Classification of traffic over collaborative IoT and Cloud platforms using deep learning recurrent LSTM

Internet of Things (IoT) and cloud based collaborative platforms are emerging as new infrastructures during recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT-cloud based collaborative platforms to utilize the channel capacity optimally for transmitting the benign traffic and to block the malicious traffic. The traffic classification mechanism should be dynamic and capable enough to classify the network traffic in a quick manner, so that the malevolent traffic can be identified in earlier stages and benign traffic can be channelized to the destined nodes speedily. In this paper, we are presenting deep learning recurrent LSTM based technique to classify the traffic over IoT-cloud platforms. Machine learning techniques (MLTs) have also been employed for comparison of the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is classified into three classes namely Tor-Normal, NonTor-Normal and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet classify the traffic accurately and also helps in reducing the network latency and in enhancing the data transmission rate as well as network throughput

Unknown Network Detection using Machine Learning Method in Packet Sniffing

Packet sniffing is the increased concern in this cyber era. any hacker or intruder can monitor what data is going on in the network. This raises a concern to detect and avoid these intruders. These are in the form of a botnets or small softwares which keeps eye on network traffic. In this work we provided a solution to detect these intruders and monitor the traffic securely.NIMA and MAWI dataset is used for network analysis and machine learning classifiers like SVM, KNN and navie bays are applied and compared. A pre-processing of attributes selection is done before feeding the data into classifiers

Impact of Packet Inter-arrival Time Features for Online Peer-to-Peer (P2P) Classification

Identification of bandwidth-heavy Internet traffic is important for network administrators to throttle high-bandwidth application traffic. Flow features based classification have been previously proposed as promising method to identify Internet traffic based on packet statistical features. The selection of statistical features plays an important role for accurate and timely classification. In this work, we investigate the impact of packet inter-arrival time feature for online P2P classification in terms of accuracy, Kappa statistic and time. Simulations were conducted using available traces from University of Brescia, University of Aalborg and University of Cambridge. Experimental results show that the inclusion of inter-arrival time (IAT) as an online feature increases simulation time and decreases classification accuracy and Kappa statistic

Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)

Self-Learning Algorithms for Intrusion Detection and Prevention Systems (IDPS)

Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network\u27s traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian linear functions as hidden layers display autonomous learning capabilities and are a highly accurate anomaly detection method that can be implemented in cyberattack detection and intrusion prevention with low incidence of false positives

Classification of traffic flows into QoS classes by unsupervised learning and KNN clustering

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier