Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

: The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models.publishedVersio

Classification of EEG Signals for Prediction of Epileptic Seizures

Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8% , and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.publishedVersio

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.publishedVersio

Apprentissage incrémental de la structure des réseaux bayésiens à partir de flux de données

In the last decade, data stream mining has become an active area of research, due to the importance of its applications and an increase in the generation of streaming data. The major challenges for data stream analysis are unboundedness, adaptiveness in nature and limitations over data access. Therefore, traditional data mining techniques cannot directly apply to the data stream. The problem aggravates for incoming data with high dimensional domains such as social networks, bioinformatics, telecommunication etc, having several hundreds and thousands of variables. It poses a serious challenge for existing Bayesian network structure learning algorithms. To keep abreast with the latest trends, learning algorithms need to incorporate novel data continuously. The existing state of the art in incremental structure learning involves only several tens of variables and they do not scale well beyond a few tens to hundreds of variables. This work investigates a Bayesian network structure learning problem in high dimensional domains. It makes a number of contributions in order to solve these problems. In the first step we proposed an incremental local search approach iMMPC to learn a local skeleton for each variable. Further, we proposed an incremental version of Max-Min Hill-Climbing (MMHC) algorithm to learn the whole structure of the network. We also proposed some guidelines to adapt it with sliding and damped window environments. Finally, experimental results and theoretical justifications that demonstrate the feasibility of our approach demonstrated through extensive experiments on synthetic datasets.Dans la dernière décennie, l’extraction du flux de données est devenu un domaine de recherche très actif. Les principaux défis pour les algorithmes d’analyse de flux sont de gérer leur infinité, de s’adapter au caractère non stationnaire des distributions de probabilités sous-jacentes, et de fonctionner sans relecture. Par conséquent, lestechniques traditionnelles de fouille ne peuvent s’appliquer directement aux flux de données. Le problème s’intensifie pour les flux dont les domaines sont de grande dimension tels que ceux provenant des réseaux sociaux, avec plusieurs centaines voire milliers de variables. Pour rester a jour, les algorithmes d’apprentissage de réseaux Bayésiens doivent pouvoir intégrer des données nouvelles en ligne. L’état de l’art en la matiere implique seulement plusieurs dizaines de variables et ces algorithmes ne fonctionnent pas correctement pour des dimensions supérieures.Ce travail est une contribution au problème d’apprentissage de structure de réseau Bayésien en ligne pour des domaines de haute dimension, et a donné lieu à plusieurs propositions. D’abord, nous avons proposé une approche incrémentale de recherche locale, appelée iMMPC. Ensuite, nous avons proposé une version incrémentale de l’algorithme MMHC pour apprendre la structure du réseau. Nous avons également adapté cet algorithme avec des mécanismes de fenêtre glissante et une pondération privilégiant les données nouvelles. Enfin, nous avons démontré la faisabilité de notre approche par de nombreuses expériences sur des jeux de données synthétiques

Incremental bayesian network structure learning in high dimensional domains

International audienceThe recent advances in hardware and software has led to development of applications generating a large amount of data in real-time. To keep abreast with latest trends, learning algorithms need to incorporate novel data continuously. One of the efficient ways is revising the existing knowledge so as to save time and memory. In this paper, we proposed an incremental algorithm for Bayesian network structure learning. It could deal with high dimensional domains, where whole dataset is not completely available, but grows continuously. Our algorithm learns local models by limiting search space and performs a constrained greedy hill-climbing search to obtain a global model. We evaluated our method on different datasets having several hundreds of variables, in terms of performance and accuracy. The empirical evaluation shows that our method is significantly better than existing state of the art methods and justifies its effectiveness for incremental use

Natural Language to SQL Queries: A Review

The relational database is the way of maintaining, storing, and accessing structured data but in order to access the data in that database the queries need to be translated in the format of SQL queries. Using natural language rather than SQL has introduced the advancement of a new kind of handling strategy called Natural Language Interface to Database frameworks (NLIDB).  NLIDB is a stage towards the turn of events of clever data set frameworks (IDBS) to upgrade the clients in performing adaptable questioning in data sets. A model that can deduce relational database queries from natural language. Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. In this paper, we reviewed the existing framework and compared them based on the aggregation classifier, select column pointer, and the clause pointer. Furthermore, we discussed the role of semantic parsing and neural algorithm’s contribution in predicting the aggregation, column pointer, and clause pointer.  In particular, people with limited background knowledge are unable to access databases with ease. Using natural language interfaces for relational databases is the solution to make natural language to SQL queries.  This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their framework and to highlight the limitations in the existing models

Natural Language to SQL Queries: A Review

The relational database is the way of maintaining, storing, and accessing structured data but in order to access the data in that database the queries need to be translated in the format of SQL queries. Using natural language rather than SQL has introduced the advancement of a new kind of handling strategy called Natural Language Interface to Database frameworks (NLIDB).  NLIDB is a stage towards the turn of events of clever data set frameworks (IDBS) to upgrade the clients in performing adaptable questioning in data sets. A model that can deduce relational database queries from natural language. Advanced neural algorithms synthesize the end-to-end SQL to text relation which results in the accuracy of 80% on the publicly available datasets. In this paper, we reviewed the existing framework and compared them based on the aggregation classifier, select column pointer, and the clause pointer. Furthermore, we discussed the role of semantic parsing and neural algorithm’s contribution in predicting the aggregation, column pointer, and clause pointer.  In particular, people with limited background knowledge are unable to access databases with ease. Using natural language interfaces for relational databases is the solution to make natural language to SQL queries.  This paper presents a review of the existing framework to process natural language to SQL queries and we will also cover some of the speech to SQL model in discussion section, in order to understand their framework and to highlight the limitations in the existing models

Exploring Lightweight Deep Learning Solution for Malware Detection in IoT Constraint Environment

The present era is facing the industrial revolution. Machine-to-Machine (M2M) communication paradigm is becoming prevalent. Resultantly, the computational capabilities are being embedded in everyday objects called things. When connected to the internet, these things create an Internet of Things (IoT). However, the things are resource-constrained devices that have limited computational power. The connectivity of the things with the internet raises the challenges of the security. The user sensitive information processed by the things is also susceptible to the trusability issues. Therefore, the proliferation of cybersecurity risks and malware threat increases the need for enhanced security integration. This demands augmenting the things with state-of-the-art deep learning models for enhanced detection and protection of the user data. Existingly, the deep learning solutions are overly complex, and often overfitted for the given problem. In this research, our primary objective is to investigate a lightweight deep-learning approach maximizes the accuracy scores with lower computational costs to ensure the applicability of real-time malware monitoring in constrained IoT devices. We used state-of-the-art Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM deep learning algorithm on a vanilla configuration trained on a standard malware dataset. The results of the proposed approach show that the simple deep neural models having single dense layer and a few hundred trainable parameters can eliminate the model overfitting and achieve up to 99.45% accuracy, outperforming the overly complex deep learning models

Performance Evaluation of Classification Algorithms for Intrusion Detection on NSL-KDD Using Rapid Miner

The rapid advancement of the internet and its exponentially increasing usage has also exposed it to several vulnerabilities. Consequently, it has become an extremely important that can prevent network security issues. One of the most commonly implemented solutions is Intrusion Detection System (IDS) that can detect unusual attacks and unauthorized access to a secured network. In the past, several machine learning algorithms have been evaluated on the KDD intrusion dataset. However, this paper focuses on the implementation of the four machine learning algorithms: KNN, Random Forest, gradient boosted tree and decision tree. The models are also implemented through the Auto Model feature to determine its convenience. The results show that Gradient Boosted trees have achieved the highest accuracy (99.42%) in comparison to random forest algorithm that achieved the lowest accuracy (93.63%). Full Tex

Performance Evaluation of Classification Algorithms for Intrusion Detection on NSL-KDD Using Rapid Miner

The rapid advancement of the internet and its exponentially increasing usage has also exposed it to several vulnerabilities. Consequently, it has become an extremely important that can prevent network security issues. One of the most commonly implemented solutions is Intrusion Detection System (IDS) that can detect unusual attacks and unauthorized access to a secured network. In the past, several machine learning algorithms have been evaluated on the KDD intrusion dataset. However, this paper focuses on the implementation of the four machine learning algorithms: KNN, Random Forest, gradient boosted tree and decision tree. The models are also implemented through the Auto Model feature to determine its convenience. The results show that Gradient Boosted trees have achieved the highest accuracy (99.42%) in comparison to random forest algorithm that achieved the lowest accuracy (93.63%). Full Tex