scholarly journals Technology of key feature identification s in malware API calls sequences

2021 ◽  
pp. 37-52
Author(s):  
Vladimir V. Voronin ◽  
◽  
Aleksey V. Morozov ◽  
Keyword(s):  
Neural Networks ◽  
Computer Security ◽  
Word Embedding ◽  
Behavioral Analysis ◽  
Feature Identification ◽  
Noise Masking ◽  
Malicious Software ◽  
Key Features ◽  
Conventional Methods ◽  
Semantic Dependencies

Today, almost everyone is faced with computer security problems in one or another way. Antivirus programs are used to control threats to the security of malicious software. Conventional methods for detecting malware are no longer effective enough; nowadays, neural networks and behavioral analysis technology have begun to be used for these purposes. Analyzing the behavior of programs is a difficult task, since there is no clear sequence of actions to accurately identify a program as malicious. In addition, such programs use measures to resist such detection, for example, noise masking the sequence of their work with meaningless actions. There is also the problem of uniquely identifying the class of malware due to the fact that malware can use similar methods, while being assigned to different classes. In this paper, it is proposed to use NLP methods, such as word embedding, and LDA in relation to the problems of analyzing malware API calls sequences in order to reveal the presence of semantic dependencies and assess the effectiveness of the application of these methods. The results obtained indicate the possibility of identifying the key features of malware behavior, which in the future will significantly improve the technology for detecting and identifying such programs.

scholarly journals A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Hua ◽  
Chanqin Quan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Protein Interaction ◽  
Kernel Methods ◽  
State Of The Art ◽  
Relation Extraction ◽  
Word Embedding ◽  
Protein Protein Interaction ◽  
Key Features

The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method(1)only takes the sdp and word embedding as input and(2)could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

scholarly journals Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 1928
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer ◽  
Adel Manaa Dakhil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Transmission Line ◽  
Convolutional Neural Networks ◽  
Transmission Lines ◽  
Fault Classification ◽  
Line System ◽  
Direction Discrimination ◽  
Conventional Methods

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

scholarly journals Multi-Channel Convolutional Neural Networks with Adversarial Training for Few-Shot Relation Classification (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13967-13968
Author(s):  
Yuxiang Xie ◽  
Hua Xu ◽  
Congcong Yang ◽  
Kai Gao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Word Embedding ◽  
Vector Representation ◽  
Adversarial Learning ◽  
Model Experiments ◽  
Robust Model ◽  
Adversarial Training ◽  
Relation Classification

The distant supervised (DS) method has improved the performance of relation classification (RC) by means of extending the dataset. However, DS also brings the problem of wrong labeling. Contrary to DS, the few-shot method relies on few supervised data to predict the unseen classes. In this paper, we use word embedding and position embedding to construct multi-channel vector representation and use the multi-channel convolutional method to extract features of sentences. Moreover, in order to alleviate few-shot learning to be sensitive to overfitting, we introduce adversarial learning for training a robust model. Experiments on the FewRel dataset show that our model achieves significant and consistent improvements on few-shot RC as compared with baselines.

scholarly journals Arabic Word Segmentation With Long Short-Term Memory Neural Networks and Word Embedding

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12879-12887 ◽  
Author(s):  
Abdulrahman Almuhareb ◽  
Waleed Alsanie ◽  
Abdulmohsen Al-Thubaity
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Word Segmentation ◽  
Word Embedding ◽  
Short Term ◽  
Term Memory ◽  
Arabic Word ◽  
Long Short Term Memory

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 Improvement of specialized industrial manipulator movement control

2018 ◽  
Vol 18 ◽  
pp. 01004 ◽  
Author(s):  
Vladimir Kuvshinnikov ◽  
Evgeny Kovshov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Motion Control ◽  
Movement Control ◽  
Efficiency Improvement ◽  
Industrial Robotics ◽  
Industrial Manipulator ◽  
Management Efficiency ◽  
Trajectory Formation ◽  
Key Features

This article deals with the specialized portal manipulator motion control. The key features of the system are analysed, management efficiency improvement task is considered and formulated. An approach to the manipulator trajectory formation is proposed, based on existing mobile and industrial robotics solutions including artificial neural networks.

Intrusion Detection Technique Based on Improved Intuitionistic Fuzzy Neural Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2507-2510
Author(s):  
Yang Lei ◽  
Jing Ma
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Computer Security ◽  
Detection Method ◽  
Fuzzy Neural Networks ◽  
Detection Methods ◽  
Detection Rates ◽  
Intuitionistic Fuzzy ◽  
Sufficient Stability ◽  
Fuzzy Neural

At present, the issue of intrusion detection has been a hot point to all over the computer security area. In this paper, a novel intrusion detection method has been proposed. Unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which leads to much fewer iteration numbers, higher detection rates and sufficient stability. Experimental results show that the now method proposed in this paper is promising and has obvious superiorities over other current typical ones.

Simultaneous Learning of Robot Impedance Parameters Using Neural Networks

2007 ◽  
Vol 19 (1) ◽  
pp. 106-113
Author(s):  
Mutsuhiro Terauchi ◽  
◽  
Yoshiyuki Tanaka ◽  
Seishiro Sakaguchi ◽  
Nan Bu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Simulations ◽  
Impedance Control ◽  
Robotic Manipulator ◽  
Effective Control ◽  
Control Methods ◽  
Learning Method ◽  
Conventional Methods ◽  
Impedance Parameters ◽  
Virtual Impedance

Impedance control is one of the most effective control methods for interaction between a robotic manipulator and its environment. Robot impedance control regulates the response of the manipulator to contact and virtual impedance control regulates the manipulator's response before contact. Although these impedance parameters may be regulated using neural networks, conventional methods do not consider regulating robot impedance and virtual impedance simultaneously. This paper proposes a simultaneous learning method to regulate the impedance parameters using neural networks. The validity of the proposed method is demonstrated in computer simulations of tasks by a multi-joint robotic manipulator.

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