入侵检测技术的研究

入侵检测系统是网络安全体系的重要组成部分,入侵检测技术采用主动的防护技术,弥补了传统的安全防御技术的不足。重点介绍了入侵检测常用的技术方法,并将各种检测技术有机地结合,进而降低入侵检测的误报率和漏报率,提高检测的效率。     

一种大储量罐车紧急切断阀失灵检测方法研究

为了提高大储量液化石油气罐车紧急切断阀失灵检测的精度和效率,提出了基于增强模糊支持向量机的罐车紧急切断阀失灵检测方法。首先通过对大储量液化石油气罐车紧急切断阀的常见失灵类型进行分析,将其样本数据进行训练集和测试集的训练。其次利用模糊支持向量机算法进行罐车紧急切断阀失灵检测模型建立。然后利用粒子群进化算法进行该模型的最优参数、惩罚系数和隶属度的确定,最后利用优化的参数进行支持向量机的分类。通过训练集和测试集对该模型进行仿真实验,仿真实验结果表明,该方法的失灵检测准确率均在95%以上,具有较高的鲁棒性和可靠性。     

内容耗散网络的缓存污染攻击命中率阈值控制

内容耗散网络的缓存污染攻击命中率高,难以检测。研究内容耗散网络缓存污染攻击的命中率与域间传输阈值的关系,对降低攻击命中率提供控制模型基础。提出一种基于零周期数相变自适应冗余信息抑制的内容耗散网络缓存污染攻击命中阈值分析模型,提高对缓存污染攻击的检测概率。设计内容耗散网络及缓存污染攻击系统模型,基于url内容块检测模型,得到缓存污染攻击命中判决准则,通过AS-BOOST使用并行的读写操作,进行零周期数相变自适应冗余信息抑制,得到优化阈值分布下的判决准则。实验结果表明,采用该算法能通过合理设定域间阈值,达到消除缓存污染攻击的效果,实现对内容耗散网络的安全控制。     

基于变化矢量分析和振荡指数的植被变化检测方法研究

以MODIS/NDVI作为研究植被变化的遥感数据,针对传统变化矢量分析方法无法较好反映植被变化方向性与波动性的不足,本文建立了修正的变化矢量分析公式,并提出了可描述NDVI年际变化波动性的振荡指数bz。为验证修正的变化矢量分析方法与振荡指数的有效性,选取长江上游青衣江流域为试验区,对不同的植被变化检测方法进行比较分析。研究表明:利用修正变化矢量分析方法得到的NDVI矢量变化强度不仅保留了NDVI变化的强度特征和结构特征,而且可以明确地指示每个像元上植被的变化趋势,从而在数量上和趋势上更好地刻画NDVI时空变化特征;基于振荡指数bz计算的振荡强度由于考虑了年内各时间维上NDVI的变化累积,可以有效地反映NDVI年际变化的波动性。     

建筑材料检测在建筑行业中的作用

随着建筑行业的快速发展,建筑材料的种类不断增多,而且性能各异。建筑材料是建筑的基础,建筑材料质量的好坏就决定着建筑工程质量的好坏,因此对建筑材料进行检测就显得尤为重要。加强对建筑材料的检测才能够保证建筑工程的安全以及人们的生命安全,对于提高建筑工程的质量具有重要的作用。     

基于前景检测的视差优化算法

目前立体匹配算法分两类,传统的匹配算法通过计算两幅图的像素点相似程度,采用的是一种局部优先的方法。而当前的策略主要将问题转化为求解能量方程,进而对全局空间进行优化,提高匹配精度,获得更好的视差图。但是在实际应用过程中,由于光学失真和噪声,平滑表面的镜面反射,投影缩减,透视失真,低纹理和重复纹理等影响,导致误匹配或者找不到匹配点,从而得不到有效视差。然而在特定的场合,可以利用有限的有效视差,基于前景检测以及最小二乘法,优化得到较为完整的前景视差图。     

超高压变电站设备配置与检修

超高压变电设备的运行可靠性是电力系统中非常重要的条件。为了保障超高压变电设备的正常运行,延长设备的使用寿命,避免资源浪费,必须改进并加强对超高压变电设备的日常维护技术。对设备进行状态维修,其目标主要是：确保电气设备运行的安全性、稳定性,在一定程度上防止设备运行时出现损坏。对设备进行状态监测,熟练掌握设备的运行状态是重点,只有这样才能起到理想的维修效果,进一步提高设备的使用效率。     

Users’ ability to perceive misinformation: An information quality assessment approach

Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

Transfer language selection for zero-shot cross-lingual abusive language detection

We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.