Suyeon Lee

Screening smartphone applications using malware family signatures

The sharp increase in smartphone malware has become one of the most serious security problems. Since the Android platform has taken the dominant position in smartphone popularity, the number of Android malware has grown correspondingly and represents critical threat to the smartphone users. This rise in malware is primarily attributable to the occurrence of variants of existing malware. A set of variants stem from one malware can be considered as one malware family, and malware families cover more than half of the Android malware population. A conventional technique for defeating malware is the use of signature matching which is efficient from a time perspective but not very practical because of its lack of robustness against the malware variants. As a counter approach for handling the issue of variants behavior analysis techniques have been proposed but require extensive time and resources. In this paper, we propose an Android malware detection mechanism that uses automated family signature extraction and family signature matching. Key concept of the mechanism is to extract a set of family representative binary patterns from evaluated family members as a signature and to classify each set of variants into a malware family via an estimation of similarity to the signatures. The proposed family signature and detection mechanism offers more flexible variant detection than does the legacy signature matching, which is strictly dependent on the presence of a specific string. Furthermore, compared with the previous behavior analysis techniques considering family detection, the proposed family signature has higher detection accuracy without the need for the significant overhead of data and control flow analysis. Using the proposed signature, we can detect new variants of known malware efficiently and accurately by static matching. We evaluated our mechanism with 5846 real world Android malware samples belonging to 48 families collected in April 2014 at an anti-virus company; experimental results showed that; our mechanism achieved greater than 97% accuracy in detection of variants. We also demonstrated that the mechanism has a linear time complexity with the number of target applications.

Screening Smartphone Applications Using Behavioral Signatures

The sharp increase of smartphone malwares has become one of the most serious security problems. The most significant part of the growth is the variants of existing malwares. A legacy approach for malware, the signature matching, is efficient in temporal dimension, but it is not practical because of its lack of robustness against the variants. A counter approach, the behavior analysis to handle the variant issue, takes too much time and resources. We propose a variant detection mechanism using runtime semantic signature. Our key idea is to reduce the control and data flow analysis overhead by using binary patterns for the control and data flow of critical actions as a signature. The flow information is a significant part of behavior analysis but takes high analysis overhead. In contrast to the previous behavioral signatures, the runtime semantic signature has higher family classification accuracy without the flow analysis overhead, because the binary patterns of flow parts is hardly shared by the out of family members. Using the proposed signature, we detect the new variants of known malwares by static matching efficiently and accurately. We evaluated our mechanism with 1,759 randomly collected real-world Android applications including 79 variants of 4 malware families. As the experimental result, our mechanism showed 99.89% of accuracy on variant detection. We also showed that the mechanism has a linear time complexity as the number of target applications. It is fully practical and advanced performance than the previous works in both of accuracy and efficiency.

A hierarchical Bayesian regression model for the uncertain functional constraint using screened scale mixtures of Gaussian distributions

This paper considers a hierarchical Bayesian analysis of regression models using a class of Gaussian scale mixtures. This class provides a robust alternative to the common use of the Gaussian distribution as a prior distribution in particular for estimating the regression function subject to uncertainty about the constraint. For this purpose, we use a family of rectangular screened multivariate scale mixtures of Gaussian distribution as a prior for the regression function, which is flexible enough to reflect the degrees of uncertainty about the functional constraint. Specifically, we propose a hierarchical Bayesian regression model for the constrained regression function with uncertainty on the basis of three stages of a prior hierarchy with Gaussian scale mixtures, referred to as a hierarchical screened scale mixture of Gaussian regression models (HSMGRM). We describe distributional properties of HSMGRM and an efficient Markov chain Monte Carlo algorithm for posterior inference, and apply the proposed model to real applications with constrained regression models subject to uncertainty.

Dynamic coupling of plasmonic resonators.

We clarify the nature of dynamic coupling in plasmonic resonators and determine the dynamic coupling coefficient using a simple analytic model. We show that plasmonic resonators, such as subwavelength holes in a metal film which can be treated as bound charge oscillators, couple to each other through the retarded interaction of oscillating screened charges. Our dynamic coupling model offers, for the first time, a quantitative analytic description of the fundamental symmetric and anti-symmetric modes of coupled resonators which agrees with experimental results. Our model also reveals that plasmonic electromagnetically induced transparency arises in any coupled resonators of slightly unequal lengths, as confirmed by a rigorous numerical calculation and experiments.

Characteristics of the planar second-order high-Tc SQUID gradiometer

We have studied the planar-type single-layer second-order high-Tc SQUID gradiometer that detects the transverse field gradient, d2Bz/dx2. The gradiometer consists of three parallel-connected rectangular pickup loops that are directly coupled to a SQUID. The pickup loops are designed in such a way that the SQUID measures the difference in the differential screening currents of neighbouring pickup loops. The SQUID has either step-edge or bicrystal Josephson junctions. The gradiometer was made from a single layer of YBa2Cu3O7 film on a 1 cm × 1 cm chip. The response of the gradiometer was tested in various flux distributions generated by three identical multiturn film coils patterned on the same chip in parallel with each pickup loop. The balanced gradiometer responded sensitively only to the second-order field gradient. Reduction of the inductive coupling between loops improved the intrinsic balance.

Feasibility study for reduction of the screening current induced field in a 2G high temperature superconducting coil

This paper reports the effects of thermal energy on reducing the overshoot of the current sweep cycle method to reduce the screening current-induced field (SCF) in a 2G high temperature superconducting (HTS) coil. A disadvantage of the current sweep cycle method is the necessity for large overshoot in the coil current. For a 2G HTS coil, excessive overshooting of the coil current is undesirable (Yanagisawa et al 2012 AIP Conf. Proc. 1434 1373–8). In an effort to circumvent this overshooting problem, the thermal energy effect was investigated in combination with the current sweep cycle method based on experiments in this study. The experimental results show that greater SCF reduction in the HTS coil was obtained upon increasing thermal energy by heater current.

Interaction between slots as oscillator model

We present oscillator model for coupled plasmonic resonators. Narrow slot on metal film can be modeled as a bound charge oscillator. The oscillator model offers quantitative description about fundamental modes and electromagnetic induced transparency spectrum.

Fano resonant chiral electromagnetic fields by metasurfaces

We demonstrate for the first time that chiral electromagnetic fields display Fano resonance using nano-hole metasurfaces. We show the nano-hole structure can be utilized as biosensors to detect chiral molecules.

Broadband low reflectance stepped-cone nanostructures by nanosphere lithography

The authors demonstrated broadband low reflectance through a two-step surface texturing technique that combines nanosphere lithography with dry-etching. Through this, various stepped-cone nanostructures were fabricated on the surface of GaAs to suppress its reflectance, with the shape and height of these nanostructures being precisely controlled by altering the diameter of the etch mask (SiO2 nanospheres) and the etching time. The effects of this stepped-cone nanostructure were analyzed by measuring its reflectance spectra in conjunction with finite-difference time-domain calculations. This found that the average reflectance at wavelengths of 300–2500 nm is reduced from 38.1% to 2.6% due to enhanced light scattering and a gradual change in refractive index. This novel method is therefore considered to represent an easily scalable approach to fabricating broadband antireflective surfaces for solar cell applications.