215 research outputs found
A new lookup model for multiple flow tables of open flow with implementation and optimization considerations
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Open Flow has become the key standard for the southbound interface of software defined networking. The single flow table of Open Flow implementation can lead to fast storage space growth, and finally cause table-overflow, the multiple flow tables can address this problem and provide greater efficiency and flexibility. Through analyzing the potential deployment challenges of Open Flow, this paper proposes a new lookup model with implementation and optimization considerations for multiple flow tables in an Open Flow switch. With the developed lookup model, the original single flow table is split into multiple sub-flow tables, and the fields in each sub-flow table are further divided into several categories according to different field types. Preliminary simulation results demonstrate that the proposed solution can effectively reduce the storage space of flow tables
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
With the development of intelligent transportation systems, vehicles are
exposed to a complex network environment. As the main network of in-vehicle
networks, the controller area network (CAN) has many potential security
hazards, resulting in higher requirements for intrusion detection systems to
ensure safety. Among intrusion detection technologies, methods based on deep
learning work best without prior expert knowledge. However, they all have a
large model size and rely on cloud computing, and are therefore not suitable to
be installed on the in-vehicle network. Therefore, we propose a lightweight
parallel neural network structure, LiPar, to allocate task loads to multiple
electronic control units (ECU). The LiPar model consists of multi-dimensional
branch convolution networks, spatial and temporal feature fusion learning, and
a resource adaptation algorithm. Through experiments, we prove that LiPar has
great detection performance, running efficiency, and lightweight model size,
which can be well adapted to the in-vehicle environment practically and protect
the in-vehicle CAN bus security.Comment: 13 pages, 13 figures, 6 tables, 51 referenc
H-SOFT: a heuristic storage space optimisation algorithm for flow table of OpenFlow
PublishedThis is the peer reviewed version of the article, which has been published in final form at DOI 10.1002/cpe.3206. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.OpenFlow has become the key standard and technology for software defined networking, which has been widely adopted in various environments. However, the global deployment of OpenFlow encountered several issues, such as the increasing number of fields and complex structure of flow entries, making the size of flow table in OpenFlow switches explosively grows, which results in hardware implementation difficulty. To this end, this paper presents the modelling on the minimisation for storage space of flow table and proposes a Heuristic Storage space Optimisation algorithm for Flow Table (H-SOFT) to solve this optimisation problem. The H-SOFT algorithm degrades the complex and high-dimensional fields of a flow table into multiple flow tables with simple and low-dimensional fields based on the coexistence and conflict relationships among fields to release the unused storage space due to blank fields. Extensive simulation experiments demonstrate that the H-SOFT algorithm can effectively reduce the storage space of flow table. In particular, with frequent updates on flow entries, the storage space compression rate of flow table is stable and can achieve at ~70%. Moreover, in comparison with the optimal solution, the H-SOFT algorithm can achieve the similar compression rate with much lower execution time.National Natural Science Foundation of ChinaNational Program on Key Basic Research Project (973 Program)Strategic Priority Research Program of the Chinese Academy of SciencesNational High-Tech R&D Program of China (863 Program
Experimenting adaptive services in sea-cloud innovation environment
Most of existing network testbeds can only support the experimentation of L2~L4 forwarding protocols, leaving the evaluation of L4~L7 applications still a tremendous challenge. This paper pioneers to present the design of sea-cloud innovation environment (SCIE) based on the software defined networking (SDN) and network functions virtualization (NFV) paradigms to support adaptive service-oriented experimentation, where the virtualized network functions (VNFs) can be implemented or deimplemented dynamically on network devices according to ondemand requirements. The experimentation is running to form an adaptive chain of network functions, which can be achieved by the protocol oblivious forwarding (POF) via user-defined fields and generic flow instruction set to forward the data to appropriate devices with VNFs. In SCIE, we demonstrate the experimentation of DPI service with on-demand requirement of security check
Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
As the aging population grows, particularly for the baby boomer generation,
the United States is witnessing a significant increase in the elderly
population experiencing multifunctional disabilities. These disabilities,
stemming from a variety of chronic diseases, injuries, and impairments, present
a complex challenge due to their multidimensional nature, encompassing both
physical and cognitive aspects. Traditional methods often use univariate
regression-based methods to model and predict single degradation conditions and
assume population homogeneity, which is inadequate to address the complexity
and diversity of aging-related degradation. This study introduces a novel
framework for multi-functional degradation modeling that captures the
multidimensional (e.g., physical and cognitive) and heterogeneous nature of
elderly disabilities. Utilizing deep learning, our approach predicts health
degradation scores and uncovers latent heterogeneity from elderly health
histories, offering both efficient estimation and explainable insights into the
diverse effects and causes of aging-related degradation. A real-case study
demonstrates the effectiveness and marks a pivotal contribution to accurately
modeling the intricate dynamics of elderly degradation, and addresses the
healthcare challenges in the aging population
Prompt-aligned Gradient for Prompt Tuning
Thanks to the large pre-trained vision-language models (VLMs) like CLIP, we
can craft a zero-shot classifier by "prompt", e.g., the confidence score of an
image being "[CLASS]" can be obtained by using the VLM provided similarity
measure between the image and the prompt sentence "a photo of a [CLASS]".
Therefore, prompt shows a great potential for fast adaptation of VLMs to
downstream tasks if we fine-tune the prompt-based similarity measure. However,
we find a common failure that improper fine-tuning may not only undermine the
prompt's inherent prediction for the task-related classes, but also for other
classes in the VLM vocabulary. Existing methods still address this problem by
using traditional anti-overfitting techniques such as early stopping and data
augmentation, which lack a principled solution specific to prompt. We present
Prompt-aligned Gradient, dubbed ProGrad, to prevent prompt tuning from
forgetting the the general knowledge learned from VLMs. In particular, ProGrad
only updates the prompt whose gradient is aligned (or non-conflicting) to the
"general direction", which is represented as the gradient of the KL loss of the
pre-defined prompt prediction. Extensive experiments demonstrate the stronger
few-shot generalization ability of ProGrad over state-of-the-art prompt tuning
methods. Codes are available at https://github.com/BeierZhu/Prompt-align.Comment: Accepted by ICCV202
Towards Robust Text Retrieval with Progressive Learning
Retrieval augmentation has become an effective solution to empower large
language models (LLMs) with external and verified knowledge sources from the
database, which overcomes the limitations and hallucinations of LLMs in
handling up-to-date and domain-specific information. However, existing
embedding models for text retrieval usually have three non-negligible
limitations. First, the number and diversity of samples in a batch are too
restricted to supervise the modeling of textual nuances at scale. Second, the
high proportional noise are detrimental to the semantic correctness and
consistency of embeddings. Third, the equal treatment to easy and difficult
samples would cause sub-optimum convergence of embeddings with poorer
generalization. In this paper, we propose the PEG, a progressively learned
embeddings for robust text retrieval. Specifically, we increase the training
in-batch negative samples to 80,000, and for each query, we extracted five hard
negatives. Concurrently, we incorporated a progressive learning mechanism,
enabling the model to dynamically modulate its attention to the samples
throughout the entire training process. Additionally, PEG is trained on more
than 100 million data, encompassing a wide range of domains (e.g., finance,
medicine, and tourism) and covering various tasks (e.g., question-answering,
machine reading comprehension, and similarity matching). Extensive experiments
conducted on C-MTEB and DuReader demonstrate that PEG surpasses
state-of-the-art embeddings in retrieving true positives, highlighting its
significant potential for applications in LLMs. Our model is publicly available
at https://huggingface.co/TownsWu/PEG
Insights into the issue in IPv6 adoption: a view from the Chinese IPv6 Application mix
Published onlineThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Although IPv6 has been standardized more than 15 years ago, its deployment is still very limited. China has been strongly pushing IPv6, especially due to its limited IPv4 address space. In this paper, we describe measurements from a large Chinese academic network, serving a significant population of IPv6 hosts. We show that despite its expected strength, China is struggling as much as the western world to increase the share of IPv6 traffic. To understand the reasons behind this, we examine the IPv6 applicative ecosystem. We observe a significant IPv6 traffic growth over the past 3 years, with P2P file transfers responsible for more than 80% of the IPv6 traffic, compared with only 15% for IPv4 traffic. Checking the top websites for IPv6 explains the dominance of P2P, with popular P2P trackers appearing systematically among the top visited sites, followed by Chinese popular services (e.g., Tencent), as well as surprisingly popular third-party analytics including Google. Finally, we compare the throughput of IPv6 and IPv4 flows. We find that a larger share of IPv4 flows get a high-throughput compared with IPv6 flows, despite IPv6 traffic not being rate limited. We explain this through the limited amount of HTTP traffic in IPv6 and the presence of Web caches in IPv4. Our findings highlight the main issue in IPv6 adoption, that is, the lack of commercial content, which biases the geographic pattern and flow throughput of IPv6 traffic. Copyright © 2014 John Wiley & Sons, Ltd
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