An Evasion and Counter-Evasion Study in Malicious Websites Detection

Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites detection: the attacker essentially has access to the same data that the defender uses to train its detection models. This 'symmetry' can be exploited by the attacker, at least in principle, to evade the defender's detection models. In this paper, we present a framework for characterizing the evasion and counter-evasion interactions between the attacker and the defender, where the attacker attempts to evade the defender's detection models by taking advantage of this symmetry. Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism. The framework is geared toward the popular detection model of decision tree, but can be adapted to accommodate other classifiers

Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Influence of the intense laser field on optical absorption coefficients and refractive index changes in the double trigonometric quantum wells

In this letter, the effect of the intense laser field on optical properties in the unique double trigonometric quantum wells has been investigated by applying the KH transformation. The Schrdinger equation is solved to obtain the energy levels and the wave functions of this system. Then the optical absorption coefficients (OACs) and refractive index changes (RICs) are calculated through the scheme of the compact density-matrix formalism. The results show that the OACs and RICs undergo a blue-shift first, followed by a red-shift, as the intensity of the laser field is enhanced. Furthermore, features of various transition total OACs are also discussed, which are also affected by the laser field

Short-Term Electricity Demand Forecasting for DanceSport Activities

This paper introduces a novel hybrid deep learning-based approach for short-term electricity demand forecasting in dance sport activities. Traditional deep learning methods often overlook important spatial dependencies and key features like trend and seasonal patterns. To address these limitations, we propose a model that combines Transformer for temporal feature extraction and Graph Neural Networks for spatial feature extraction, enabling prediction based on spatial-temporal features. Additionally, we employ the decomposition techniques to extract seasonal and trend features from dance sports data. By integrating early fusion (feature-level fusion) and late fusion (score-level fusion) strategies, our model achieves superior performance, outperforming baseline methods by over 4% on benchmark datasets. Additionally, we conduct the ablation study to comprehensively analyze the impact of each module on prediction accuracy, providing valuable insights into the contribution of spatial, temporal, seasonal and trend features to the overall forecasting performance

A Highly Sensitive Ratiometric Fluorescent Probe for the Detection of Cytoplasmic and Nuclear Hydrogen Peroxide

As a marker for oxidative stress and a second messenger in signal transduction, hydrogen peroxide (H₂O₂) plays an important role in living systems. It is thus critical to monitor the changes in H₂O₂ in cells and tissues. Here, we developed a highly sensitive and versatile ratiometric H₂O₂ fluorescent probe (<b>NP1</b>) based on 1,8-naphthalimide and boric acid ester. In response to H₂O₂, the ratio of its fluorescent intensities at 555 and 403 nm changed 1020-fold within 200 min. The detecting limit of <b>NP1</b> toward H₂O₂ is estimated as 0.17 μM. It was capable of imaging endogenous H₂O₂ generated in live RAW 264.7 macrophages as a cellular inflammation response, and especially, it was able to detect H₂O₂ produced as a signaling molecule in A431 human epidermoid carcinoma cells through stimulation by epidermal growth factor. This probe contains an azide group and thus has the potential to be linked to various molecules via the click reaction. After binding to a Nuclear Localization Signal peptide, the peptide-based combination probe (<b>pep-NP1</b>) was successfully targeted to nuclei and was capable of ratiometrically detecting nuclear H₂O₂ in living cells. These results indicated that <b>NP1</b> was a highly sensitive ratiometric H₂O₂ dye with promising biological applications