledgers have brought renewed interest in Byzantine fault-tolerant protocols and decentralized systems, two domains studied for several decades. Recent promising works have in particular proposed to use epidemic protocols to overcome the limitations of popular Blockchain mechanisms, such as proof-of-stake or proof-of-work. These works unfortunately assume a perfect peer-sampling service, immune to malicious attacks, a property that is difficult and costly to achieve. We revisit this fundamental problem in this paper, and propose a novel Byzantine-tolerant peer-sampling service that is resilient to Sybil attacks in open systems by exploiting the underlying structure of wide-area networks.
Hadiansyah, R., Suryani, V., Wardana, A. A..  2020.  IoT Object Security towards the Sybil Attack Using the Trustworthiness Management. 2020 8th International Conference on Information and Communication Technology (ICoICT). :1–4.

Internet of Things (IoT), commonly referred to a physical object connected to network, refers to a paradigm in information technology integrating the advances in terms of sensing, computation and communication to improve the service in daily life. This physical object consists of sensors and actuators that are capable of changing the data to offer the improvement of service quality in daily life. When a data exchange occurs, the exchanged data become sensitive; making them vulnerable to any security attacks, one of which, for example, is Sybil attack. This paper aimed to propose a method of trustworthiness management based upon the authentication and trust value. Once performing the test on three scenarios, the system was found to be capable of detecting the Sybil attack rapidly and accurately. The average of time to detect the Sybil attacks was 9.3287 seconds and the average of time required to detect the intruder object in the system was 18.1029 seconds. The accuracy resulted in each scenario was found 100% indicating that the detection by the system to Sybil attack was 100% accurate.

2020-12-11
Kumar, S., Vasthimal, D. K..  2019.  Raw Cardinality Information Discovery for Big Datasets. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :200—205.
Real-time discovery of all different types of unique attributes within unstructured data is a challenging problem to solve when dealing with multiple petabytes of unstructured data volume everyday. Popular discovery solutions such as the creation of offline jobs to uniquely identify attributes or running aggregation queries on raw data sets limits real time discovery use-cases and often results into poor resource utilization. The discovery information must be treated as a parallel problem to just storing raw data sets efficiently onto back-end big data systems. Solving the discovery problem by creating a parallel discovery data store infrastructure has multiple benefits as it allows such to channel the actual search queries against the raw data set in much more funneled manner instead of being widespread across the entire data sets. Such focused search queries and data separation are far more performant and requires less compute and memory footprint.
2020-12-02
Abeysekara, P., Dong, H., Qin, A. K..  2019.  Machine Learning-Driven Trust Prediction for MEC-Based IoT Services. 2019 IEEE International Conference on Web Services (ICWS). :188—192.

We propose a distributed machine-learning architecture to predict trustworthiness of sensor services in Mobile Edge Computing (MEC) based Internet of Things (IoT) services, which aligns well with the goals of MEC and requirements of modern IoT systems. The proposed machine-learning architecture models training a distributed trust prediction model over a topology of MEC-environments as a Network Lasso problem, which allows simultaneous clustering and optimization on large-scale networked-graphs. We then attempt to solve it using Alternate Direction Method of Multipliers (ADMM) in a way that makes it suitable for MEC-based IoT systems. We present analytical and simulation results to show the validity and efficiency of the proposed solution.

Gliksberg, J., Capra, A., Louvet, A., García, P. J., Sohier, D..  2019.  High-Quality Fault-Resiliency in Fat-Tree Networks (Extended Abstract). 2019 IEEE Symposium on High-Performance Interconnects (HOTI). :9—12.
Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of HPC systems. In this paper we present Dmodc, a fast deterministic routing algorithm for Parallel Generalized Fat-Trees (PGFTs) which minimizes congestion risk even under massive topology degradation caused by equipment failure. It applies a modulo-based computation of forwarding tables among switches closer to the destination, using only knowledge of subtrees for pre-modulo division. Dmodc allows complete re-routing of topologies with tens of thousands of nodes in less than a second, which greatly helps centralized fabric management react to faults with high-quality routing tables and no impact to running applications in current and future very large-scale HPC clusters. We compare Dmodc against routing algorithms available in the InfiniBand control software (OpenSM) first for routing execution time to show feasibility at scale, and then for congestion risk under degradation to demonstrate robustness. The latter comparison is done using static analysis of routing tables under random permutation (RP), shift permutation (SP) and all-to-all (A2A) traffic patterns. Results for Dmodc show A2A and RP congestion risks similar under heavy degradation as the most stable algorithms compared, and near-optimal SP congestion risk up to 1% of random degradation.