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2020-02-10
Palacio, David N., McCrystal, Daniel, Moran, Kevin, Bernal-Cárdenas, Carlos, Poshyvanyk, Denys, Shenefiel, Chris.  2019.  Learning to Identify Security-Related Issues Using Convolutional Neural Networks. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). :140–144.
Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.
Niu, Xiangyu, Li, Jiangnan, Sun, Jinyuan, Tomsovic, Kevin.  2019.  Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning. 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT). :1–6.
Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection (FDI) attacks that can bypass bad data detection mechanisms. Existing mitigation in the power system either focus on redundant measurements or protect a set of basic measurements. These methods make specific assumptions about FDI attacks, which are often restrictive and inadequate to deal with modern cyber threats. In the proposed approach, a deep learning based framework is used to detect injected data measurement. Our time-series anomaly detector adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM) network. To effectively estimate system variables, our approach observes both data measurements and network level features to jointly learn system states. The proposed system is tested on IEEE 39-bus system. Experimental analysis shows that the deep learning algorithm can identify anomalies which cannot be detected by traditional state estimation bad data detection.
Zhan, Ying, Qin, Jin, Huang, Tao, Wu, Kang, Hu, Dan, Zhao, Zhengang, Wang, Yuntao, Cao, Ying, Jiao, RunCheng, Medjadba, Yasmine et al..  2019.  Hyperspectral Image Classification Based on Generative Adversarial Networks with Feature Fusing and Dynamic Neighborhood Voting Mechanism. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. :811–814.

Classifying Hyperspectral images with few training samples is a challenging problem. The generative adversarial networks (GAN) are promising techniques to address the problems. GAN constructs an adversarial game between a discriminator and a generator. The generator generates samples that are not distinguishable by the discriminator, and the discriminator determines whether or not a sample is composed of real data. In this paper, by introducing multilayer features fusion in GAN and a dynamic neighborhood voting mechanism, a novel algorithm for HSIs classification based on 1-D GAN was proposed. Extracting and fusing multiple layers features in discriminator, and using a little labeled samples, we fine-tuned a new sample 1-D CNN spectral classifier for HSIs. In order to improve the accuracy of the classification, we proposed a dynamic neighborhood voting mechanism to classify the HSIs with spatial features. The obtained results show that the proposed models provide competitive results compared to the state-of-the-art methods.

Zhang, Kevin.  2019.  A Machine Learning Based Approach to Identify SQL Injection Vulnerabilities. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :1286–1288.
This paper presents a machine learning classifier designed to identify SQL injection vulnerabilities in PHP code. Both classical and deep learning based machine learning algorithms were used to train and evaluate classifier models using input validation and sanitization features extracted from source code files. On ten-fold cross validations a model trained using Convolutional Neural Network(CNN) achieved the highest precision (95.4%), while a model based on Multilayer Perceptron(MLP) achieved the highest recall (63.7%) and the highest f-measure (0.746).
Shahariar, G. M., Biswas, Swapnil, Omar, Faiza, Shah, Faisal Muhammad, Binte Hassan, Samiha.  2019.  Spam Review Detection Using Deep Learning. 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). :0027–0033.

A robust and reliable system of detecting spam reviews is a crying need in todays world in order to purchase products without being cheated from online sites. In many online sites, there are options for posting reviews, and thus creating scopes for fake paid reviews or untruthful reviews. These concocted reviews can mislead the general public and put them in a perplexity whether to believe the review or not. Prominent machine learning techniques have been introduced to solve the problem of spam review detection. The majority of current research has concentrated on supervised learning methods, which require labeled data - an inadequacy when it comes to online review. Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labeled and unlabeled data and proposed deep learning methods for spam review detection which includes Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and a variant of Recurrent Neural Network (RNN) that is Long Short-Term Memory (LSTM). We have also applied some traditional machine learning classifiers such as Nave Bayes (NB), K Nearest Neighbor (KNN) and Support Vector Machine (SVM) to detect spam reviews and finally, we have shown the performance comparison for both traditional and deep learning classifiers.

2020-01-28
Bernardi, Mario Luca, Cimitile, Marta, Martinelli, Fabio, Mercaldo, Francesco.  2019.  Keystroke Analysis for User Identification Using Deep Neural Networks. 2019 International Joint Conference on Neural Networks (IJCNN). :1–8.

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.

2020-01-27
Farag, Nadine, El-Seoud, Samir Abou, McKee, Gerard, Hassan, Ghada.  2019.  Bullying Hurts: A Survey on Non-Supervised Techniques for Cyber-Bullying Detection. Proceedings of the 2019 8th International Conference on Software and Information Engineering. :85–90.
The contemporary period is scarred by the predominant place of social media in everyday life. Despite social media being a useful tool for communication and social gathering it also offers opportunities for harmful criminal activities. One of these activities is cyber-bullying enabled through the abuse and mistreatment of the internet as a means of bullying others virtually. As a way of minimising this occurrence, research into computer-based researched is carried out to detect cyber-bullying by the scientific research community. An extensive literature search shows that supervised learning techniques are the most commonly used methods for cyber-bullying detection. However, some non-supervised techniques and other approaches have proven to be effective towards cyber-bullying detection. This paper, therefore, surveys recent research on non-supervised techniques and offers some suggestions for future research in textual-based cyber-bullying detection including detecting roles, detecting emotional state, automated annotation and stylometric methods.
Taher, Kazi Abu, Mohammed Yasin Jisan, Billal, Rahman, Md. Mahbubur.  2019.  Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection. 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST). :643–646.
A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate.
2020-01-21
Zhang, Jiange, Chen, Yue, Yang, Kuiwu, Zhao, Jian, Yan, Xincheng.  2019.  Insider Threat Detection Based on Adaptive Optimization DBN by Grid Search. 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). :173–175.
Aiming at the problem that one-dimensional parameter optimization in insider threat detection using deep learning will lead to unsatisfactory overall performance of the model, an insider threat detection method based on adaptive optimization DBN by grid search is designed. This method adaptively optimizes the learning rate and the network structure which form the two-dimensional grid, and adaptively selects a set of optimization parameters for threat detection, which optimizes the overall performance of the deep learning model. The experimental results show that the method has good adaptability. The learning rate of the deep belief net is optimized to 0.6, the network structure is optimized to 6 layers, and the threat detection rate is increased to 98.794%. The training efficiency and the threat detection rate of the deep belief net are improved.
2020-01-20
Huang, Yongjie, Yang, Qiping, Qin, Jinghui, Wen, Wushao.  2019.  Phishing URL Detection via CNN and Attention-Based Hierarchical RNN. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :112–119.
Phishing websites have long been a serious threat to cyber security. For decades, many researchers have been devoted to developing novel techniques to detect phishing websites automatically. While state-of-the-art solutions can achieve superior performances, they require substantial manual feature engineering and are not adept at detecting newly emerging phishing attacks. Therefore, developing techniques that can detect phishing websites automatically and handle zero-day phishing attacks swiftly is still an open challenge in this area. In this work, we propose PhishingNet, a deep learning-based approach for timely detection of phishing Uniform Resource Locators (URLs). Specifically, we use a Convolutional Neural Network (CNN) module to extract character-level spatial feature representations of URLs; meanwhile, we employ an attention-based hierarchical Recurrent Neural Network(RNN) module to extract word-level temporal feature representations of URLs. We then fuse these feature representations via a three-layer CNN to build accurate feature representations of URLs, on which we train a phishing URL classifier. Extensive experiments on a verified dataset collected from the Internet demonstrate that the feature representations extracted automatically are conducive to the improvement of the generalization ability of our approach on newly emerging URLs, which makes our approach achieve competitive performance against other state-of-the-art approaches.
He, Zecheng, Raghavan, Aswin, Hu, Guangyuan, Chai, Sek, Lee, Ruby.  2019.  Power-Grid Controller Anomaly Detection with Enhanced Temporal Deep Learning. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :160–167.
Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (\textbackslashtextgreater99.9%), nearly zero false positives and short (\textbackslashtextless; 360ms) latency.
Osken, Sinem, Yildirim, Ecem Nur, Karatas, Gozde, Cuhaci, Levent.  2019.  Intrusion Detection Systems with Deep Learning: A Systematic Mapping Study. 2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT). :1–4.

In this study, a systematic mapping study was conducted to systematically evaluate publications on Intrusion Detection Systems with Deep Learning. 6088 papers have been examined by using systematic mapping method to evaluate the publications related to this paper, which have been used increasingly in the Intrusion Detection Systems. The goal of our study is to determine which deep learning algorithms were used mostly in the algortihms, which criteria were taken into account for selecting the preferred deep learning algorithm, and the most searched topics of intrusion detection with deep learning algorithm model. Scientific studies published in the last 10 years have been studied in the IEEE Explorer, ACM Digital Library, Science Direct, Scopus and Wiley databases.

Li, Peisong, Zhang, Ying.  2019.  A Novel Intrusion Detection Method for Internet of Things. 2019 Chinese Control And Decision Conference (CCDC). :4761–4765.

Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks.

Ishaque, Mohammed, Hudec, Ladislav.  2019.  Feature extraction using Deep Learning for Intrusion Detection System. 2019 2nd International Conference on Computer Applications Information Security (ICCAIS). :1–5.

Deep Learning is an area of Machine Learning research, which can be used to manipulate large amount of information in an intelligent way by using the functionality of computational intelligence. A deep learning system is a fully trainable system beginning from raw input to the final output of recognized objects. Feature selection is an important aspect of deep learning which can be applied for dimensionality reduction or attribute reduction and making the information more explicit and usable. Deep learning can build various learning models which can abstract unknown information by selecting a subset of relevant features. This property of deep learning makes it useful in analysis of highly complex information one which is present in intrusive data or information flowing with in a web system or a network which needs to be analyzed to detect anomalies. Our approach combines the intelligent ability of Deep Learning to build a smart Intrusion detection system.

2020-01-02
Jung, Byungho, Kim, Taeguen, Im, Eul Gyu.  2018.  Malware Classification Using Byte Sequence Information. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. :143–148.
The number of new malware and new malware variants have been increasing continuously. Security experts analyze malware to capture the malicious properties of malware and to generate signatures or detection rules, but the analysis overheads keep increasing with the increasing number of malware. To analyze a large amount of malware, various kinds of automatic analysis methods are in need. Recently, deep learning techniques such as convolutional neural network (CNN) and recurrent neural network (RNN) have been applied for malware classifications. The features used in the previous approches are mostly based on API (Application Programming Interface) information, and the API invocation information can be obtained through dynamic analysis. However, the invocation information may not reflect malicious behaviors of malware because malware developers use various analysis avoidance techniques. Therefore, deep learning-based malware analysis using other features still need to be developed to improve malware analysis performance. In this paper, we propose a malware classification method using the deep learning algorithm based on byte information. Our proposed method uses images generated from malware byte information that can reflect malware behavioral context, and the convolutional neural network-based sentence analysis is used to process the generated images. We performed several experiments to show the effecitveness of our proposed method, and the experimental results show that our method showed higher accuracy than the naive CNN model, and the detection accuracy was about 99%.
Mar\'ın, Gonzalo, Casas, Pedro, Capdehourat, Germán.  2019.  Deep in the Dark - Deep Learning-Based Malware Traffic Detection Without Expert Knowledge. 2019 IEEE Security and Privacy Workshops (SPW). :36–42.

With the ever-growing occurrence of networking attacks, robust network security systems are essential to prevent and mitigate their harming effects. In recent years, machine learning-based systems have gain popularity for network security applications, usually considering the application of shallow models, where a set of expert handcrafted features are needed to pre-process the data before training. The main problem with this approach is that handcrafted features can fail to perform well given different kinds of scenarios and problems. Deep Learning models can solve this kind of issues using their ability to learn feature representations from input raw or basic, non-processed data. In this paper we explore the power of deep learning models on the specific problem of detection and classification of malware network traffic, using different representations for the input data. As a major advantage as compared to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as the input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones. Our results suggest that deep learning models can better capture the underlying statistics of malicious traffic as compared to classical, shallow-like models, even while operating in the dark, i.e., without any sort of expert handcrafted inputs.

2019-12-30
Wang, Zhicheng, Zhao, Yu, Xue, Mingyu, Tang, Chuan, Huang, Xinrui, Wang, Xin.  2018.  A Simplified and Efficient Method for Facial Key Points Detection. Proceedings of the 3rd International Conference on Intelligent Information Processing. :69–75.
Facial recognition and detection is a traditional problem of computer vision, and the problem of facial key points detection is one of the most important branches of it. As the development of deep learning, more and more methods based on it are proposed for solving related issues, which bring numerous revolutionary changes. In our experiment, we propose a simplified method based on Convolution Neural Network for solving the problem of detecting 5 human facial key points. The method mainly consists of 2 sections. The first one is determining the facial area in an image, the output of which is a matrix representing coordinates of the facial area. The second one detects the relative position within the facial area and fine-tune it repeatedly. In the two sections, we design the structure of Neural Network for it, which size of hidden layers is small but stay efficient. We use Caffe, a popular open source framework of deep learning, to build our neural network and get the satisfactory result.
Amato, Giuseppe, Carrara, Fabio, Falchi, Fabrizio, Gennaro, Claudio, Vairo, Claudio.  2018.  Facial-based Intrusion Detection System with Deep Learning in Embedded Devices. Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing. :64–68.
With the advent of deep learning based methods, facial recognition algorithms have become more effective and efficient. However, these algorithms have usually the disadvantage of requiring the use of dedicated hardware devices, such as graphical processing units (GPUs), which pose restrictions on their usage on embedded devices with limited computational power. In this paper, we present an approach that allows building an intrusion detection system, based on face recognition, running on embedded devices. It relies on deep learning techniques and does not exploit the GPUs. Face recognition is performed using a knn classifier on features extracted from a 50-layers Residual Network (ResNet-50) trained on the VGGFace2 dataset. In our experiment, we determined the optimal confidence threshold that allows distinguishing legitimate users from intruders. In order to validate the proposed system, we created a ground truth composed of 15,393 images of faces and 44 identities, captured by two smart cameras placed in two different offices, in a test period of six months. We show that the obtained results are good both from the efficiency and effectiveness perspective.
Taha, Bilal, Hatzinakos, Dimitrios.  2019.  Emotion Recognition from 2D Facial Expressions. 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE). :1–4.
This work proposes an approach to find and learn informative representations from 2 dimensional gray-level images for facial expression recognition application. The learned features are obtained from a designed convolutional neural network (CNN). The developed CNN enables us to learn features from the images in a highly efficient manner by cascading different layers together. The developed model is computationally efficient since it does not consist of a huge number of layers and at the same time it takes into consideration the overfitting problem. The outcomes from the developed CNN are compared to handcrafted features that span texture and shape features. The experiments conducted on the Bosphours database show that the developed CNN model outperforms the handcrafted features when coupled with a Support Vector Machines (SVM) classifier.
Kim, Sunbin, Kim, Hyeoncheol.  2019.  Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit. 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). :1–4.
Facial expression is the most powerful and natural non-verbal emotional communication method. Facial Expression Recognition(FER) has significance in machine learning tasks. Deep Learning models perform well in FER tasks, but it doesn't provide any justification for its decisions. Based on the hypothesis that facial expression is a combination of facial muscle movements, we find that Facial Action Coding Units(AUs) and Emotion label have a relationship in CK+ Dataset. In this paper, we propose a model which utilises AUs to explain Convolutional Neural Network(CNN) model's classification results. The CNN model is trained with CK+ Dataset and classifies emotion based on extracted features. Explanation model classifies the multiple AUs with the extracted features and emotion classes from the CNN model. Our experiment shows that with only features and emotion classes obtained from the CNN model, Explanation model generates AUs very well.
2019-12-18
Essaid, Meryam, Kim, DaeYong, Maeng, Soo Hoon, Park, Sejin, Ju, Hong Taek.  2019.  A Collaborative DDoS Mitigation Solution Based on Ethereum Smart Contract and RNN-LSTM. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1–6.
Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).
2019-12-16
DiPaola, Steve, Yalçin, Özge Nilay.  2019.  A multi-layer artificial intelligence and sensing based affective conversational embodied agent. 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). :91–92.
Building natural and conversational virtual humans is a task of formidable complexity. We believe that, especially when building agents that affectively interact with biological humans in real-time, a cognitive science-based, multilayered sensing and artificial intelligence (AI) systems approach is needed. For this demo, we show a working version (through human interaction with it) our modular system of natural, conversation 3D virtual human using AI or sensing layers. These including sensing the human user via facial emotion recognition, voice stress, semantic meaning of the words, eye gaze, heart rate, and galvanic skin response. These inputs are combined with AI sensing and recognition of the environment using deep learning natural language captioning or dense captioning. These are all processed by our AI avatar system allowing for an affective and empathetic conversation using an NLP topic-based dialogue capable of using facial expressions, gestures, breath, eye gaze and voice language-based two-way back and forth conversations with a sensed human. Our lab has been building these systems in stages over the years.
Karve, Shreya, Nagmal, Arati, Papalkar, Sahil, Deshpande, S. A..  2018.  Context Sensitive Conversational Agent Using DNN. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). :475–478.
We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.
Alam, Mehreen.  2018.  Neural Encoder-Decoder based Urdu Conversational Agent. 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :901–905.
Conversational agents have very much become part of our lives since the renaissance of neural network based "neural conversational agents". Previously used manually annotated and rule based methods lacked the scalability and generalization capabilities of the neural conversational agents. A neural conversational agent has two parts: at one end an encoder understands the question while the other end a decoder prepares and outputs the corresponding answer to the question asked. Both the parts are typically designed using recurrent neural network and its variants and trained in an end-to-end fashion. Although conversation agents for other languages have been developed, Urdu language has seen very less progress in building of conversational agents. Especially recent state of the art neural network based techniques have not been explored yet. In this paper, we design an attention driven deep encoder-decoder based neural conversational agent for Urdu language. Overall, we make following contributions we (i) create a dataset of 5000 question-answer pairs, and (ii) present a new deep encoder-decoder based conversational agent for Urdu language. For our work, we limit the knowledge base of our agent to general knowledge regarding Pakistan. Our best model has the BLEU score of 58 and gives syntactically and semantically correct answers in majority of the cases.
2019-11-26
Hassanpour, Reza, Dogdu, Erdogan, Choupani, Roya, Goker, Onur, Nazli, Nazli.  2018.  Phishing E-Mail Detection by Using Deep Learning Algorithms. Proceedings of the ACMSE 2018 Conference. :45:1-45:1.

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.