Sonification of Network Traffic Flow for Monitoring and Situational Awareness

Maintaining situational awareness of what is happening within a network is challenging, not least because the behaviour happens within computers and communications networks, but also because data traffic speeds and volumes are beyond human ability to process. Visualisation is widely used to present information about the dynamics of network traffic dynamics. Although it provides operators with an overall view and specific information about particular traffic or attacks on the network, it often fails to represent the events in an understandable way. Visualisations require visual attention and so are not well suited to continuous monitoring scenarios in which network administrators must carry out other tasks. Situational awareness is critical and essential for decision-making in the domain of computer network monitoring where it is vital to be able to identify and recognize network environment behaviours.Here we present SoNSTAR (Sonification of Networks for SiTuational AwaReness), a real-time sonification system to be used in the monitoring of computer networks to support the situational awareness of network administrators. SoNSTAR provides an auditory representation of all the TCP/IP protocol traffic within a network based on the different traffic flows between between network hosts. SoNSTAR raises situational awareness levels for computer network defence by allowing operators to achieve better understanding and performance while imposing less workload compared to visual techniques. SoNSTAR identifies the features of network traffic flows by inspecting the status flags of TCP/IP packet headers and mapping traffic events to recorded sounds to generate a soundscape representing the real-time status of the network traffic environment. Listening to the soundscape allows the administrator to recognise anomalous behaviour quickly and without having to continuously watch a computer screen.Comment: 17 pages, 7 figures plus supplemental material in Github repositor

ForChaos: Real Time Application DDoS detection using Forecasting and Chaos Theory in Smart Home IoT Network

Recently, D/DoS attacks have been launched by zombie IoT devices in smart home networks. They pose a great threat to to network systems with Application Layer DDoS attacks being especially hard to detect due to their stealth and seemingly legitimacy. In this paper, we propose we propose ForChaos, a lightweight detection algorithm for IoT devices, that is based on forecasting and chaos theory to identify flooding and DDoS attacks. For every time-series behaviour collected, a forecasting-technique prediction is generated, based on a number of features, and the error between the two values is calcualted. In order to assess the error of the forecasting from the actual value, the lyapunov exponent is used to detect potential malicious behaviour. In NS-3 we evaluate our detection algorithm through a series of experiments in Flooding and Slow-Rate DDoS attacks. The results are presented and discussed in detail and compared with related studies, demonstrating its effectiveness and robustness

An Overview of Distributed Denial of Service Traffic Detection Approaches

The availability of information and communication (IC) resources is a growing problem caused by the increase in the number of users, IC services, and the capacity constraints. IC resources need to be available to legitimate users at the required time. The availability is of crucial importance in IC environments such as smart city, autonomous vehicle, or critical infrastructure management systems. In the mentioned and similar environments the unavailability of resources can also have negative consequences on people\u27s safety. The distributed denial of service (DDoS) attacks and traffic that such attacks generate, represent a growing problem in the last decade. Their goal is to disable access to the resources for legitimate users. This paper analyses the trends of such traffic which indicates the importance of its detection methods research. The paper also provides an overview of the currently used approaches used in detection system and model development. Based on the analysis of the previous research, the disadvantages of the used approaches have been identified which opens the space and gives the direction for future research. Besides the mentioned this paper highlights a DDoS traffic generated through Internet of things (IoT) devices as an evolving threat that needs to be taken into consideration in the future studies.</p

Unsupervised Anomaly Detection with Unlabeled Data Using Clustering

Intrusions pose a serious security risk in a network environment. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate as verified over the Knowledge Discovery and Data Mining - KDD CUP 1999 dataset

Classification, testing and optimization of intrusion detection systems

Modem network security products vary greatly in their underlying technology and architecture. Since the introduction of intrusion detection decades ago, intrusion detection technologies have continued to evolve rapidly. This rapid change has led to the introduction of a wealth of security devices, technologies and algorithms that perform functions originally associated with intrusion detection systems. This thesis offers an analysis of intrusion detection technologies, proposing a new classification system for intrusion detection systems. Working closely with the development of a new intrusion detection product, this thesis introduces a method of testing related technologies in a production environment by outlining and executing a series of denial of service and scan and probe attacks. Based on the findings of these experiments, a series of enhancements to the core intrusion detection product is introduced to improve its capabilities and adapt to modem needs of security products

A denial of service detector based on maximum likelihood detection and the random neural network

In spite of extensive research in defence against De- nial of Service (DoS), such attacks remain a predom- inant threat in today’s networks. Due to the sim- plicity of the concept and the availability of the rele- vant attack tools, launching a DoS attack is relatively easy, while defending a network resource against it is disproportionately difficult. The first step of any comprehensive protection scheme against DoS is the detection of its existence, ideally long before the de- structive traffic build-up. In this paper we propose a generic approach for DoS detection which uses multi- ple Bayesian classifiers and random neural networks (RNN). Our method is based on measuring various instantaneous and statistical variables describing the incoming network traffic, acquiring a likelihood esti- mation and fusing the information gathered from the individual input features using likelihood averaging and different architectures of RNNs. We present and compare seven different implementations of it and evaluate our experimental results obtained in a large networking testbed

Comparative Analysis Based on Survey of DDOS Attacks’ Detection Techniques at Transport, Network, and Application Layers

Distributed Denial of Service (DDOS) is one of the most prevalent attacks and can be executed in diverse ways using various tools and codes. This makes it very difficult for the security researchers and engineers to come up with a rigorous and efficient security methodology. Even with thorough research, analysis, real time implementation, and application of the best mechanisms in test environments, there are various ways to exploit the smallest vulnerability within the system that gets overlooked while designing the defense mechanism. This paper presents a comprehensive survey of various methodologies implemented by researchers and engineers to detect DDOS attacks at network, transport, and application layers using comparative analysis. DDOS attacks are most prevalent on network, transport, and application layers justifying the need to focus on these three layers in the OSI model