Unleashing the power of pseudo-code for binary code similarity analysis

Code similarity analysis has become more popular due to its significant applicantions,including vulnerability detection,malware detection,and patch analysis.Since the source code of the software is difficult to obtain under most circumstances,binary-level code similarity analysis(BCSA)has been paid much attention to.In recent years,many BCSA studies incorporating Al techniques focus on deriving semantic information from binary functions with code representations such as assembly code,intermediate representations,and control flow graphs to measure the similarity.However,due to the impacts of different compilers,architectures,and obfuscations,binaries compiled from the same source code may vary considerably,which becomes the major obstacle for these works to obtain robust features.In this paper,we propose a solution,named UPPC(Unleashing the Power of Pseudo-code),which leverages the pseudo-code of binary function as input,to address the binary code similarity analysis challenge,since pseudocode has higher abstraction and is platform-independent compared to binary instructions.UPPC selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the function.We evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures(X86,ARM),different optimization options(O0-O3),different compilers(GCC,Clang),and four obfuscation strategies.The experimental results show that the accuracy of UPPC in function search is 33.2%higher than that of existing methods.

Fast approximate matching of binary codes with distinctive bits

Although the distance between binary codes can be computed fast in Hamming space, linear search is not practical for large scale datasets. Therefore attention has been paid to the efficiency of performing approximate nearest neighbor search, in which hierarchical clustering trees （HCT） are widely used. However, HCT select cluster centers randomly and build indexes with the entire binary code, this degrades search performance. In this paper, we first propose a new clustering algorithm, which chooses cluster centers on the basis of relative distances and uses a more homogeneous partition of the dataset than HCT has to build the hierarchical clustering trees. Then, we present an algorithm to compress binary codes by extracting distinctive bits according to the standard deviation of each bit. Consequently, a new index is proposed using compressed binary codes based on hierarchical decomposition of binary spaces. Experiments conducted on reference datasets and a dataset of one billion binary codes demonstrate the effectiveness and efficiency of our method.

Study on Binary Code Evolution with Concrete Semantic Analysis

The study on binary code evolution is very crucial for understanding vulnerability repair and malicious code variants.Researchers on code evolution focus on the source code level,whereas very few works have been done to tackle this problem at the binary code level.In this paper,a binary code evolution analysis framework is proposed to automatically locate evolution area and identify evolution semantic with concrete semantic difference.Difference of binary function domain was applied based on function similarity.Trace alignment was used to find evolution blocks,instruction classification semantic was utilized to identify evolution operation,and evolution semantic was extracted combined with function domain elements.The experimental results show that binary code evolution analysis framework can correctly locate binary code evolution area and identify all concrete semantic evolution.

Implementation of a kind of FPGA-based binary phase coded radar signal processor architecture

A flexible field programmable gate array based radar signal processor is presented. The radar signal processor mainly consists of five functional modules： radar system timer, binary phase coded pulse compression（PC）, moving target detection （MTD）, constant false alarm rate （CFAR） and target dots processing. Preliminary target dots information is obtained in PC, MTD, and CFAR modules and Nios I! CPU is used for target dots combination and false sidelobe target removing. Sys- tem on programmable chip （SOPC） technique is adopted in the system in which SDRAM is used to cache data. Finally, a FPGA-based binary phase coded radar signal processor is realized and simula- tion result is given.

Research of Methods for Lost Data Reconstruction in Erasure Codes over Binary Fields

In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.

Optimal binary codes and binary construction of quantum codes

This paper discusses optimal binary codes and pure binary quantum codes created using Steane construction. First, a local search algorithm for a special subclass of quasi-cyclic codes is proposed, then five binary quasi-cyclic codes are built. Second, three classical construction methods are generalized for new codes from old such that they are suitable for constructing binary self-orthogonal codes, and 62 binary codes and six subcode chains of obtained self-orthogonal codes are designed. Third, six pure binary quantum codes are constructed from the code pairs obtained through Steane construction. There are 66 good binary codes that include 12 optimal linear codes, 45 known optimal linear codes, and nine known optimal self-orthogonal codes. The six pure binary quantum codes all achieve the performance of their additive counterparts constructed by quaternary construction and thus are known optimal codes.

Optimization of Mapping Rule of Bit-Interleaved Turbo Coded Modulation with 16QAM

Optimization of mapping rule of bit-interleaved Turbo coded modulation with 16 quadrature amplitude modulation (QAM) is investigated based on different impacts of various encoded bits sequence on Turbo decoding performance. Furthermore, bit-interleaved in-phase and quadrature phase (I-Q) Turbo coded modulation scheme are designed similarly with I-Q trellis coded modulation (TCM). Through performance evaluation and analysis, it can be seen that the novel mapping rule outperforms traditional one and the I-Q Turbo coded modulation can not achieve good performance as expected. Therefore, there is not obvious advantage in using I-Q method in bit-interleaved Turbo coded modulation.

A New Description of the Additive Quantum Codes

A new description of the additive quantum codes is presented and a new way to construct good quantum codes [[n, k, d]] is given by using classical binary codes with specific properties in F2^3n. We show several consequences and examples of good quantum codes by using our new description of the additive quantum codes.

Face Recognition Algorithm Fusing Monogenic Binary Coding and Collaborative Representation

Monogenic binary coding （MBC） have been known to be effective for local feature extraction, while sparse or collaborative representation based classification （CRC） has shown interesting results in robust face recognition. In this paper, a novel face recognition algorithm of fusing MBC and CRC named M-CRC is proposed; in which the dimensionality problem is resolved by projection matrix. The proposed algorithm is evaluated on benchmark face databases, including AR, PolyU-NIR and CAS-PEAL. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods.

Repeater jamming suppression method for pulse Doppler fuze based on identity recognition and chaotic encryption

Pulse Doppler(PD) fuze is widely used in current battlefield. However, with the threat of repeater jamming, especially digital radio frequency memory technology, the deficiency in the anti-repeater jamming of a traditional PD fuze increasingly emerges. Therefore, a repeater jamming suppression method for a PD fuze based on identity(ID) recognition and chaotic encryption is proposed. Every fuze has its own ID which is encrypted with different chaotic binary sequences in every pulse period of the transmitted signal. The thumbtack-shaped ambiguity function shows a good resolution and distance cutoff characteristic. The ability of anti-repeater jamming is emphatically analyzed, and the results at different signal-to-noise ratio(SNR) show a strong anti-repeater jamming ability and range resolution that the proposed method possesses. Furthermore, the anti-repeater jamming ability is influenced by processing gain, bit error rate(BER) and correlation function. The simulation result validates the theoretical analysis, it shows the proposed method can significantly improve the anti-repeater jamming ability of a PD fuze.

Multi-Threat Real-Time Separating System

This paper describes a multi-threat real-time separating system for broadband anti-radiation missile seeker. It presents a method, with a dual-port memory as comparer, to perform PF and PW hardware real-time separation and to determine the time-of-arrival (TOA) by use of sequential difference histogram (SDIF). The method has been applied to practice, which has achieved good results.

Key Frames Extraction Based on the Improved Genetic Algorithm

In order toovercomethe poor local search ability of genetic algorithm, resulting in the basic genetic algorithm is time-consuming, and low search abilityin the late evolutionary, we use thegray coding instead ofbinary codingatthebeginning of the coding;we use multi-point crossoverto replace the originalsingle-point crossoveroperation.Finally, theexperimentshows that the improved genetic algorithmnot only has a strong search capability, but also thestability has been effectively improved.

Finite Projective Geometries and Classification of the Weight Hierarchies of Codes(Ⅰ)

The weight hierarchy of a binary linear [n, k] code C is the sequence (d 1, d 2, . . . , d k ), where d r is the smallest support of an r-dimensional subcode of C. The codes of dimension 4 are collected in classes and the possible weight hierarchies in each class is determined by finite projective geometries. The possible weight hierarchies in class A, B, C, D are determined in Part (I). The possible weight hierarchies in class E, F, G, H, I are determined in Part (II).

High throughput VLSI architecture for H.264/AVC context-based adaptive binary arithmetic coding(CABAC) decoding

Context-based adaptive binary arithmetic coding(CABAC) is the major entropy-coding algorithm employed in H.264/AVC.In this paper,we present a new VLSI architecture design for an H.264/AVC CABAC decoder,which optimizes both decode decision and decode bypass engines for high throughput,and improves context model allocation for efficient external memory access.Based on the fact that the most possible symbol(MPS) branch is much simpler than the least possible symbol(LPS) branch,a newly organized decode decision engine consisting of two serially concatenated MPS branches and one LPS branch is proposed to achieve better parallelism at lower timing path cost.A look-ahead context index(ctxIdx) calculation mechanism is designed to provide the context model for the second MPS branch.A head-zero detector is proposed to improve the performance of the decode bypass engine according to UEGk encoding features.In addition,to lower the frequency of memory access,we reorganize the context models in external memory and use three circular buffers to cache the context models,neighboring information,and bit stream,respectively.A pre-fetching mechanism with a prediction scheme is adopted to load the corresponding content to a circular buffer to hide external memory latency.Experimental results show that our design can operate at 250 MHz with a 20.71k gate count in SMIC18 silicon technology,and that it achieves an average data decoding rate of 1.5 bins/cycle.

Remote Sensing Image Registration Based on Improved KAZE and BRIEF Descriptor

Remote sensing image registration is still a challenging task owing to the significant influence of nonlinear differences between remote sensing images.To solve this problem,this paper proposes a novel approach with regard to feature-based remote sensing image registration.There are two key contributions:1)we bring forward an improved strategy of composite nonlinear diffusion filtering according to the scale factors in multi-scale space and 2)we design a gradually decreasing resolution of multi-scale pyramid space.And a binary code string is served as feature descriptors to improve matching efficiency.Extensive experiments of different categories of remote image datasets on feature extraction and feature registration are performed.The experimental results demonstrate the superiority of our proposed scheme compared with other classical algorithms in terms of correct matching ratio,accuracy and computation efficiency.

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

Conventional proportional integral derivative(PID)controllers are being used in the industries for control purposes.It is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the systems.Therefore,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the system.This paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient stability.STATCOM with PIDF controller is used to provide reactive power support and to improve stability of the HPSM.The controller parameters are also optimized by using soft computing technique for performance improvement.This paper proposes the effectiveness of symbiosis organisms search algorithm for optimization purpose.Binary coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.