arXiv subjects

Akbar Siami Namin

Publications and source records attributed to Akbar Siami Namin.

34 records · Page 2Linked to original sources

arXiv2020

Cyber-Attack Consequence Prediction

Cyber-physical systems posit a complex number of security challenges due to interconnection of heterogeneous devices having limited processing, communication, and power capabilities. Additionally, the conglomeration of both physical and cyber-space further makes it difficult to devise a single security plan spanning both these spaces. Cyber-security researchers are often overloaded with a variety of cyber-alerts on a daily basis many of which turn out to be false positives. In this paper, we use machine learning and natural language processing techniques to predict the consequences of cyberattacks. The idea is to enable security researchers to have tools at their disposal that makes it easier to communicate the attack consequences with various stakeholders who may have little to no cybersecurity expertise. Additionally, with the proposed approach researchers' cognitive load can be reduced by automatically predicting the consequences of attacks in case new attacks are discovered. We compare the performance through various machine learning models employing word vectors obtained using both tf-idf and Doc2Vec models. In our experiments, an accuracy of 60% was obtained using tf-idf features and 57% using Doc2Vec method for models based on LinearSVC model.

cs.CR↗

arXiv2020

Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition

This paper proposes to use Fast Fourier Transformation-based U-Net (a refined fully convolutional networks) and perform image convolution in neural networks. Leveraging the Fast Fourier Transformation, it reduces the image convolution costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the overall computational costs. The proposed model identifies the object information from the images. We apply the Fast Fourier transform algorithm on an image data set to obtain more accessible information about the image data, before segmenting them through the U-Net architecture. More specifically, we implement the FFT-based convolutional neural network to improve the training time of the network. The proposed approach was applied to publicly available Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated improvement in training time during convolution from $600-700$ ms/step to $400-500$ ms/step. We evaluated the accuracy of our model using Intersection over Union (IoU) metric showing significant improvements.

cs.CV↗

arXiv2020

Fake Reviews Detection through Analysis of Linguistic Features

Online reviews play an integral part for success or failure of businesses. Prior to purchasing services or goods, customers first review the online comments submitted by previous customers. However, it is possible to superficially boost or hinder some businesses through posting counterfeit and fake reviews. This paper explores a natural language processing approach to identify fake reviews. We present a detailed analysis of linguistic features for distinguishing fake and trustworthy online reviews. We study 15 linguistic features and measure their significance and importance towards the classification schemes employed in this study. Our results indicate that fake reviews tend to include more redundant terms and pauses, and generally contain longer sentences. The application of several machine learning classification algorithms revealed that we were able to discriminate fake from real reviews with high accuracy using these linguistic features.

cs.CL↗

arXiv2020

Launching Stealth Attacks using Cloud

Cloud computing offers users scalable platforms and low resource cost. At the same time, the off-site location of the resources of this service model makes it more vulnerable to certain types of adversarial actions. Cloud computing has not only gained major user base, but also, it has the features that attackers can leverage to remain anonymous and stealth. With convenient access to data and technology, cloud has turned into an attack platform among other utilization. This paper reports our study to show that cyber attackers heavily abuse the public cloud platforms to setup their attack environments and launch stealth attacks. The paper first reviews types of attacks launched through cloud environment. It then reports case studies through which the processes of launching cyber attacks using clouds are demonstrated.

cs.CR↗

arXiv2020

Fake Reviews Detection through Ensemble Learning

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms.

cs.LG↗

arXiv2020

Cloud as an Attack Platform

We present an exploratory study of responses from $75$ security professionals and ethical hackers in order to understand how they abuse cloud platforms for attack purposes. The participants were recruited at the Black Hat and DEF CON conferences. We presented the participants' with various attack scenarios and asked them to explain the steps they would have carried out for launching the attack in each scenario. Participants' responses were studied to understand attackers' mental models, which would improve our understanding of necessary security controls and recommendations regarding precautionary actions to circumvent the exploitation of clouds for malicious activities. We observed that in 93.78% of the responses, participants are abusing cloud services to establish their attack environment and launch attacks.

cs.CR↗

arXiv2020

Vulnerability Coverage for Secure Configuration

We present a novel idea on adequacy testing called ``{vulnerability coverage}.'' The introduced coverage measure examines the underlying software for the presence of certain classes of vulnerabilities often found in the National Vulnerability Database (NVD) website. The thoroughness of the test input generation procedure is performed through the adaptation of evolutionary algorithms namely Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The methodology utilizes the Common Vulnerability Scoring System (CVSS), a free and open industry standard for assessing the severity of computer system security vulnerabilities, as a fitness measure for test inputs generation. The outcomes of these evolutionary algorithms are then evaluated in order to identify the vulnerabilities that match a class of vulnerability patterns for testing purposes.

cs.CR↗

arXiv2020

Detection of Coincidentally Correct Test Cases through Random Forests

The performance of coverage-based fault localization greatly depends on the quality of test cases being executed. These test cases execute some lines of the given program and determine whether the underlying tests are passed or failed. In particular, some test cases may be well-behaved (i.e., passed) while executing faulty statements. These test cases, also known as coincidentally correct test cases, may negatively influence the performance of the spectra-based fault localization and thus be less helpful as a tool for the purpose of automated debugging. In other words, the involvement of these coincidentally correct test cases may introduce noises to the fault localization computation and thus cause in divergence of effectively localizing the location of possible bugs in the given code. In this paper, we propose a hybrid approach of ensemble learning combined with a supervised learning algorithm namely, Random Forests (RF) for the purpose of correctly identifying test cases that are mislabeled to be the passing test cases. A cost-effective analysis of flipping the test status or trimming (i.e., eliminating from the computation) the coincidental correct test cases is also reported.

cs.SE↗

arXiv2020

Vulnerability Coverage as an Adequacy Testing Criterion

Mainstream software applications and tools are the configurable platforms with an enormous number of parameters along with their values. Certain settings and possible interactions between these parameters may harden (or soften) the security and robustness of these applications against some known vulnerabilities. However, the large number of vulnerabilities reported and associated with these tools make the exhaustive testing of these tools infeasible against these vulnerabilities infeasible. As an instance of general software testing problem, the research question to address is whether the system under test is robust and secure against these vulnerabilities. This paper introduces the idea of ``vulnerability coverage,'' a concept to adequately test a given application for a certain classes of vulnerabilities, as reported by the National Vulnerability Database (NVD). The deriving idea is to utilize the Common Vulnerability Scoring System (CVSS) as a means to measure the fitness of test inputs generated by evolutionary algorithms and then through pattern matching identify vulnerabilities that match the generated vulnerability vectors and then test the system under test for those identified vulnerabilities. We report the performance of two evolutionary algorithms (i.e., Genetic Algorithms and Particle Swarm Optimization) in generating the vulnerability pattern vectors.

cs.SE↗

arXiv2020

Clustering Time Series Data through Autoencoder-based Deep Learning Models

Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data mining-based analysis radically into a learning-based model in which existing data sets along with their cluster labels (i.e., train set) are learned to build a supervised learning model and predict the cluster labels of unseen data (i.e., test set). 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. However, the major problem is that time series data are often unlabeled and thus supervised learning-based deep learning algorithms cannot be directly adapted to solve the clustering problems for these special and complex types of data sets. To address this problem, this paper introduces a two-stage method for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of given time series data in order to create labels and thus be able to transform the problem from unsupervised learning into supervised learning. Second, an autoencoder-based deep learning model is built to learn and model both known and hidden features of time series data along with their created labels to predict the labels of unseen time series data. The paper reports a case study in which financial and stock time series data of selected 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed procedure is capable of achieving 87.5\% accuracy in clustering and predicting the labels for unseen time series data.

cs.LG↗

arXiv2019

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

Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin. The LSTM-based models incorporate additional "gates" for the purpose of memorizing longer sequences of input data. 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. Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research question of interest is then whether BiLSTM, with additional training capability, outperforms regular unidirectional LSTM. This paper reports a behavioral analysis and comparison of BiLSTM and LSTM models. The objective is to explore to what extend additional layers of training of data would be beneficial to tune the involved parameters. The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models. More specifically, it was observed that BiLSTM models provide better predictions compared to ARIMA and LSTM models. It was also observed that BiLSTM models reach the equilibrium much slower than LSTM-based models.

cs.LG↗

arXiv2019

The Performance of Machine and Deep Learning Classifiers in Detecting Zero-Day Vulnerabilities

The detection of zero-day attacks and vulnerabilities is a challenging problem. It is of utmost importance for network administrators to identify them with high accuracy. The higher the accuracy is, the more robust the defense mechanism will be. In an ideal scenario (i.e., 100% accuracy) the system can detect zero-day malware without being concerned about mistakenly tagging benign files as malware or enabling disruptive malicious code running as none-malicious ones. This paper investigates different machine learning algorithms to find out how well they can detect zero-day malware. Through the examination of 34 machine/deep learning classifiers, we found that the random forest classifier offered the best accuracy. The paper poses several research questions regarding the performance of machine and deep learning algorithms when detecting zero-day malware with zero rates for false positive and false negative.

cs.CR↗

arXiv2019

Deep Reinforcement Learning for Detecting Malicious Websites

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. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A \textit{phisher} usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.

cs.LG↗

arXiv2019

Markov Decision Process to Enforce Moving Target Defence Policies

Moving Target Defense (MTD) is an emerging game-changing defense strategy in cybersecurity with the goal of strengthening defenders and conversely puzzling adversaries in a network environment. The successful deployment of an MTD system can be affected by several factors including 1) the effectiveness of the employed technique, 2) the deployment strategy, 3) the cost of the MTD implementation, and 4) the impact yielded by the enforced security policies. Many research efforts have been spent on introducing a variety of MTD techniques which are often evaluated through simulations. Nevertheless, this line of research needs more attention. In particular, the determination of optimal cost and policy analysis and the selection of those policies in an MTD setting is still an open research question. To advance the state-of-the-art of this line of research, this paper introduces an approach based on control theory to model, analyze and select optimal security policies for Moving Target Defense (MTD) deployment strategies. A Markov Decision Process (MDP) scheme is presented to model states of the system from attacking point of view. The employed value iteration method is based on the Bellman optimality equation for optimal policy selection for each state defined in the system. The model is then utilized to analyze the impact of various costs on the optimal policy. The MDP model is then applied to two case studies to evaluate the performance of the model.

eess.SY↗

arXiv2018

The Sounds of Cyber Threats

The Internet enables users to access vast resources, but it can also expose users to harmful cyber-attacks. This paper investigates human factors issues concerning the use of sounds in a cyber-security domain. It describes a methodology, referred to as sonification, to effectively design and develop auditory cyber-security threat indicators to warn users about cyber-attacks. A case study is presented, along with the results, of various types of usability testing with a number of Internet users who are visually impaired. The paper concludes with a discussion of future steps to enhance this work.

cs.CR↗

arXiv2018

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as "epoch" in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.

cs.LG↗