scholarly journals Binary Code Similarity Detection through LSTM and Siamese Neural Network

2021 ◽  
pp. 170956
Author(s):  
Zhengping Luo ◽  
Tao Hou ◽  
Xiangrong Zhou ◽  
Hui Zeng ◽  
Zhuo Lu
Keyword(s):  
Neural Network ◽  
Binary Code ◽  
Similarity Detection

scholarly journals Order Matters: Semantic-Aware Neural Networks for Binary Code Similarity Detection

2020 ◽  
Vol 34 (01) ◽  
pp. 1145-1152 ◽  
Author(s):  
Zeping Yu ◽  
Rui Cao ◽  
Qiyi Tang ◽  
Sen Nie ◽  
Junzhou Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Security ◽  
Semantic Information ◽  
Binary Code ◽  
Graph Matching ◽  
Control Flow ◽  
Binary Function ◽  
Similarity Detection ◽  
Block Level

Binary code similarity detection, whose goal is to detect similar binary functions without having access to the source code, is an essential task in computer security. Traditional methods usually use graph matching algorithms, which are slow and inaccurate. Recently, neural network-based approaches have made great achievements. A binary function is first represented as an control-flow graph (CFG) with manually selected block features, and then graph neural network (GNN) is adopted to compute the graph embedding. While these methods are effective and efficient, they could not capture enough semantic information of the binary code. In this paper we propose semantic-aware neural networks to extract the semantic information of the binary code. Specially, we use BERT to pre-train the binary code on one token-level task, one block-level task, and two graph-level tasks. Moreover, we find that the order of the CFG's nodes is important for graph similarity detection, so we adopt convolutional neural network (CNN) on adjacency matrices to extract the order information. We conduct experiments on two tasks with four datasets. The results demonstrate that our method outperforms the state-of-art models.

scholarly journals A Lightweight Cross-Version Binary Code Similarity Detection Based on Similarity and Correlation Coefficient Features

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 120501-120512
Author(s):  
Hui Guo ◽  
Shuguang Huang ◽  
Cheng Huang ◽  
Min Zhang ◽  
Zulie Pan ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Binary Code ◽  
Similarity Detection

scholarly journals Coral Reef Image Classifications with Hybrid Methods

2019 ◽  
Vol 8 (11S) ◽  
pp. 1247-1254
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Coral Species ◽  
Binary Code ◽  
Hybrid Methods ◽  
Machine Learning Algorithms ◽  
Local Descriptors ◽  
Feature Descriptors ◽  
The One ◽  
State Of Art

There are several organisms on oceans. Among the organisms coral reefs are the one with 800 species. Classifying coral is a difficult task. Scientist classify the coral organism and put in to groups based on their characteristics. There are several machine learning algorithms are implemented to analyzer and classify the coral species. The main aim of this work is to effectively use handcrafted features with deep features for classifying the coral classes. Here the state of art feature descriptors such as Local Binary Pattern, Local Arc Pattern and Improved Webbers Binary Code are proposed to extract the features of coral. The results which obtained can be further improved by combining these local descriptors with convolution neural network .The feature extracted by above methods are classified using KNN and Random Forest. Experiments with these methods are conducted using EILAT dataset. The Experimental results obtained by these methods demonstrate the effectiveness and robustness of our proposed method.

scholarly journals Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network

2021 ◽  
Vol 5 (6) ◽  
pp. 1070-1082
Author(s):  
I Made Suwija Putra ◽  
Putu Jhonarendra ◽  
Ni Kadek Dwi Rusjayanthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Detection System ◽  
Learning System ◽  
Learning Activities ◽  
Percentage Error ◽  
Similarity Detection ◽  
Online Test ◽  
E Learning ◽  
Artificial Neural Network Ann

E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.

scholarly journals Cross-platform binary code similarity detection based on NMT and graph embedding

2021 ◽  
Vol 18 (4) ◽  
pp. 4528-4551
Author(s):  
Xiaodong Zhu ◽  
◽  
Liehui Jiang ◽  
Zeng Chen ◽  
Keyword(s):  
Binary Code ◽  
Graph Embedding ◽  
Similarity Detection ◽  
Cross Platform

scholarly journals Биоморфный нейропроцессор – прототип компьютера нового поколения, являющегося носителем искусственного интеллекта. Часть 2

2021 ◽  
Vol 14 (1) ◽  
pp. 68-79
Author(s):  
С.Ю. Удовиченко ◽  
А.Д. Писарев ◽  
А.Н. Бусыгин ◽  
А.Н. Бобылев
Keyword(s):  
Neural Network ◽  
Binary Code ◽  
Digital Information ◽  
Electrical Model ◽  
Input And Output ◽  
New Knowledge ◽  
New Association ◽  
Hardware Neural Network ◽  
First Time ◽  
Self Learning

Во входном и выходном устройствах биоморфного нейропроцессора происходят первичная и конечная обработка информации. Представлены результаты по сжатию на входе цифровой информации и ее кодированию в импульсы, а также по декодированию информации об активации нейронов на выходе в цифровой двоичный код. Представлена реализация аппаратной нейросети процессора на основе оригинальной биоморфной электрической модели нейрона. Приведены результаты SPICE-моделирования и экспериментального исследования процессов обработки сигналов в режимах маршрутизации выходных импульсов нейронов на синапсы других нейронов в логической матрице, скалярного умножения матрицы чисел на вектор, а также ассоциативного самообучения в запоминающей матрице. Впервые продемонстрирована генерация новой ассоциации (нового знания) как в компьютерном моделировании, так и в изготовленном мемристорно-диодном кроссбаре, в отличие от самообучения в существующих аппаратных нейросетях с синапсами на базе дискретных мемристоров. Primary and ultimate information processing takes place in the input and output devices of the biomorphic neuroprocessor. The results are presented on the compression of digital information at the input and its coding into pulses, as well as on the decoding of information about the activation of neurons at the output into a digital binary code. An implementation of a hardware neural network of a processor based on an original biomorphic electrical model of a neuron is presented. The results of SPICE modeling and experimental research of signal processing processes in the modes of routing neuron output pulses to synapses of other neurons in a logical matrix, scalar multiplication of a matrix of numbers by a vector, and associative selflearning in a memory matrix are presented. For the first time, the generation of a new association (new knowledge) was demonstrated both in computer simulation and in a fabricated memristor-diode crossbar, in contrast to self-learning in existing hardware neural networks with synapses based on discrete memristors.

Sign in / Sign up

Export Citation Format

Share Document