The widespread adoption of encrypted network protocols has made traffic encryption ubiquitous, creating substantial challenges for network management and security. This paper introduces a novel encrypted traffic classification system, ANASETC, which combines traffic burst features with Neural Architecture Search (NAS) to automatically design efficient neural network architectures. ANASETC autonomously generates high-performance classification models, significantly reducing manual intervention while maintaining high classification accuracy. To enhance search efficiency, we introduce a new search space called ETNasnet, which optimizes the training process through parameter sharing among sub-models. We evaluate ANASETC’s performance on three public datasets and a real-world satellite network traffic dataset. The results show that ANASETC achieves an optimal balance between classification accuracy and search efficiency, demonstrating strong robustness and adaptability across various task scenarios, outperforming state-of-the-art methods.

Auction plays a crucial role in many modern trading environments, including online advertising and public resource allocation. As the number of competing bidders increases, learning Bayesian Nash Equilibrium (BNE) in auctions faces significant scalability challenges. Existing methods often experience slow convergence in large-scale auctions. For example, in a classic symmetric auction setting, the convergence rate depends on the number of bidders quadratically. To address this issue, we propose the Approximate Best Response Gradient method, a new approach for learning BNE efficiently in auction games. We leverage an analytic solution for gradient estimation to enable efficient gradient computation during optimization. Moreover, we introduce the Best Response Distance objective, which serves as an upper bound of approximation quality to BNE. By optimizing the new objective, our method is proven to achieve a local convergence rate independent of bidder numbers and circumvent the traditional quadratic complexity in the classic symmetric setting. Extensive experiments across various auction formats demonstrate that our approach accelerates convergence and enhances learning efficiency in complex auction settings.

Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While existing metrics primarily assess data quality based on either explicit or implicit reward margins, their single-margin focus often leads to contradictory evaluations for the same data. To address this issue, we propose a new metric of alignment potential, $M_{AP}$, which integrates both margins to quantify the gap from the model’s current implicit reward margin to the target explicit reward margin, thereby estimating the model’s potential to align on the preference data. Empirical results demonstrate that training on the data selected by $M_{AP}$ consistently enhances alignment performance, surpassing existing metrics across different base models and optimization objectives. Furthermore, our method can be extended to self-play data generation frameworks, where we use this metric to identify high-quality data within the self-generated content by LLMs. Under this data generation scenario, our method surpasses current state-of-the-art methods across various training settings and demonstrates continuous improvements with increasing dataset size and training iterations.

Malware poses a significant threat to the security of cyberspace. For malware detection, utilizing machine learning or deep learning techniques to analyze API sequences has been proven to be effective. However, the existing methods fail in mitigating the interference caused by redundant information when processing excessively lengthy or behavior-masking sequences. To address this issue, we propose MalSE, a novel malware detection framework based on API call sensitivity estimation. MalSE aims to highlight key information in the sequence and minimize the interference of redundant information, thereby increasing detection performance. Firstly, MalSE uses a novel statistical-based method to annotate parameter sensitivity labels, which provide a foundation for subsequent module training. Secondly, MalSE employs a Bert-based estimator to transform the parameters into the semantic space and then predict the parameters’ sensitivities. Thirdly, MalSE assesses the sensitivity of each API call by aggregating the sensitivities of parameters, thus providing powerful features for detection tasks. Finally, MalSE employs an attention-based sequence model, which can concentrate on crucial information within the sequence to enhance the detection performance. We evaluate MalSE on 2 binary classification tasks and 1 multi-classification task. MalSE outperforms other methods across all tasks, demonstrating superior and robust detection capabilities under different scenarios.

Smart contracts control critical financial assets on blockchains, with potential weaknesses risking substantial losses. Thus, smart contract vulnerability detection is essential for maintaining blockchain ecosystem stability. Traditional methods depend extensively on expert-driven patterns, resulting in poor scalability. Although deep learning-based approaches have made significant progress, they still suffer from issues such as inflexible representations, insufficient feature modalities, and limited model capabilities. In this paper, we propose FSGDec, a novel smart contract vulnerability detection framework that fuses sequential information and structural features at the bytecode level. Firstly, an efficient node embedding method is developed for contract control flow graphs, flexibly processing node sequences and incorporating node-specific semantic information associated with weaknesses. Then, by modeling node features as time series signals, an adaptive graph wave network is introduced to automatically capture vulnerability-related structural features. Finally, a classifier is deployed to perform bug detection utilizing the extracted graph-level features that integrate semantic information. Evaluated on two real-world smart contract datasets, the experimental results demonstrate that FSGDec achieves superior performance compared to state-of-the-art baselines.

Infrared small target detection (IRSTD) remains challenging due to the weak spatial features of targets and their susceptibility to background clutter. Recent studies have improved detection performance through the embedding of additional spatial representations. However, these feature prompting methods rely on discretely sampled feature spaces, which weaken high-frequency information and consequently limit their representational efficiency. To overcome this, we propose NeRI, a network that leverages the potential of implicit neural representations (INRs) through a continuous formulation to learn mappings from spatial coordinates to the high-frequency structural representations of targets. Specifically, these mappings are realized through INR Blocks (INRBs) integrated into different encoder layers, providing continuous spatial guidance from multi-scale inputs and enabling more accurate localization and distinction. In addition, to better model the distinction between foreground and background, we construct a hybrid U-shaped block (HUB) that combines a U-shaped Transformer block (UTB) and multi-scale convolution block (MCB). The UTB component effectively increases network depth and facilitates long-range dependency modeling across different scales, while the MCB employs convolutions with varying receptive fields to capture fine-grained local information, thereby enabling the two components to fully exploit their complementary strengths. Finally, we propose a simple yet effective spatial–semantic fusion (SSF) module that reweights and integrates spatial information from diverse layers to enhance the expressive power of the features. The proposed NeRI offers a robust solution for the accurate separation of targets from backgrounds. Experimental validation, conducted on three public datasets (i.e., NUDT-SIRST, NUAA-SIRST, and IRSTD-1K), demonstrates the superior performance of NeRI compared to other methods. Open-source implementations will be available at https://github.com/Shangwei-Deng/NeRI.

Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetries, poses significant challenges in efficiently solving for equilibria. Traditional learning approaches often face limitations due to their specificity to certain settings and high resource demands. Addressing this, we introduce *Auctionformer*, an efficient transformer-based method to solve equilibria of diverse auctions in a unified framework. Leveraging the flexible tokenization schemes, Auctionformer translates varying auction games into a standard token series, making use of renowned Transformer architectures. Moreover, we employ Nash error as the loss term, sidestepping the need for underlying equilibrium solutions and enabling efficient training and inference. Furthermore, a few-shot framework supports adaptability to new mechanisms, reinforced by a self-supervised fine-tuning approach. Extensive experimental results affirm the superior performance of Auctionformer over contemporary methods, heralding its potential for broad real-world applications.

Detecting malware in Internet of Things (IoT) networks is crucial for ensuring IoT security. Machine learning based Network Intrusion Detection System (NIDS) has been proven to be effective, but it faces the challenge of achieving high computational efficiency. Previous feature selection methods improve the efficiency of NIDS by removing redundant features. However, these methods fail to consider the significant disparity in computational cost among different traffic features, so they are not fully applicable for resource-constrained environment. To address this issue, we propose a novel framework RecoSelector to select traffic features in a cost-sensitive manner for NIDS. RecoSelector aims to effectively select feature subsets with strong detection capability and low computational cost. Firstly, we quantify and analyze the feature construction cost among IoT short flows, IoT long flows and cross platform flows from 6 scenarios. Based on the flows, we generate computational-loss of 69 flow features through flow transmission frequency. Secondly, we propose Particle Initialization based on Orthogonal Sparse Vector (PIOSV) to optimize search direction and increase the possibility of finding a global optimal solution. Finally, we design an elaborate fitness function RecoFitness, aiming to carry out multi-objective optimization, for iterative selection. We obtain flow feature cost in a real resource-constrained environment. Experiments demonstrate that RecoSelector exhibits superior performance with spending only 3% to 60% of the computational time cost while achieving comparable F1 scores compared to existing methods.

Smart contracts have been increasingly deployed and applied on various blockchain platforms. Nevertheless, vulnerabilities may cause significant financial losses due to the involvement of substantial funds in smart contracts. Traditional analysis tools heavily rely on manually predefined rules. Recent studies have demonstrated the promising potential of deep learning techniques in smart contract vulnerability detection. However, existing approaches often disregard cross-function and cross-contract vulnerability scenarios, focusing primarily on characterization or detection tasks at the function level. In this study, we propose CL-HGAN, a novel framework for smart contract vulnerability detection at the contract level. Firstly, we construct a contract-level heterogeneous graph to embody the relationships between contracts and functions. Specifically, we build the backbone of the heterogeneous graph based on the abstract syntax tree (AST) and multiple types of edges and then incorporate two additional categories of edges to augment its structural information. Subsequently, we design a two-phase feature learning method to automatically generate graph-level representations based on a heterogeneous graph attention network and meta-paths specific to the constructed graph. Finally, we employ a classifier to perform vulnerability detection tasks. In particular, the proposed CL-HGAN comprehensively captures vulnerability features and accurately identifies vulnerabilities at the contract level. Furthermore, we evaluate the CL-HGAN framework on an Ethereum smart contract dataset containing thirty types of vulnerabilities. The experimental results show that the average metrics of our approach outperform the state-of-the-art baselines.

Mid-air gesture-based systems are becoming ubiquitous. Many mid-air gestures control different kinds of interactive devices, applications, and systems. They are, however, still targeted at specific devices in specific domains and are not necessarily consistent across domain boundaries. A comprehensive evaluation of the transferability of gesture vocabulary between domains is also lacking. Consequently, interaction designers cannot decide which gestures to use for which domain. In this systematic literature review, we contribute to the future research agenda in this area, based on an analysis of 172 papers. As part of our analysis, we clustered gestures according to the dimensions of an existing taxonomy to identify their common characteristics in different domains, and we investigated the extent to which existing mid-air gesture sets are consistent across different domains. We derived a consensus gesture set containing 22 gestures based on agreement rates calculation and considered their transferability across different domains.

Domain Name System (DNS) is one of the most common and vital services on the Internet. Attackers take advantage of DNS tunneling to bypass firewalls for data exfiltration, which has been a significant risk in cyber security. Although there are studies on DNS tunneling detection, few are concerned with low-throughput data exfiltration over DNS tunnels. This paper proposes a low-throughput data exfiltration detection method via time-frequency domain analysis on DNS traffic. We conduct the time and frequency domain analysis on time windows of DNS traffic in different granularities. The frequency domain features effectively encode sequential information of the packet sequences, which sketch malicious patterns in low-throughput DNS tunnels. We evaluate our method on the DNS traffic of an enterprise network. Experimental results exhibit that our method significantly outperforms the existing approach in detecting low-throughput data exfiltration over DNS tunnels.

Recently, privacy issues in web services that rely on users’ personal data have raised great attention. Despite that recent regulations force companies to offer choices for each user to opt-in or opt-out of data disclosure, real-world applications usually only provide an “all or nothing” binary option for users to either disclose all their data or preserve all data with the cost of no personalized service. In this article, we argue that such a binary mechanism is not optimal for both consumers and platforms. To study how different privacy mechanisms affect users’ decisions on information disclosure and how users’ decisions affect the platform’s revenue, we propose a privacy-aware recommendation framework that gives users fine control over their data. In this new framework, users can proactively control which data to disclose based on the tradeoff between anticipated privacy risks and potential utilities. Then we study the impact of different data disclosure mechanisms via simulation with reinforcement learning due to the high cost of real-world experiments. The results show that the platform mechanisms with finer split granularity and more unrestrained disclosure strategy can bring better results for both consumers and platforms than the “all or nothing” mechanism adopted by most real-world applications.

User identity linkage refers to linking different social accounts belonging to the same natural person. Cross social network user identity linkage based on spatiotemporal data has attracted more and more attention. However, the existing methods have some problems such as track processing is not suitable for sparse data and grid processing leads to information loss and abnormality. In view of the above problems, we propose a spatial density-based method VKP, which can accurately and efficiently solve the user identity linkage problem based on spatiotemporal data. According to the sparsity, heterogeneity and imbalance of spatiotemporal data in social networks, the user identity is expressed as several virtual key points, and then the user identity is linked by calculating the similarity between the user identity representations. We compare this method with several state-of-the-art user identity linkage methods based on spatiotemporal data on real datasets, and the results show that this method exceeds the baseline methods in terms of effectiveness and efficiency.

Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against different orientations of the same vehicle. However, these two characteristics are hard to be encapsulated into a single feature representation simultaneously with unified supervision. Here we propose a Disentangled Feature Learning Network (DFLNet) to learn orientation specific and common features concurrently, which are discriminative at details and invariant to orientations, respectively. Moreover, to effectively use these two types of features for ReID, we further design a feature metric alignment scheme to ensure the consistency of the metric scales. The experiments show the effectiveness of our method that achieves state-of-the-art performance on three challenging datasets.

The digital retina excels at providing enhanced visual sensing and analysis capability for city brain in smart cities, and can feasibly convert the visual data from visual sensors into semantic features. With the deployment of deep learning or handcrafted models, these features are extracted on front-end devices, then delivered to back-end servers for advanced analysis. In this scenario, we propose a model generation, utilization and communication paradigm, aiming at strong front-end sensing capabilities for establishing better artificial visual systems in smart cities. In particular, we propose an integrated multiple deep learning models reuse and prediction strategy, which dramatically increases the feasibility of the digital retina in large-scale visual data analysis in smart cities. The proposed multi-model reuse scheme aims to reuse the knowledge from models cached and transmitted in digital retina to obtain more discriminative capability. To efficiently deliver these newly generated models, a model prediction scheme is further proposed by encoding and reconstructing model differences. Extensive experiments have been conducted to demonstrate the effectiveness of proposed model-centric paradigm.

The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner. By deploying deep learning and/or handcrafted models in front-end devices, the compact features can be extracted and subsequently delivered to back-end cloud for search and advanced analytics. In this context, we propose a model generation, utilization, and communication paradigm, aiming to address a set of unique challenges for better artificial intelligence services in smart cities. In particular, we present an integrated multiple deep learning models reuse and prediction strategy, which greatly increases the feasibility of the digital retina in processing and analyzing the large-scale visual data in smart cities. The promise of the proposed paradigm is demonstrated through a set of experiments.

The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.

The visual data, which are acquired from the ubiquitous visual sensors deployed in metropolitans, are of great value and paramount significance to enhance the effectiveness and pursue the future development of smart cities. In this paper, the essential building blocks of the unified visual data management and analysis infrastructure that serve as the foundation for the economical visual system in the city brain, are introduced to facilitate the utilization of the visual signal in the artificial intelligence era. In particular, we start by the discussion of the front-end smart visual sensing in the context of economical communication and service with the heterogeneous network, and the functionalities and necessities of compact visual feature and deep learning model representations are detailed. Subsequently, the utilities of the infrastructure are demonstrated through two intelligent applications at the back-end, including vehicle re-identification and person re-identification. The standardizations regarding compact feature and deep neural network representations, which are regarded as the key ingredients in this infrastructure and greatly facilitate the construction of the visual system in the city brain, are also discussed. Finally, we envision how the potential issues regarding the economical visual communications for future smart cities might be pragmatically approached within this unified infrastructure.

Recently Convolutional Neural Networks (CNNs) have achieved great success in different fields including image instance retrieval. However traditional global pooling approaches fail to capture all possible discriminative information of CNN activations and treat activations over channels equally regardless of the different importance between channels. In this work, we focus on the mentioned problem of global feature pooling over CNN activations for image instance retrieval. We make two contributions. First, we introduce a channel-wise SQUare-root (SQU) pooling (2-norm) approach, which makes better use of information over activation maps and is superior to Average (1-norm) and Max pooling (infinity norm), in the context of instance retrieval. Second, we further improve SQU by learning a gating function that weights the contributions of different channels, in an end-to-end manner. Extensive experiments on 6 benchmark datasets show that the proposed strategies achieve considerable improvements over state-of-the-art.

With the advances of artificial intelligence, recent years have witnessed a gradual transition from the big data to the big knowledge. Based on the knowledge-powered deep learning models, the big data such as the vast text, images and videos can be efficiently analyzed. As such, in addition to data, the communication of knowledge implied in the deep learning models is also strongly desired. As a specific example regarding the concept of knowledge creation and communication in the context of Knowledge Centric Networking (KCN), we investigate the deep learning model compression and demonstrate its promise use through a set of experiments. In particular, towards future KCN, we introduce efficient transmission of deep learning models in terms of both single model compression and multiple model prediction. The necessity, importance and open problems regarding the standardization of deep learning models, which enables the interoperability with the standardized compact model representation bitstream syntax, are also discussed.