scholarly journals Forecasting number of vulnerabilities using long short-term neural memory network

2021 ◽  
Vol 11 (5) ◽  
pp. 4381
Author(s):  
Mohammad Shamsul Hoque ◽  
Norziana Jamil ◽  
Nowshad Amin ◽  
Azril Azam Abdul Rahim ◽  
Razali B. Jidin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Data Points ◽  
Memory Network ◽  
Past Data ◽  
Long Short Term Memory

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.

scholarly journals Prediksi Penjualan Supermarket Menggunakan Pendekatan Deep Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 160
Author(s):  
Marchel Thimoty Tombeng ◽  
Zalfie Ardian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Time Series Model ◽  
Time Machine ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Berdasarkan data transaksi tahun 2014 sampai 2016 dari salah satu supermarket yang ada di Taiwan, penulis menghasilkan analisa model prediksi dengan menguji data menggunakan metode Deep Learning. Beberapa faktor yang berpengaruh telah di dipelajari dan berguna untuk input prediksi, antara lain keadaan cuacu, diskon, hari raya, dan lain sebagainya. Motivasi utama dari penelitian yang penulis lakukan adalah menggunakan teknologi yang berhubungan dengan eksplorasi data untuk memprediksikan penjualan dari produk-produk dan waktu berkunjung pelangan dalam industry retail, untuk mencari grup target yang tepat dan korelasi produk yang tinggi. Pada akhirnya penulis menciptakan sistem keputusan produk yang berisi analisa visual dan tindakan saran untuk manajer produk pemasaran serta pemangku kepentingan dalam pemasaran produk. Dengan adanya hasil prediksi ini, diharapkan dapat menbantu manajer atau pemangku kepentingan lainnya untuk dapat memasarkan serta menjual produk secara tepat sehingga dapat menghasilkan keuntungan yang banyak dengan menggunkan analisa prediksi yang kami buat. LSTM merupakan model yang sering dipakai dalam Recursive Neural Network (RNN), dan pada dasarnya berfungsi untuk memecahkan masalah dari Time Series. Model Deep Learning yang penulis gunakan adalah Long Short Term Memory (LSTM), dimana model ini menyediakan analisa dan prediksi dari serangkaian data. Sebagai contoh, pada saat akhir pekan pengunjungnya melonjat, maka time machine learning ini akan menambahkan pengartian dari nilai parameter akhir pekan dan nilai ouputnya memiliki korelasi yang kuat.Kata kunci—Predictions, Time Series, LSTM, RNN, Deep Learning

Prediction of InSAR deformation time-series using a long short-term memory neural network

2021 ◽  
Vol 42 (18) ◽  
pp. 6921-6944
Author(s):  
Yi Chen ◽  
Yi He ◽  
Lifeng Zhang ◽  
Youdong Chen ◽  
Hongyu Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

scholarly journals A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

2022 ◽  
Vol 12 (1) ◽  
pp. 721
Author(s):  
Ahmed Nasser ◽  
Huthaifa AL-Khazraji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

scholarly journals Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network

2020 ◽  
Vol 27 (3) ◽  
pp. 373-389 ◽  
Author(s):  
Ashesh Chattopadhyay ◽  
Pedram Hassanzadeh ◽  
Devika Subramanian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Short Term Memory ◽  
Data Driven ◽  
Reservoir Computing ◽  
Short Term ◽  
Term Memory ◽  
Machine Learning Methods ◽  
Long Short Term Memory ◽  
Lorenz 96

Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.

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