Search-based Stress Testing of Wireless Network Protocol Stacks

Abstract—The operation of wireless network protocol stacks is heavily dependent on the actual deployment of the system and especially on the corresponding network topology, e. g., due to channel contention. The nature of wireless communication does not allow for a-priori determination of network topology; network-defining metrics such as neighbor density and routing span may drastically differ for various deployments. Therefore, it is a difficult problem to foresee and consider the large number of possible topologies that a system may run on during protocol stack development. We propose to use an automated approach for searching topologies for which a protocol stack exhibits particularly poor quantitative performance. We formulate stress testing of protocol stacks on specific topologies as a multi-objective optimization problem and use an evolutionary algorithm for finding a set of small topologies that particularly stress the protocol stack of a wireless network. For searching the topology space, we present novel problem-specific variation operators and show their improvements on search performance in case studies. We showcase our results on stress testing using two protocol stacks for wireless sensor networks

OpenEPC Integration within 5GTN as an NFV proof of concept

Abstract. Gone are the days, when a hardware is changed on every malfunctioning and the whole operation either stays down or load on the replacing hardware becomes too much which ultimately compromises the QoS. The IT industry is mature enough to tackle problems regarding scalability, space utilization, energy consumption, cost, agility and low availability. The expected throughput and network latency with 5G in the cellular Telecommunication Networks seems to be unachievable with the existing architecture and resources. Network Function Virtualization promises to merge IT and Telecommunications in such an efficient way that the expected results could be achieved no longer but sooner. The thesis work examines the compatibility and flexibility of a 3GPP virtual core network in a virtualization platform. The testbed is established on an LTE (Long Term Evolution) based network being already deployed and OpenEPC is added as virtual core network on it. The integration of OpenEPC in 5GTN (5TH Generation Test Network) is discussed in details in the thesis which will give an account of the possibility of implementing such a simulated vEPC (Virtual Evolved Packet Core) in a real network platform. The deployed setup is tested to check its feasibility and flexibility for a platform which could be used for NFV deployment in future. The monitoring of OpenEPC’s individual components while utilizing the major resources within them, forms the primary performance test. The CPU Load and Memory Utilization is tested on different CPU stress levels having a constant data traffic from actual UEs. At the completion of the thesis work, a consensus is built up based on the test results that the test setup can hold number of subscribers to a certain amount without any performance degradation. Moreover, the virtual core network throughput and network latency is also compared to the commercial LTE networks and theoretical maximum values on similar resources to check performance consistency OpenEPC must offer

Description and Experience of the Clinical Testbeds

This deliverable describes the up-to-date technical environment at three clinical testbed demonstrator sites of the 6WINIT Project, including the adapted clinical applications, project components and network transition technologies in use at these sites after 18 months of the Project. It also provides an interim description of early experiences with deployment and usage of these applications, components and technologies, and their clinical service impact

Systematically Detecting Packet Validation Vulnerabilities in Embedded Network Stacks

Embedded Network Stacks (ENS) enable low-resource devices to communicate with the outside world, facilitating the development of the Internet of Things and Cyber-Physical Systems. Some defects in ENS are thus high-severity cybersecurity vulnerabilities: they are remotely triggerable and can impact the physical world. While prior research has shed light on the characteristics of defects in many classes of software systems, no study has described the properties of ENS defects nor identified a systematic technique to expose them. The most common automated approach to detecting ENS defects is feedback-driven randomized dynamic analysis ("fuzzing"), a costly and unpredictable technique. This paper provides the first systematic characterization of cybersecurity vulnerabilities in ENS. We analyzed 61 vulnerabilities across 6 open-source ENS. Most of these ENS defects are concentrated in the transport and network layers of the network stack, require reaching different states in the network protocol, and can be triggered by only 1-2 modifications to a single packet. We therefore propose a novel systematic testing framework that focuses on the transport and network layers, uses seeds that cover a network protocol's states, and systematically modifies packet fields. We evaluated this framework on 4 ENS and replicated 12 of the 14 reported IP/TCP/UDP vulnerabilities. On recent versions of these ENSs, it discovered 7 novel defects (6 assigned CVES) during a bounded systematic test that covered all protocol states and made up to 3 modifications per packet. We found defects in 3 of the 4 ENS we tested that had not been found by prior fuzzing research. Our results suggest that fuzzing should be deferred until after systematic testing is employed.Comment: 12 pages, 3 figures, to be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Congestion Prediction in Internet of Things Network using Temporal Convolutional Network A Centralized Approach

The unprecedented ballooning of network traffic flow, specifically, Internet of Things (IoT) network traffic, has big stressed of congestion on todays Internet. Non-recurring network traffic flow may be caused by temporary disruptions, such as packet drop, poor quality of services, delay, etc. Hence, the network traffic flow estimation is important in IoT networks to predict congestion. As the data in IoT networks is collected from a large number of diversified devices which have unlike format of data and also manifest complex correlations, so the generated data is heterogeneous and nonlinear in nature. Conventional machine learning approaches unable to deal with nonlinear datasets and suffer from misclassification of real network traffic due to overfitting. Therefore, it also becomes really hard for conventional machine learning tools like shallow neural networks to predict the congestion accurately. Accuracy of congestion prediction algorithms play an important role to control the congestion by regulating the send rate of the source. Various deeplearning methods (LSTM, CNN, GRU, etc.) are considered in designing network traffic flow predictors, which have shown promising results. In this work, we propose a novel congestion predictor for IoT, that uses Temporal Convolutional Network (TCN). Furthermore, we use Taguchi method to optimize the TCN model that reduces the number of runs of the experiments. We compare TCN with other four deep learning-based models concerning Mean Absolute Error (MAE) and Mean Relative Error (MRE). The experimental results show that TCN based deep learning framework achieves improved performance with 95.52% accuracy in predicting network congestion. Further, we design the Home IoT network testbed to capture the real network traffic flows as no standard dataset is available

Design Experiences on Single and Multi Radio Systems in Wireless Embedded Platforms

The progress of radio technology has made several flavors of radio available on the market.Wireless sensor network platform designers have used these radios to build a variety of platforms. Withnew applications and different types of radios on wireless sensing nodes, it is often hard to interconnectdifferent types of networks. Hence, often additional radios have to be integrated onto existingplatforms or new platforms have to be built. Additionally, the energy consumption of these nodes have to be optimized to meetlifetime requirements of years without recharging.In this thesis, we address two issues of single and multi radio platform designfor wireless sensor network applications - engineering issues and energy optimization.We present a set of guiding principles from our design experiences while building 3 real life applications,namely asset tracking, burglar tracking and finally in-situ psychophysiological stress monitoring of human subjects in behavioral studies.In the asset tracking application, we present our design of a tag node that can be hidden inside valuable personal assets such asprinters or sofas in a home. If these items are stolen, a city wide anchor node infrastructure networkwould track them throughout the city. We also present our design for the anchor node.In the burglar tracking application, we present the design of tag nodes and the issueswe faced while integrating it with a GSM radio. Finally, we discuss our experiencesin designing a bridge node, that connects body worn physiological sensorsto a Bluetooth enabled mobile smartphone. We present the software framework that acts as middleware toconnect to the bridge, parse the sensor data, and send it to higher layers of the softwareframework.We describe 2 energy optimization schemes that are used in the Asset Tracking and the Burglar Tracking applications, that enhance the lifetime of the individual applications manifold.In the asset tracking application,we design a grouping scheme that helps increase reliability of detection of the tag nodes at theanchor nodes while reducing the energy consumption of the group of tag nodes travelling together.We achieve an increase of 5 times improvement in lifetime of the entire group. In the Burglar Tracking application, weuse sensing to determine when to turn the GSM radio on and transmit data by differentiatingturns and lane changes. This helps us reduce the number of times the GSM radio is woken up, thereby increasing thelifetime of the tag node while it is being tracked. This adds 8 minutes of trackablelifetime to the burglar tracking tag node. We conclude this thesis by observing the futuretrends of platform design and radio evolution

ENSURING SPECIFICATION COMPLIANCE, ROBUSTNESS, AND SECURITY OF WIRELESS NETWORK PROTOCOLS

Several newly emerged wireless technologies (e.g., Internet-of-Things, Bluetooth, NFC)—extensively backed by the tech industry—are being widely adopted and have resulted in a proliferation of diverse smart appliances and gadgets (e.g., smart thermostat, wearables, smartphones), which has ensuingly shaped our modern digital life. These technologies include several communication protocols that usually have stringent requirements stated in their specifications. Failing to comply with such requirements can result in incorrect behaviors, interoperability issues, or even security vulnerabilities. Moreover, lack of robustness of the protocol implementation to malicious attacks—exploiting subtle vulnerabilities in the implementation—mounted by the compromised nodes in an adversarial environment can limit the practical utility of the implementation by impairing the performance of the protocol and can even have detrimental effects on the availability of the network. Even having a compliant and robust implementation alone may not suffice in many cases because these technologies often expose new attack surfaces as well as new propagation vectors, which can be exploited by unprecedented malware and can quickly lead to an epidemic

CC-Fuzz: Genetic algorithm-based fuzzing for stress testing congestion control algorithms

Congestion control research has experienced a significant increase in interest in the past few years, with many purpose-built algorithms being designed with the needs of specific applications in mind. These algorithms undergo limited testing before being deployed on the Internet, where they interact with other congestion control algorithms and run across a variety of network conditions. This often results in unforeseen performance issues in the wild due to algorithmic inadequacies or implementation bugs, and these issues are often hard to identify since packet traces are not available. In this paper, we present CC-Fuzz, an automated congestion control testing framework that uses a genetic search algorithm in order to stress test congestion control algorithms by generating adversarial network traces and traffic patterns. Initial results using this approach are promising - CC-Fuzz automatically found a bug in BBR that causes it to stall permanently, and is able to automatically discover the well-known low-rate TCP attack, among other things.Comment: This version was submitted to Hotnets 202