Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection

Alex, Scaria; Rajkumar, T. Dhiliphan

doi:10.1002/int.22584

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…In the existing methods of building LSTM, most people set the initial parameters of the model randomly without a fixed pattern. It can result in the processing power of the neural network model not being fully utilized, and there are some studies on the parameters of the neural network to find the optimal values [31] [32] [33].…”

Section: Sca-hho-lstm Prediction Modelmentioning

confidence: 99%

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

et al. 2022

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In recent years, with the development of modern technology and industrialization, the shipbuilding industry has become an essential field in a country. Offshore operations, such as helicopter takeoff and landing, and ship-to-ship cargo replenishment, need an accurate prediction of ship motion attitude, not only to improve efficiency but also to protect crew members' lives. However, using the single model has limited influence on prediction. Therefore, this paper proposed a Cross-Parallel optimization algorithm framework, which made full use of the development ability of the Harris Hawk optimization algorithm (HHO) and the searching ability of the sine-cosine algorithm (SCA). In this paper, we use Rosenbrock and Rastrigin functions for testing. The results show that the cross-parallel optimization algorithm has faster search speed and stronger optimization capability. The optimization algorithm is used to optimize the hyperparameters of long short-term memory (LSTM) neural networks. It improves the network's training ability to predict the ship's attitude. The proposed method in this paper is compared with 9 models such as BP, LSTM, GRU, BILSTM and TCN. It is tested with three degrees of freedom under different sea states. MAE and RMSE are used as the evaluation indexes of this paper. In the six sets of experimental results, compared with the other nine models, the method in this paper can reduce the MAE by up to 9.2%, 7.2%, 4.7%, 5.4%, 6.4%, and 6.8%, respectively. When using RMSE as the evaluation index, the decrease was at most 10.1%, 8.7%, 5.6%, 7.1%, 7.5%, and 8.2%. The experimental results show that the method in this paper has the highest prediction accuracy.

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Section: Sca-hho-lstm Prediction Modelmentioning

confidence: 99%

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

et al. 2022

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“…, 2017; Xingjian et al. , 2015) (Gandhmal and Kumar, 2021) (Scaria and Rajkumar, 2021) in which the affective-state prediction of the learners is performed. Deep LSTM is performed with the proposed RiderSSA and is obtained by integrating ROA and SSA.…”

Section: Proposed Riderssa-based Deep Lstm For Affective-state and Le...mentioning

confidence: 99%

Course complexity in engineering education using E-learner's affective-state prediction

Rathi¹,

Deshpande²

2022

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PurposeAffective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.Design/methodology/approachA new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.FindingsThe proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.Originality/valueThe proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.

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“…Another choice to detecting malware is to use features obtained by monitoring program execution (Application Programming Interface [API] called, instructions executed, IP addresses accessed, etc.). These behavior analysis-based methods [6][7][8] usually have better performance compared with static analysis-based detection approaches. To further improve the performance, behavior analysis-based detection approaches employ graph models, such as network traffic and control flow, to describe the relationship between program entities.…”

Section: Introductionmentioning

confidence: 99%

Malware detection with dynamic evolving graph convolutional networks

Zhang

Wang

et al. 2022

Int J of Intelligent Sys

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Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats come from a small number of Application Programming Interfaces (APIs) embedded in the API sequences, which are easily ignored or obfuscated in the detection process.Prior works proposed graph-based learning methods to solve this problem using API-level behavior relations.However, the malware detection is still challenging, due to the ignore of the temporal correlation between malicious behaviors. In this study, we model the software behaviors with multiscaled API graph sequences to represent API-level behaviors as well as graph-level temporal behavior correlations. We then propose a novel Dynamic Evolving Graph Convolutional Network (DEGCN) model to capture dynamic evolving pattern of both local API-level and global graph-level software behaviors. In particular, we first extract the API-level (node) representations to capture the directed graph representations for each time slot. We then propose a Graph-encoding-based Gate Recurrent Unit (GGRU) network to capture the graph-level evolving features and their evolving status. The graph features of different time slots and different graph scales are concatenated to detect whether the software is benign or malicious. Our evaluation with two public

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Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection

Cited by 4 publications

References 24 publications

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

Ship Attitude Prediction Model Based on Cross-Parallel Algorithm Optimized Neural Network

Course complexity in engineering education using E-learner's affective-state prediction

Malware detection with dynamic evolving graph convolutional networks

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