Abstract Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and... Outlier and anomaly detection has always been a critical problem in many fields. Although it has been investigated deeply in data mining, the problem has become more difficult and critical in the Big Data era since the volume, velocity and variety of data change drastically with rather complicated types of outliers. In such an environment, where real-time outlier detection and analysis over data streams is a necessity, the existing solutions are no longer effective and sufficient. While many existing algorithms and approaches consider the content of the data stream, there are few approaches which consider the context and conditions in which the content has been produced. In this paper, we propose a novel framework for contextual outlier detection in big data streams which inject the contextual attributes in the stream content as a primary input for outlier detection rather than using the stream content alone or applying the contextual detection on content anomalies only. The detection algorithm incorporates two approaches; the first, a supervised detection method and the other, an unsupervised, which allows the detection process to adapt to the normal change in the stream behavior over time. The detected outliers are either both content and contextual outliers or contextual outliers only. The proposed contextual detection approach prunes the false positive outliers and detects the true negative outliers at the same time. Moreover, in this framework, the detection engine preserves both outliers and context values in which those outliers were detected to be used in the engine self-training and in outliers modeling in order to enhance the outlier prediction accuracy. Read More Read Less

Abstract Whenever a user tries communicating with another recipient on the Internet, vibrant information is sent over different networks until the information is intercepted or normally reaches the recipient. Precarious information crisscrossing networks is usually encrypted. In order to conceal the sender's identity, different implementations have proven successful - one of which is the invention of anonymous communication systems. There are many anonymous communication systems developed but, the... Whenever a user tries communicating with another recipient on the Internet, vibrant information is sent over different networks until the information is intercepted or normally reaches the recipient. Precarious information crisscrossing networks is usually encrypted. In order to conceal the sender's identity, different implementations have proven successful - one of which is the invention of anonymous communication systems. There are many anonymous communication systems developed but, the Onion Router (Tor) is the greatest organized anonymous communication system, which offers online anonymity and privacy. There are a vast number of obstacles in security that have to be considered when deploying Tor. This paper thoroughly investigates and presents these security issues in Tor. Read More Read Less

Abstract We propose an image encryption algorithm based on a 6-dimensional chaotic cellular neural network (CNN) that is robust to noise/missing pixels in the cipher image. We performed parameter search on the templates of the CNN in order to discover the parameters that leads to 6D chaotic evolution of the state, and then used the resulting chaotic sequence as the basis of encryption. The... We propose an image encryption algorithm based on a 6-dimensional chaotic cellular neural network (CNN) that is robust to noise/missing pixels in the cipher image. We performed parameter search on the templates of the CNN in order to discover the parameters that leads to 6D chaotic evolution of the state, and then used the resulting chaotic sequence as the basis of encryption. The encryption process itself consists of shuffling the positions of image pixels based on the numerical value of the chaotic sequence; the second half of the encryption process consists of changing the shuffled image pixel values by performing XOR operation between the pixel values and the numerical value of the chaotic sequence. By using simple operations like sorting and XOR in the encryption process, the algorithm is robust to noise/ missing pixels in the cipher image. We illustrate this by comparing the robustness against 3 recently proposed chaos-based image encryption algorithms. The results show that our algorithm is competitive with the state-ofthe- art in term of encryption security, and superior in term of robustness. Read More Read Less

Abstract Nowadays, Big Data has reached every area of our lives because it covers many tasks in different operation. This new technique forces the cloud computing to use it as a layer, for this reason cloud technology embraces it as Big Data as a service (BDAAS). After solving the problem of storing huge volumes of information circulating on the Internet, remains to us... Nowadays, Big Data has reached every area of our lives because it covers many tasks in different operation. This new technique forces the cloud computing to use it as a layer, for this reason cloud technology embraces it as Big Data as a service (BDAAS). After solving the problem of storing huge volumes of information circulating on the Internet, remains to us how we can protect and ensure that this information are stored without loss or distortion. The aim of this paper is to study the problem of safety in BDAAS, inparticularly we will cover the problem of Intrusion detection system (IDS). In order to solve the problems tackled in this paper, we have proposed a Self-Learning Autonomous Intrusion Detection system (SLA-IDS) which is based on the architecture ofautonomic system to detect the anomaly data. In this approach, to add the autonomy aspect to the proposed system we have used mobile and situated agents. The implementation of this model has been provided to evaluate our system. The obtained findings show the effectiveness of our proposed model. We validate our proposition using Hadoop as Big Data Platform and CloudSim, machine learning Weka with java to create Model of detection. Read More Read Less