A new procedure for misbehavior detection in vehicular ad-hoc networks using machine learning

Misbehavior detection in vehicular ad hoc networks (VANETs) is performed to improve the traffic safety and driving accuracy. All the nodes in the VANETs communicate to each other through message logs. Malicious nodes in the VANETs can cause inevitable situation by sending message logs with tampered values. In this work, various machine learning algorithms are used to detect the primarily five types of attacks namely, constant attack, constant offset attack, random attack, random offset attack, and eventual attack. Firstly, each attack is detected by different machine learning algorithms using binary classification. Then, the new procedure is created to do the multi classification of the attacks on best chosen algorithm from different machine learning techniques. The highest accuracy in case of binary classification is obtained with Naïve Bayes (100%), decision tree (100%), and random forest (100%) in type1 attack, decision tree (100%) in type2 attack, and random forest (98.03%, 95.56%, and 95.55%) in Type4, Type8 and Type16 attack respectively. In case of new procedure for multi-classification, the highest accuracy is obtained with random forest (97.62%) technique. For this work, VeReMi dataset (a public repository for the malicious node detection in VANETs) is used

A predictive model for network intrusion detection using stacking approach

Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study

Performance analysis of binary and multiclass models using azure machine learning

Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time

Data mining approach for predicting the daily Internet data traffic of a smart university

Internet traffic measurement and analysis generate dataset that are indicators of usage trends, and such dataset can be used for traffic prediction via various statistical analyses. In this study, an extensive analysis was carried out on the daily internet traffic data generated from January to December, 2017 in a smart university in Nigeria. The dataset analysed contains seven key features: the month, the week, the day of the week, the daily IP traffic for the previous day, the average daily IP traffic for the two previous days,the traffic status classification (TSC) for the download and the TSC for the upload internet traffic data. The data mining analysis was performed using four learning algorithms: the Decision Tree, the Tree Ensemble, the Random Forest, and the Naïve Bayes Algorithm on KNIME (Konstanz Information Miner) data mining application and kNN, Neural Network, Random Forest, Naïve Bayes and CN2 Rule Inducer algorithms on the Orange platform. A comparative performance analysis for the models is presented using the confusion matrix, Cohen’s Kappa value, the accuracy of each model, Area under ROC Curve, etc. A minimum accuracy of 55.66% was observed for both the upload and the download IP data on the KNIME platform while minimum accuracies of 57.3% and 51.4% respectively were observed on the Orange platform

Big data, factor clave para la sociedad del conocimiento

We are currently in an era of information explosion that affects our life in one way or another. Because of this, the transformation of huge databases into knowledge has become one of the tasks of greatest interest to society in general. Big Data was born as an instrument for knowledge due to the inability of current computer systems to store and process large volumes of data. The knowledge society arises from the use of technologies such as Big Data. The purpose of this article is to analyze the influence of Big Data on the knowledge society through a review of the state of the art supported by research articles and books published in the last 15 years, which allow us to put these two terms into context, understand their relationship and highlight the influence of Big Data as a generator of knowledge for today's society. The concept of Big Data, and its main applications to society will be defined. The concept of the Information Society is addressed and the main challenges it has are established. The relationship between both concepts is determined. And finally the conclusions are established. In order to reduce the digital divide, it is imperative to make profound long-term changes in educational models and public policies on investment, technology and employment that allow the inclusion of all social classes. In this sense, knowledge societies with the help of Big Data are called to be integrative elements and transform the way they are taught and learned, the way they are investigated, new social and economic scenarios are simulated, the brand decisions in Companies and share knowledge.Actualmente estamos en una época de explosión de información que afecta de una u otra manera nuestra vida. Debido a esto, la transformación de enormes bases de datos en conocimiento se ha convertido en una de las tareas de mayor interés para la sociedad en general. Big Data nace como instrumento para el conocimiento ante la incapacidad de los sistemas informáticos actuales para  almacenar  y  procesar  grandes  volúmenes  de  datos.  La  sociedad  de  conocimiento  surge del uso de tecnologías como del Big Data. El presente artículo tiene por objetivo analizar la influencia del Big Data sobre la sociedad del conocimiento por medio de una revisión del estado del arte soportada en artículos de investigación y libros publicados en los últimos 15 años, que permitan colocar en contexto estos dos términos, entender su relación y poner de manifiesto la influencia del Big Data como generador de conocimiento para la sociedad actual. Se definirá el concepto de Big Data, y sus principales aplicaciones a la sociedad. Se aborda el concepto de  Sociedad  de  la  Información  y  se  establecen  los  principales  desafíos  que  esta  posee.  Se determina la relación entre ambos conceptos. Y Finalmente se establecen las conclusiones. A fin de disminuir la brecha digital, es imperativo realizar cambios profundos a largo plazo en los modelos  educativos  y  las  políticas  públicas  sobre  inversión,  tecnología  y  empleo  que  permitan  la  inclusión  de  todas  las  clases  sociales.  En  este  sentido,  las  sociedades  del  conocimiento con  la  ayuda  de  Big  Data  están  llamadas  a  ser  elementos  integradores  y  a  transformar  la  forma  en  que  se  enseñan  y  aprenden,  la  forma  en  que  se  investigan,  se  simulan  nuevos  escenarios sociales y económicos, la marca decisiones en empresas y compartir conocimiento

An Efficient Intrusion Detection System to Combat Cyber Threats using a Deep Neural Network Model

The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and  CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool

Data mining approach for predicting the daily Internet data traffic of a smart university

Internet traffic measurement and analysis generate dataset that are indicators of usage trends, and such dataset can be used for traffic prediction via various statistical analyses. In this study, an extensive analysis was carried out on the daily internet traffic data generated from January to December, 2017 in a smart university in Nigeria. The dataset analysed contains seven key features: the month, the week, the day of the week, the daily IP traffic for the previous day, the average daily IP traffic for the two previous days, the traffic status classification (TSC) for the download and the TSC for the upload internet traffic data. The data mining analysis was performed using four learning algorithms: the Decision Tree, the Tree Ensemble, the Random Forest, and the Naïve Bayes Algorithm on KNIME (Konstanz Information Miner) data mining application and kNN, Neural Network, Random Forest, Naïve Bayes and CN2 Rule Inducer algorithms on the Orange platform. A comparative performance analysis for the models is presented using the confusion matrix, Cohen’s Kappa value, the accuracy of each model, Area under ROC Curve, etc. A minimum accuracy of 55.66% was observed for both the upload and the download IP data on the KNIME platform while minimum accuracies of 57.3% and 51.4% respectively were observed on the Orange platform

PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS FOR INTRUSION DETECTION SYSTEM

The escalation of hazards to safety and hijacking of digital networks are among the strongest perilous difficulties that must be addressed in the present day. Numerous safety procedures were set up to track and recognize any illicit activity on the network\u27s infrastructure. IDS are the best way to resist and recognize intrusions on internet connections and digital technologies. To classify network traffic as normal or anomalous, Machine Learning (ML) classifiers are increasingly utilized. An IDS with machine learning increases the accuracy with which security attacks are detected. This paper focuses on intrusion detection systems (IDSs) analysis using ML techniques. IDSs utilizing ML techniques are efficient and precise at identifying network assaults. In data with large dimensional spaces, however, the efficacy of these systems degrades. correspondingly, the case is essential to execute a feasible feature removal technique capable of getting rid of characteristics that have little effect on the classification process. In this paper, we analyze the KDD CUP-\u2799\u27 intrusion detection dataset used for training and validating ML models. Then, we implement ML classifiers such as “Logistic Regression, Decision Tree, K-Nearest Neighbour, Naïve Bayes, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, XG-Boost Classifier, Ada-Boost, Random Forest, SVM, Rocchio classifier, Ridge, Passive-Aggressive classifier, ANN besides Perceptron (PPN), the optimal classifiers are determined by comparing the results of Stochastic Gradient Descent and back-propagation neural networks for IDS”, Conventional categorization indicators, such as accuracy, precision, recall, and the f1-measure , have been used to evaluate the performance of the ML classification algorithms

Intrusion detection model using machine learning algorithm on Big Data environment

Abstract Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process. This paper introduced Spark-Chi-SVM model for intrusion detection. In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform. We used KDD99 to train and test the model. In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier. The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data