An investigation of network security management methods

Network Management (NM) is concerned with reducing complexity and managing cost. The traditional NM tools and techniques are based on the Open System Interconnection (OSI) NM model. However, several drawbacks have been identified when managing a network using traditional NM tools (Sarkar & Verma, 2001). Network security is a major issue when managing a network. Even though the technology assists to reduce security risks, unless properly managed, the security measures may not do the job as expected. The State Model (SM) diagram is a new method, which may assists in managing the network. This new method may provide functionality not currently offered by current NM tools. Furthermore it may be possible to integrate the SM with NM tools. SM diagrams integrate relevant output from devices with protocol finite state information by means of tables. The diagrams are modular and hierarchical thereby providing top down decomposition by means of levelling. Furthermore, their modular, hierarchical characteristics allow technical detail to be introduced in an integrated and controlled manner. The State Model Diagrams were evaluated as a network management tool by twenty participants. The results clearly demonstrated that these diagrams could be of value not only as a NM tool but also as a tool for network security management

A Machine Learning Enhanced Scheme for Intelligent Network Management

The versatile networking services bring about huge influence on daily living styles while the amount and diversity of services cause high complexity of network systems. The network scale and complexity grow with the increasing infrastructure apparatuses, networking function, networking slices, and underlying architecture evolution. The conventional way is manual administration to maintain the large and complex platform, which makes effective and insightful management troublesome. A feasible and promising scheme is to extract insightful information from largely produced network data. The goal of this thesis is to use learning-based algorithms inspired by machine learning communities to discover valuable knowledge from substantial network data, which directly promotes intelligent management and maintenance. In the thesis, the management and maintenance focus on two schemes: network anomalies detection and root causes localization; critical traffic resource control and optimization. Firstly, the abundant network data wrap up informative messages but its heterogeneity and perplexity make diagnosis challenging. For unstructured logs, abstract and formatted log templates are extracted to regulate log records. An in-depth analysis framework based on heterogeneous data is proposed in order to detect the occurrence of faults and anomalies. It employs representation learning methods to map unstructured data into numerical features, and fuses the extracted feature for network anomaly and fault detection. The representation learning makes use of word2vec-based embedding technologies for semantic expression. Next, the fault and anomaly detection solely unveils the occurrence of events while failing to figure out the root causes for useful administration so that the fault localization opens a gate to narrow down the source of systematic anomalies. The extracted features are formed as the anomaly degree coupled with an importance ranking method to highlight the locations of anomalies in network systems. Two types of ranking modes are instantiated by PageRank and operation errors for jointly highlighting latent issue of locations. Besides the fault and anomaly detection, network traffic engineering deals with network communication and computation resource to optimize data traffic transferring efficiency. Especially when network traffic are constrained with communication conditions, a pro-active path planning scheme is helpful for efficient traffic controlling actions. Then a learning-based traffic planning algorithm is proposed based on sequence-to-sequence model to discover hidden reasonable paths from abundant traffic history data over the Software Defined Network architecture. Finally, traffic engineering merely based on empirical data is likely to result in stale and sub-optimal solutions, even ending up with worse situations. A resilient mechanism is required to adapt network flows based on context into a dynamic environment. Thus, a reinforcement learning-based scheme is put forward for dynamic data forwarding considering network resource status, which explicitly presents a promising performance improvement. In the end, the proposed anomaly processing framework strengthens the analysis and diagnosis for network system administrators through synthesized fault detection and root cause localization. The learning-based traffic engineering stimulates networking flow management via experienced data and further shows a promising direction of flexible traffic adjustment for ever-changing environments

Banco de dados distribuídos para auxiliar na gerência de redes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Atualmente a maioria das empresas de médio e grande porte formadas de filiais que geralmente se localizam geograficamente distantes entre si não possuem uma integração entre seus bancos de dados, ou seja, armazenam e consultam apenas os dados locais. Isso é indesejável não somente para informações financeiras e contábeis, mas também no âmbito de gerência de redes. Devido ao crescimento do número de tecnologias de acesso a Internet que usam banda larga e por conseqüência seu barateamento, surge a possibilidade de utilização destes serviços nas empresas. Assim como o crescimento do número de softwares de banco de dados com suporte a distribuição de informações (e seu barateamento) facilita a integração de dados. O emprego de banco de dados distribuídos e gerência de redes de forma conjunta torna as informações de gerência (além de dados diversos das filiais) mais integradas e fáceis de se consultar e analisar

Decentralised Approaches for Network Management

Centralised network management has shown inadequacy for efficient management of large heterogenous networks. As a result, several distributed approaches have been adapted to overcome the problem. This paper is a review of decentralised network management techniques and technologies. We explain distributed architectures for network management, and discuss some of the most important implemented distributed network management systems. A comparison is made between these approaches to show the pitfalls and merits of each