The proliferation of Distributed Denial-of-Service (DDoS) attacks in the Internet of Things (IoT) and the emergence of variants such as Distributed Reflection Denial-of-Service (DRDoS) attacks severely threaten the fifth-generation (5G) networks. More importantly, as attack methods continue to escalate, attackers can obtain deployed critical defense strategies through continuous probing, increasing the defense difficulty. We propose a hybrid strategy model and corresponding Software Defined Network (SDN) paradigm-based framework based on existing defense strategies to deploy it flexibly and conveniently into the 3GPP-defined 5G architecture. In addition, after quantifying the proposed hybrid strategy's defense capacity, the Stackelberg game is employed to model the hybrid strategy and solve the optimal packet sampling rate. The simulation shows that the optimal packet sampling rate is effective and robust and can force a rational attacker to give up DRDoS attacks and achieve the effect of subduing the enemy without fighting.

Network slicing is an emerging architecture that allows multiple virtual networks to be created on top of a shared physical infrastructure, each tailored to a specific type of service or application. Managing network slices using a Software Defined Network (SDN) controller makes the network more scalable and manageable but also vulnerable due to the centralized control of SDN. Low-rate denial-of-service (LDoS) is periodic and stealthy, and its attack frequency is lower than that of ordinary distributed denial-of-service attacks, making it one of the most severe threats on SDN. To cope with the above challenges, we propose a real-time Lightweight LDoS Detection and Mitigation (L2DM) framework. Moreover, an Adaptive Weight XGBoost (AW-XGBoost) algorithm is designed to extract features and obtain the detection model via adjusting adaptive weights. We also leverage the architecture of network slicing to build honeypot slices to gather information from attackers. Experimental results show that the proposed framework and corresponding algorithm can be deployed on SDN controllers to achieve LDoS attack detection and mitigation at low cost with high accuracy and effectiveness.

"Remind me to get milk later this afternoon." In communications and planning, people often express uncertainty about time using imprecise temporal expressions (ITEs). Unfortunately, modern virtual assistants often lack system support to capture the intents behind these expressions. This can result in unnatural interactions and undesirable interruptions (e.g., having a work reminder delivered at 12pm when out at lunch, because the user said "this afternoon"). In this paper we explore existing practices, expectations, and preferences surrounding the use of ITEs. Our mixed methods approach employs surveys, interviews, and an analysis of a large corpus of written communications. We find that people frequently use a diverse set of ITEs in both communication and planning. These uses reflect a variety of motivations, such as conveying uncertainty or task priority. In addition, we find that people have a variety of expectations about time input and management when interacting with virtual assistants. We conclude with design implications for future virtual assistants.

Software APIs often contain too many methods and parameters for developers to memorize or navigate effectively. Instead, developers resort to finding answers through online search engines and systems such as Stack Overflow. However, the process of finding and integrating a working solution is often very time-consuming. Though code search engines have increased in quality, there remain significant language- and workflow-gaps in meeting end-user needs. Novice and intermediate programmers often lack the language to query, and the expertise in transferring found code to their task. To address this problem, we present CodeMend, a system to support finding and integration of code. CodeMend leverages a neural embedding model to jointly model natural language and code as mined from large Web and code datasets. We also demonstrate a novel, mixed-initiative, interface to support query and integration steps. Through CodeMend, end-users describe their goal in natural language. The system makes salient the relevant API functions, the lines in the end-user's program that should be changed, as well as proposing the actual change. We demonstrate the utility and accuracy of CodeMend through lab and simulation studies.

Programming can be hard to learn and master. Novice programmers often find themselves struggling with terminology, concepts, or different solutions to the same problem with little clue on how to choose the best one. Professional programmers often spend a considerable amount of time learning to use third-party libraries, APIs, or an unfamiliar piece of code. Although programmers can turn to search engines or question-and-answer websites for help, the problem solving process can often take multiple iterations and can be time-consuming. An integrated system that can recognize a programmer's difficulties and provide contextualized solutions is thus desirable, as it may significantly reduce the amount of manual effort required in the loop of troubleshooting.

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel word-similarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-of-the-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.

The spreading of innovations among individuals and organizations in a social network has been extensively studied. Although the recent studies among the social computing and data mining communities have produced various insightful conclusions about the diffusion process of innovations by focusing on the properties and evolution of social network structures, less attention has been paid to the interrelationships among the multiple innovations being diffused, such as the competitive and collaborative relationships between innovations. In this paper, we take a formal quantitative approach to address how different pieces of innovations socialize with each other and how the interrelationships among innovations affect users' adoption behavior, which provides a novel perspective of understanding the diffusion of innovations. Networks of innovations are constructed by mining large scale text collections in an unsupervised fashion. We are particularly interested in the following questions: what are the meaningful metrics on the network of innovations? What effects do these metrics exert on the diffusion of innovations? Do these effects vary among users with different adoption preferences or communication styles? While existing studies primarily address social influence, we provide a detailed discussion of how innovations interrelate and influence the diffusion process.