Search Results for author: Akbar Siami Namin

Found 15 papers, 0 papers with code

Attack Prediction using Hidden Markov Model

no code implementations3 Jun 2021 Shuvalaxmi Dass, Prerit Datta, Akbar Siami Namin

We propose the use of Hidden Markov Model (HMM) to predict the family of related attacks.

Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks

no code implementations3 Jun 2021 Faranak Abri, Luis Felipe Gutierrez, Chaitra T. Kulkarni, Akbar Siami Namin, Keith S. Jones

The results of the open card sorting study showed a large amount of inter-participant variation making the research team wonder how the consequences of security attacks were comprehended by the participants.

Phishing Detection through Email Embeddings

no code implementations28 Dec 2020 Luis Felipe Gutiérrez, Faranak Abri, Miriam Armstrong, Akbar Siami Namin, Keith S. Jones

In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues are captured or disregarded by email embeddings, i. e., vectorizations.

Predicting Emotions Perceived from Sounds

no code implementations4 Dec 2020 Faranak Abri, Luis Felipe Gutiérrez, Akbar Siami Namin, David R. W. Sears, Keith S. Jones

This paper conducts an experiment through which several mainstream and conventional machine learning algorithms are developed to study the prediction of emotions perceived from sounds.

A Concern Analysis of FOMC Statements Comparing The Great Recession and The COVID-19 Pandemic

no code implementations3 Dec 2020 Luis Felipe Gutiérrez, Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin

It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic.

Computational Engineering, Finance, and Science General Economics Economics

Cyber-Attack Consequence Prediction

no code implementations1 Dec 2020 Prerit Datta, Natalie Lodinger, Akbar Siami Namin, Keith S. Jones

We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models.

Cryptography and Security Human-Computer Interaction

Detection of Coincidentally Correct Test Cases through Random Forests

no code implementations14 Jun 2020 Shuvalaxmi Dass, Xiaozhen Xue, Akbar Siami Namin

The performance of coverage-based fault localization greatly depends on the quality of test cases being executed.

Ensemble Learning Fault localization

Fake Reviews Detection through Ensemble Learning

no code implementations14 Jun 2020 Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin, Keith S. Jones, David R. W. Sears

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews.

Ensemble Learning

Clustering Time Series Data through Autoencoder-based Deep Learning Models

no code implementations11 Apr 2020 Neda Tavakoli, Sima Siami-Namini, Mahdi Adl Khanghah, Fahimeh Mirza Soltani, Akbar Siami Namin

In particular, deep learning techniques are capable of capturing and learning hidden features in a given data sets and thus building a more accurate prediction model for clustering and labeling problem.

Time Series

A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM

no code implementations21 Nov 2019 Sima Siami-Namini, Neda Tavakoli, Akbar Siami Namin

The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction.

Time Series

Deep Reinforcement Learning for Detecting Malicious Websites

no code implementations22 May 2019 Moitrayee Chatterjee, Akbar Siami Namin

Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords.

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

no code implementations16 Mar 2018 Sima Siami-Namini, Akbar Siami Namin

Forecasting time series data is an important subject in economics, business, and finance.

Time Series

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