The necessity for strong security measures to fend off cyberattacks has increased due to the growing use of industrial Internet of Things (IIoT) technologies. This research introduces IoTForge Pro, a comprehensive security testbed designed to generate a diverse and extensive intrusion dataset for IIoT environments. The testbed simulates various IIoT scenarios, incorporating network topologies and communication protocols to create realistic attack vectors and normal traffic patterns. The generated dataset, named ForgeIIOT, includes various attack types, such as denial-of-service, man-in-the-middle, ransomware, wildcard abuse, and malware-based intrusions, providing a valuable resource for developing and evaluating intrusion detection systems (IDSs). Additionally, we apply advanced machine learning techniques to analyze the ForgeIIOT dataset, demonstrating the effectiveness of different models in identifying and classifying various types of cyberattacks. Our experimental results highlight the potential of machine learning algorithms in enhancing the security of IIoT systems by accurately detecting anomalies and malicious activities. This research contributes to the field by offering a rich dataset and a robust framework for testing and improving IDS for IIoT, ultimately aiming to strengthen the cybersecurity posture of industrial networks.

A wide variety of in-network services have been developed for RMT-based switching hardware, almost exclusively through the P4 language and ecosystem. Many of these applications maintain state in switch memory, a scarce shared resource. As with any other network resource, varying traffic demands necessitate reallocations, yet the P4 ecosystem is not well suited for dynamic resource management: Modifying the set of services deployed on a switch using P4 requires the network operator to prepare a new binary image and re-provision the switch, disrupting all existing traffic. We present an alternate approach---using techniques from capsule-based active networking---to programming RMT devices that enables non-disruptive (re)allocation of switch memory at time scales that are much faster than P4 compilation without operator intervention. We use P4 to implement a single, shared runtime on commodity RMT hardware that interprets instructions received via the switch data plane to deliver a variety of exemplar services including caching, load balancing, and network telemetry. Our prototype implementation is able to dynamically provision dozens-to-hundreds of instances of simultaneous stateful services at the timescale of seconds.

The ecosystem of application functionality built around programmable switch hardware is growing at a rapid pace in recent times, fueled by the advent of reconfigurable match-action table (RMT) technology. This new class of devices is capable of simultaneously delivering basic computation and line-rate forwarding at a reasonable cost. Given this transformation in commodity switching functionality, we suggest it may be time to reconsider the concept of active networking, where end hosts can off-load application functionality to the network in real time without requiring the assistance of the network operator. We present a preliminary approach to encoding (nearly) arbitrary computation into a series of network packets that can be decoded and executed on programmable switch hardware. Our programs can leverage both the forwarding and storage capabilities of RMT devices. We also conduct an initial exploration into the importance of dynamically allocating switch resources across active programs to improve aggregate performance.

Datacenters are characterized by large scale, stringent reliability requirements, and significant application diversity. However, the realities of employing hardware with non-zero failure rates mean that datacenters are subject to significant numbers of failures that can impact performance. Moreover, failures are not always obvious; network components can fail partially, dropping or delaying only subsets of packets. Thus, traditional fault detection techniques involving end-host or router-based statistics can fall short in their ability to identify these errors. We describe how to expedite the process of detecting and localizing partial datacenter faults using an end-host method generalizable to most datacenter applications. In particular, we correlate end-host transport-layer flow metrics with per-flow network paths and apply statistical analysis techniques to identify outliers and localize faulty links and/or switches. We evaluate our approach in a production Facebook front-end datacenter, focusing on its effectiveness across a range of traffic patterns.

Bandwidth adaptation for real-time streaming applications is typically designed to be conservative, since pushing for higher bandwidth could be counterproductive if it means an increased latency. However, such bandwidth adaptation operates based on the "symptoms" of congestion (e.g., increased delay) without knowing the underlying cause (self-congestion vs. cross-traffic). In this paper, we consider this problem in the context of Wi-Fi networks and introduce a novel technique, Ping-Pair, to measure and attribute congestion. We have integrated Ping-Pair into the popular Skype audio-video conferencing application to enable improved bandwidth adaptation dubbed Kwikr, using which we have conducted controlled experiments and also randomized A/B tests in a production setting.

We present GradeIT, a system that combines the dual objectives of automated grading and program repairing for introductory programming courses (CS1). Syntax errors pose a significant challenge for testcase-based grading as it is difficult to differentiate between a submission that is almost correct and has some minor syntax errors and another submission that is completely off-the-mark. GradeIT also uses program repair to help in grading submissions that do not compile. This enables running testcases on submissions containing minor syntax errors, thereby awarding partial marks for these submissions (which, without repair, do not compile successfully and, hence, do not pass any testcase). Our experiments on 15613 submissions show that GradeIT results are comparable to manual grading by teaching assistants (TAs), and do not suffer from unintentional variability that happens when multiple TAs grade the same assignment. The repairs performed by GradeIT enabled successful compilation of 56% of the submissions having compilation errors, and resulted in an improvement in marks for 11% of these submissions.

Interactive real-time streaming applications such as audio-video conferencing, online gaming and app streaming, place stringent requirements on the network in terms of delay, jitter, and packet loss. Many of these applications inherently involve client-to-client communication, which is particularly challenging since the performance requirements need to be met while traversing the public wide-area network (WAN). This is different from the typical situation of cloud-to-client communication, where the WAN can often be bypassed by moving a communication end-point to a cloud “edge”, close to the client. Can we nevertheless take advantage of cloud resources to improve the performance of real-time client-to-client streaming over the WAN?