Precision Target Guide Strategy for Applying SERS into Environmental Monitoring

Surface enhanced Raman spectroscopy (SERS) is a promising analytical technique that exhibits various applications in trace detection and identification. When it is applied into environmental monitoring, we should concern several key points to improve detection sensitivity and selectivity for the detection in complex matrix. In this tutorial review, we mainly focus on the strategies for improving the use of SERS into environmental application. The strategies are summarized for enhancing the ability of the substrate to selectively capture specific targets, and for achieving separation and concentration of the analytes from the matrix and the assembly structures for multiple phase detection. We have also introduced several newly developed detection systems using portable instruments and miniaturized devices that are more suitable for infield applications. In addition, we discuss the present challenges that hide it from wide real application and give the outlook for the future development in applying SERS in environmental monitoring

Cdc42 is essential for the polarized movement and adhesion of human dental pulp stem cells

Objective: Stem cell-based tissue repair and regeneration require the regulation of cell migration and adhesion. As a regulator of cell polarization, Cdc42 (cell division control protein 42) plays a basic role at the initial stage of cell migration and adhesion. This study explores the effect of Cdc42 on the polarized migration and adhesion of hDPSCs (human dental pulp stem cells). Design: HDPSCs were isolated from extracted third molars and transfected with siRNA targeted against Cdc42. Scratch wound assays and transwell assays were performed to detect the migration of human dental pulp stem cells. Polarization assays were applied to explore the polarized movement of Golgi bodies and nuclei. Western blot was used to examine the expression of related proteins. Results: The expression of Cdc42 was knocked down by siRNA transfection, which inhibited the migration of hDPSCs in both the scratch wound assays and transwell assays. Meanwhile, the proportion of polarized hDPSCs during migration was also decreased, and the adhesion ability of hDPSCs was downregulated. Western blot demonstrated that these effects were dependent on FAK (focal adhesion kinase), β-catenin and GSK3β (Glycogen synthase kinase-3β). Conclusion Our study demonstrates that Cdc42 plays an essential role during the polarized movement and adhesion of hDPSCs

Protect sensitive information against channel state information based attacks

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock

Find Me A Safe Zone:A Countermeasure for Channel State Information Based Attacks

Recently, channel state information (CSI) is shown to be an effective side-channel to perform attacks in public environments. Prior work has demonstrated that by analyzing how the CSI measurements of the wireless signal are affected by the mobile user's finger movements or gestures, an attacker can recover the user's input with a high success rate. Furthermore, the setup of this new attack is trivial, where the adversary only needs to place one or two malicious wireless devices near the target user. It would be difficult for many users to identify the nearby malicious devices while they want to continue to use mobile applications in public places. This dilemma makes protection of CSI-based attacks an urgent need. This article presents the first countermeasure for CSI-based attacks. Our key insight is that the success of any CSI-based attack requires high-quality CSI measurements; and we can significantly reduce the risk of information leakage by directing the user to a nearby location where the CSI readings are inherently noisy. To this end, we develop a regression based method to assess the risk of CSI-based attacks and exploit a well-established localization technique to identify potential malicious wireless devices. We then use this information to guide the user to a safe zone. We evaluate our approach by applying it to protect pattern lock and keystrokes in various indoor and outdoor environments. Experimental results show that our approach can effectively protect mobile users against CSI-based attacks

Protect sensitive information against channel state information based attacks

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock

Defeat Your Enemy Hiding Behind Public WiFi:WiGuard Can Protect Your Sensitive information from CSI-based Attack

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen input information in a public place. Our approach carefully exploits WiFi channel interference to introduce noise to attacker’s CSI measurements to reduce the success rate of CSI-based attacks. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI-based attacks from 92–42% for text-based passwords and from 82–22% for pattern lock

CrossSense:towards cross-site and large-scale WiFi sensing

We present CrossSense, a novel system for scaling up WiFi sensing to new environments and larger problems. To reduce the cost of sensing model training data collection, CrossSense employs machine learning to train, off-line, a roaming model that generates from one set of measurements synthetic training samples for each target environment. To scale up to a larger problem size, CrossSense adopts a mixture-of-experts approach where multiple specialized sensing models, or experts, are used to capture the mapping from diverse WiFi inputs to the desired outputs. The experts are trained offline and at runtime the appropriate expert for a given input is automatically chosen. We evaluate CrossSense by applying it to two representative WiFi sensing applications, gait identification and gesture recognition, in controlled single-link environments. We show that CrossSense boosts the accuracy of state-of-the-art WiFi sensing techniques from 20% to over 80% and 90% for gait identification and gesture recognition respectively, delivering consistently good performance – particularly when the problem size is significantly greater than that current approaches can effectively handle

AppIS:Protect Android Apps Against Runtime Repackaging Attacks

Apps repackaged through reverse engineering pose a significant security threat to the Android smart phone ecosystem. Previous solutions have mostly focused on the detection and identification of repackaged apps. Nevertheless, current app anti-repackaging services can only protect applications at a coarse level and have significant performance overhead. These approaches can neither meet the performance requirements of Android nor achieve fine-grained protection against cumulative attack at the same time. Specifically, these solutions rely on a fix-structure detecting engine and then will execute the same path at different times, which lead to the whole protection performs poorly when faced with dynamic cumulative attack, which is typical in real-world attack. This paper introduces the AppIS, a reinforced antirepackaging immune system, that is robust to app-repackaging attack scenarios. Unlike past work, which mostly focuses on simple protection only from just one respect, our design exploits an interlocking guarding net with time diversity for the tamperproofing of Android applications. The intuition underlying our design is that a dynamic and static combining method can provide a multi-level protection for the codes, core algorithm and sensitive data. We analyze and classify the existing threats on Android platform and furthermore abstract then model the repackaging attack scenarios. We then adapt a random controller used by the dispatcher to randomly construct guarding net with different structure every time. We have built a prototype of our design using Java Native Interface cross-layer calling mechanism for performance requirement. Results from a deployment of AppIS on three kinds of popular apps demonstrate that the new design can prevent our apps from cumulative attack without extra performance cost