Intelligent Network-Based Stepping Stone Detection Approach.

This research intends to introduce a new usage of Artificial Intelligent (AI) approaches in Stepping Stone Detection (SSD) fields of research

Exploratory study to explore the role of ICT in the process of knowledge management in an Indian business environment

In the 21st century and the emergence of a digital economy, knowledge and the knowledge base economy are rapidly growing. To effectively be able to understand the processes involved in the creating, managing and sharing of knowledge management in the business environment is critical to the success of an organization. This study builds on the previous research of the authors on the enablers of knowledge management by identifying the relationship between the enablers of knowledge management and the role played by information communication technologies (ICT) and ICT infrastructure in a business setting. This paper provides the findings of a survey collected from the four major Indian cities (Chennai, Coimbatore, Madurai and Villupuram) regarding their views and opinions about the enablers of knowledge management in business setting. A total of 80 organizations participated in the study with 100 participants in each city. The results show that ICT and ICT infrastructure can play a critical role in the creating, managing and sharing of knowledge in an Indian business environment

A stepping stone perspective to detection of network threats

Current computing trends such as cloud computing, file sharing and social networking promote collaboration and allow greater mobility for users.Nevertheless, these computing trends increase the vulnerability of networks to security threats and challenge network resources.An ingenious technique employed by attackers for retaining anonymity is by exploiting intermediary host computers or stepping stones to instigate attacks on other computers.This paper explores novel application of the stepping stone detection concept in addressing network threats such as spams, backdoors, proxy server intrusions and denial of service attacks.Preliminary stepping stone detection models for each security threat will be constructed and the potential detection process is delineated.These preliminary concepts and models may prove useful for further optimization of network security in conjunction with other conventional detection techniques

Traffic microstructures and network anomaly detection

Much hope has been put in the modelling of network traffic with machine learning methods to detect previously unseen attacks. Many methods rely on features on a microscopic level such as packet sizes or interarrival times to identify reoccurring patterns and detect deviations from them. However, the success of these methods depends both on the quality of corresponding training and evaluation data as well as the understanding of the structures that methods learn. Currently, the academic community is lacking both, with widely used synthetic datasets facing serious problems and the disconnect between methods and data being named the "semantic gap". This thesis provides extensive examinations of the necessary requirements on traffic generation and microscopic traffic structures to enable the effective training and improvement of anomaly detection models. We first present and examine DetGen, a container-based traffic generation paradigm that enables precise control and ground truth information over factors that shape traffic microstructures. The goal of DetGen is to provide researchers with extensive ground truth information and enable the generation of customisable datasets that provide realistic structural diversity. DetGen was designed according to four specific traffic requirements that dataset generation needs to fulfil to enable machine-learning models to learn accurate and generalisable traffic representations. Current network intrusion datasets fail to meet these requirements, which we believe is one of the reasons for the lacking success of anomaly-based detection methods. We demonstrate the significance of these requirements experimentally by examining how model performance decreases when these requirements are not met. We then focus on the control and information over traffic microstructures that DetGen provides, and the corresponding benefits when examining and improving model failures for overall model development. We use three metrics to demonstrate that DetGen is able to provide more control and isolation over the generated traffic. The ground truth information DetGen provides enables us to probe two state-of-the-art traffic classifiers for failures on certain traffic structures, and the corresponding fixes in the model design almost halve the number of misclassifications . Drawing on these results, we propose CBAM, an anomaly detection model that detects network access attacks through deviations from reoccurring flow sequence patterns. CBAM is inspired by the design of self-supervised language models, and improves the AUC of current state-of-the-art by up to 140%. By understanding why several flow sequence structures present difficulties to our model, we make targeted design decisions that improve on these difficulties and ultimately boost the performance of our model. Lastly, we examine how the control and adversarial perturbation of traffic microstructures can be used by an attacker to evade detection. We show that in a stepping-stone attack, an attacker can evade every current detection model by mimicking the patterns observed in streaming services

A graph oriented approach for network forensic analysis

Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex multi-stage intrusions. This dissertation presents a novel graph based network forensic analysis system. The evidence graph model provides an intuitive representation of collected evidence as well as the foundation for forensic analysis. Based on the evidence graph, we develop a set of analysis components in a hierarchical reasoning framework. Local reasoning utilizes fuzzy inference to infer the functional states of an host level entity from its local observations. Global reasoning performs graph structure analysis to identify the set of highly correlated hosts that belong to the coordinated attack scenario. In global reasoning, we apply spectral clustering and Pagerank methods for generic and targeted investigation respectively. An interactive hypothesis testing procedure is developed to identify hidden attackers from non-explicit-malicious evidence. Finally, we introduce the notion of target-oriented effective event sequence (TOEES) to semantically reconstruct stealthy attack scenarios with less dependency on ad-hoc expert knowledge. Well established computation methods used in our approach provide the scalability needed to perform post-incident analysis in large networks. We evaluate the techniques with a number of intrusion detection datasets and the experiment results show that our approach is effective in identifying complex multi-stage attacks