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A First Look at Android Malware Traffic in First Few Minutes 14

A First Look at Android Malware Traffic in First Few Minutes

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Joint 14th IEEE Int Conf on Trust, Secur and Privacy in Comp and Commun / 13th IEEE Int symposium on Parallel and Distributed Proc with Applications / 9th IEEE Int Conf on Big data Science and Engineering (IEEE TrustCom-ISPA-BigdataSE)

作者： Chen, Zhenxiang Han, Hongbo Yan, Qiben Yang, Bo Peng, Lizhi Zhangi, Lei Li, Jin Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Shape Secur Mountain View CA 94040 USA Guangzhou Univ Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781467379526

With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. the growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes;(2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively;(3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.

关键词： malware traffic behavior traffic analysis

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the Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering

The Network Lens: Interactive Exploration of Multivariate Ne...

引用

International Conference on Information Visualisation (IV)

作者： Ilir Jusufi Yang Dingjie Andreas Kerren School of Computer Science Physics and Mathematics (DFM) Linnaeus University Vaxjo Sweden

Networks are widely used in modeling relational data often comprised of thousands of nodes and edges. this kind of data alone implies a challenge for its visualization as it is hard to avoid clutter of network elements if using traditional node-link diagrams. Moreover, real-life network data sets usually represent objects with a large number of additional attributes that need to be visualized, such as in software engineering, social network analysis, or biochemistry. In this paper, we present a novel approach, called Network Lens, to visualize such attributes in context of the underlying network. Our implementation of the Network Lens is an interactive tool that extends the idea of so-called magic lenses in such a way that users can interactively build and combine various lenses by specifying different attributes and selecting suitable visual representations.

关键词： Lenses data visualization visualization Image color analysis Layout Context Prototypes

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A 3D visualization Technique for large Scale Time-Varying data

A 3D Visualization Technique for Large Scale Time-Varying Da...

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International Conference on Information Visualisation (IV)

作者： Maiko Imoto Takayuki Itoh Ochanomizu University Japan

We represent time-varying data as polyline charts very often. At the same time, we often need to observe hundreds or even thousands of time-varying values in one chart. However, it is often difficult to understand such large-scale time-varying if all the values are drawn in a single polyline chart. this paper presents a polyline-based 3D time-varying data visualization technique. the technique places a set of polylines in the 3D space, where the X-axis denotes time, the Y-axis denotes values, and the polylines are arranged along the Z-axis. It provides two views: the first viewpoint has a view direction along Y-axis, and the second viewpoint has a view direction along Z-axis. the technique displays the overview of the data from the first viewpoint, and the detail of the specific parts of the data from the second viewpoint. It also detects frequent or outlier patterns by applying SAX (Symbolic Aggregate approXimation), and indicates them so that users can discover such characteristic patterns. this paper shows several interesting visualization examples to demonstrate the effectiveness of the presented technique.

关键词： data visualization data mining three dimensional displays Image color analysis Ocean temperature Temperature sensors Temperature distribution

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MemoryBank: Enhancing large Language Models with Long -Term Memory 38

MemoryBank: Enhancing Large Language Models with Long -Term ...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Zhong, Wanjun Guo, Lianghong Gao, Qiqi Ye, He Wang, Yanlin Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China KTH Royal Inst Technol Stockholm Sweden Sun Yat Sen Univ Sch Software Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)1577358872

large Language Models (LLMs) have drastically reshaped our interactions with artificial intelligence (AI) systems, showcasing impressive performance across an extensive array of tasks. Despite this, a notable hindrance remains the deficiency of a long-term memory mechanism within these models. this shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems, psychological counseling, and secretarial assistance. Recognizing the necessity for long-term memory, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user's personality over time by synthesizing information from previous interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory. this mechanism permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a more human -like memory mechanism and enriched user experience. Memory Bank is versatile in accommodating both closed-source models like ChatGPT and open -source models such as ChatGLM. To validate MemoryBank's effectiveness, we exemplify its application through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialog data, SiliconFriend displays heightened empathy and discernment in its interactions. Experiment involves both qualitative analysis with real world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as multiple users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. the results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capabilit

关键词： Computational linguistics

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Rethinking the Development of large Language Models from the Causal Perspective: A Legal Text Prediction Case Study 38

Rethinking the Development of Large Language Models from the...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Chen, Haotian Zhang, Lingwei Liu, Yiran Yu, Yang Fudan Univ Sch Comp Sci Shanghai Peoples R China Johns Hopkins Univ Dept Comp Sci Baltimore MD USA Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing Peoples R China

ISBN: (纸本)1577358872

While large language models (LLMs) exhibit impressive performance on a wide range of NLP tasks, most of them fail to learn the causality from correlation, which disables them from learning rationales for predicting. Rethinking the whole developing process of LLMs is of great urgency as they are adopted in various critical tasks that need rationales, including legal text prediction (e.g., legal judgment prediction). In this paper, we first explain the underlying theoretical mechanism of their failure and argue that both the data imbalance and the omission of causality in model design and selection render the current training-testing paradigm failed to select the unique causality-based model from correlation-based models. Second, we take the legal text prediction task as the testbed and reconstruct the developing process of LLMs by simultaneously infusing causality into model architectures and organizing causality-based adversarial attacks for evaluation. Specifically, we base our reconstruction on our theoretical analysis and propose a causality-aware self-attention mechanism (CASAM), which prevents LLMs from entangling causal and non-causal information by restricting the interaction between causal and non-causal words. Meanwhile, we propose eight kinds of legal-specific attacks to form causality-based model selection. Our extensive experimental results demonstrate that our proposed CASAM achieves state-of-the-art (SOTA) performances and the strongest robustness on three commonly used legal text prediction benchmarks. We make our code publicly available at https://***/Carrot-Red/Rethink-LLM-development.

关键词： Forecasting

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Onshore Use of Hull Monitoring data Retrieved from Ultra large Container Ships 14th

Onshore Use of Hull Monitoring Data Retrieved from Ultra Lar...

引用

14th International symposium on Practical Design of Ships and Other Floating Structures, PRADS 2019

作者： Storhaug, Gaute Hareide, Ole Jacob DNV GL Maritime Advisory Oslo Norway

ISBN: (纸本)9789811546235

Digitalization is associated with the fourth industrial revolution. It covers many aspects, e.g. sensors. Sensor data can be displayed on board for decision support. Class approved hull monitoring systems do exactly that. this is not new, and it is not about digitalization. there is however a need to use sensor data in decision support onshore. this can be related to an alternative survey arrangement, which can give benefits compared to a rigid inspection regime. the aim is to reduce risk of damages, repairs and inspection costs. To do this, data needs to be collected, quality assured and assessed, and results needs to be displayed to decision makers. this process involves digitalization, and this is new for hull structures of merchant ships. Four container ships are instrumented. they are sister vessels and operates on the same Asia to Europe trade. the hull monitoring supplier has retrieved data from the vessels and converted the data to a supplier independent format stored on a data platform. A quality dashboard and a result dashboard are developed. these dashboards are available through a secure web access for the owner. the experience with the hull response for such large ships is limited. Results for fatigue and extreme loading including the effect of vibration are shown and related to seamanship, inspection and future expectation. Damping is estimated for main vibration modes, which can be useful for numerical analysis. Overall, the structural performance on this trade is good building confidence to the design. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Condition monitoring

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data analysis of University Educational Administration Information Based on Prefixspan Algorithm 14th

Data Analysis of University Educational Administration Infor...

引用

14th International symposium on Intelligence Computation and Applications, ISICA 2023

作者： Xu, Yiying Liu, Yi Yu, Haili Academic Affairs Office of Jiangsu University Zhenjiang China School of Computer Science and Communication Engineering JiangSu University Zhenjiang China Mengxi Honors College of Jiangsu University Zhenjiang China

ISBN: (纸本)9789819743957

With the rapid development of data mining technology, a large amount of educational administration information data has been produced. How to make full use of these information resources and dig out valuable information is a hot topic in the research of colleges and universities. Compared with positive sequential pattern mining, negative sequential pattern mining considers not only the events that have already occurred, but also the events that have not occurred, and it can assist decision making when simple positive sequential pattern mining may mislead decision making. And the existing sequential pattern mining algorithm has the same importance in each project when applied, which is impractical. In this paper, a data mining algorithm based on Prefixspan is proposed. During the mining process, different weights are set for the items, and the weighted support degree of each sequence is compared with the minimum support degree to obtain frequent sequence patterns. the algorithm is applied to the modeling and simulation of student data by using K-means clustering method. the experimental results show that this method can effectively improve the efficiency and accuracy of data mining, and has strong practicability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data mining

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Automated Defect Report Generation for Enhanced Industrial Quality Control 38

Automated Defect Report Generation for Enhanced Industrial Q...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Xie, Jiayuan Zhou, Zhiping Wu, Zihan Zhang, Xinting Wang, Jiexin Cai, Yi Li, Qing Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China MOE China Key Lab Big Data & Intelligent Robot SCUT Beijing Peoples R China Univ Hong Kong Dept Math Hong Kong Peoples R China

ISBN: (纸本)1577358872

Defect detection is a pivotal aspect ensuring product quality and production efficiency in industrial manufacturing. Existing studies on defect detection predominantly focus on locating defects through bounding boxes and classifying defect types. However, their methods can only provide limited information and fail to meet the requirements for further processing after detecting defects. To this end, we propose a novel task called defect detection report generation, which aims to provide more comprehensive and informative insights into detected defects in the form of text reports. For this task, we propose some new datasets, which contain 16 different materials and each defect contains a detailed report of human constructs. In addition, we propose a knowledge-aware report generation model as a baseline for future research, which aims to incorporate additional knowledge to generate detailed analysis and subsequent processing related to defects in images. By constructing defect report datasets and proposing corresponding baselines, we chart new directions for future research and practical applications of this task.

关键词： Defects

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Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers 38

Near-Optimal Resilient Aggregation Rules for Distributed Lea...

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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th symposium on Educational Advances in Artificial Intelligence

作者： Yi, Yuhao You, Ronghui Liu, Hong Liu, Changxin Wang, Yuan Lv, Jiancheng Sichuan Univ Coll Comp Sci Chengdu Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China KTH Royal Inst Technol Sch Elect Engn & Comp Sci Stockholm Sweden Hunan Univ Sch Robot Changsha Hunan Peoples R China

ISBN: (纸本)1577358872

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. the key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. this paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. the proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers.

关键词： Learning systems

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EyeFIX: An Interactive Visual Analytics Interface for Eye Movement analysis 21

EyeFIX: An Interactive Visual Analytics Interface for Eye Mo...

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14th International symposium on Visual Information Communication and Interaction, VINCI 2021

作者： Kumar, Ayush Goel, Bharat Premkumar, Keshav Rajupet Burch, Michael Mueller, Klaus Schepens Eye Research Institute Harvard Medical School United States Stony Brook University United States University of Applied Sciences of the Grisons Chur Switzerland

ISBN: (纸本)9781450386470

Eye movements are closely related to the cognitive processes and often act as a window to the brain and mind. To facilitate a window to the brain using eye movements, we propose EyeFIX, an interactive visual analytics interface. Our interface currently focuses on processing gaze movements to generate the two most prominent events, fixations, and saccades which are used in a large part of the eye movement literature. Our interface has multiple interactive widgets that allow the users to choose the algorithm and tune its hyper parameters that control how fixations and saccades are generated. It has four major visualizations: (1) A Gaze Plot to showcase the gaze movement of each participant, (2) a Fixation Plot that helps to identify regions of the stimulus which are of interest to a particular participant, (3) a Heat Map to study the denser regions of fixation and regions which the participant frequently visited, and (4) a Timeline visualization to assist in having a closer look at temporal regions of interest. We design a set of brushing and linking operators that allow users to interactively control all the visualizations to dig deeper in both, the spatial and temporal domains. We demonstrate the applicability of our interface with datasets obtained by human subjects viewing naturalistic stimuli while performing various viewing tasks, including visual exploration, visual search, and prolonged visual fixation. © 2021 ACM.

关键词： Eye movements

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