找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪(fǎng)問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Ubiquitous Security; Second International Guojun Wang,Kim-Kwang Raymond Choo,Ernesto Damiani Conference proceedings 2023 The Editor(s) (if

[復(fù)制鏈接]
31#
發(fā)表于 2025-3-27 00:44:16 | 只看該作者
32#
發(fā)表于 2025-3-27 04:49:18 | 只看該作者
33#
發(fā)表于 2025-3-27 05:16:44 | 只看該作者
Vulnerability Detection with Representation Learningtion. However, existing methods usually ignore the feature representation of vulnerable datasets, resulting in unsatisfactory model performance. Such vulnerability detection techniques should achieve high accuracy, relatively high true-positive rate, and low false-negative rate. At the same time, it
34#
發(fā)表于 2025-3-27 11:02:57 | 只看該作者
35#
發(fā)表于 2025-3-27 15:24:03 | 只看該作者
Malware Traffic Classification Based on GAN and BP Neural Networkstworks for malware traffic classification, which is to identify malware traffic, normal traffic, and traffic types. The model is composed of generative adversarial network and back propagation neural networks. The generator of the generative adversarial network is responsible for inputting random no
36#
發(fā)表于 2025-3-27 21:05:17 | 只看該作者
37#
發(fā)表于 2025-3-28 01:29:03 | 只看該作者
Detecting Unknown Vulnerabilities in?Smart Contracts with?Binary Classification Model Using Machine contracts are inevitably written with some vulnerabilities, which makes them vulnerable to attacks that cause property damage, and existing detection techniques and static analysis methods mainly target known vulnerability detection. We design a machine learning-based unknown vulnerability detectio
38#
發(fā)表于 2025-3-28 03:54:50 | 只看該作者
39#
發(fā)表于 2025-3-28 09:15:39 | 只看該作者
An Aspect-Based Semi-supervised Generative Model for?Online Review Spam Detection is gradually changed by the network. More and more people consume food, clothing, housing and transportation through the Internet, and the online reviews left by people have become valuable information resources. However, the authenticity of online reviews is worrying. The proliferation of review s
40#
發(fā)表于 2025-3-28 13:28:35 | 只看該作者
Hierarchical Policies of?Subgoals for?Safe Deep Reinforcement Learnings well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its b
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-16 18:56
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
伊川县| 九江县| 滁州市| 宜春市| 临澧县| 广德县| 高密市| 郑州市| 乌恰县| 东乡| 黔东| 兴安县| 安阳县| 安庆市| 青浦区| 海晏县| 柳州市| 库伦旗| 泾川县| 开阳县| 大田县| 吉隆县| 枞阳县| 思茅市| 武安市| 广安市| 怀远县| 漳平市| 聂拉木县| 长沙市| 浦东新区| 红原县| 青田县| 遵义县| 当雄县| 墨江| 桐庐县| 双牌县| 哈巴河县| 秭归县| 田林县|