Automatic Configuration of Programmable Logic Controller Emulators

Programmable logic controllers (PLCs), which are used to control much of the world\u27s critical infrastructures, are highly vulnerable and exposed to the Internet. Many efforts have been undertaken to develop decoys, or honeypots, of these devices in order to characterize, attribute, or prevent attacks against Industrial Control Systems (ICS) networks. Unfortunately, since ICS devices typically are proprietary and unique, one emulation solution for a particular vendor\u27s model will not likely work on other devices. Many previous efforts have manually developed ICS honeypots, but it is a very time intensive process. Thus, a scalable solution is needed in order to automatically configure PLC emulators. The ScriptGenE Framework presented in this thesis leverages several techniques used in reverse engineering protocols in order to automatically configure PLC emulators using network traces. The accuracy, flexibility, and efficiency of the ScriptGenE Framework is tested in three fully automated experiments

Network Traffic Analysis Using Stochastic Grammars

Network traffic analysis is widely used to infer information from Internet traffic. This is possible even if the traffic is encrypted. Previous work uses traffic characteristics, such as port numbers, packet sizes, and frequency, without looking for more subtle patterns in the network traffic. In this work, we use stochastic grammars, hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs), as pattern recognition tools for traffic analysis. HMMs are widely used for pattern recognition and detection. We use a HMM inference approach. With inferred HMMs, we use confidence intervals (CI) to detect if a data sequence matches the HMM. To compare HMMs, we define a normalized Markov metric. A statistical test is used to determine model equivalence. Our metric systematically removes the least likely events from both HMMs until the remaining models are statistically equivalent. This defines the distance between models. We extend the use of HMMs to PCFGs, which have more expressive power. We estimate PCFG production probabilities from data. A statistical test is used for detection. We present three applications of HMM and PCFG detection to network traffic analysis. First, we infer the presence of protocol tunneling through Tor (the onion router) anonymization network. The Markov metric quantifies the similarity of network traffic HMMs in Tor to identify the protocol. It also measures communication noise in Tor network. We use HMMs to detect centralized botnet traffic. We infer HMMs from botnet traffic data and detect botnet infections. Experimental results show that HMMs can accurately detect Zeus botnet traffic. To hide their locations better, newer botnets have P2P control structures. Hierarchical P2P botnets contain recursive and hierarchical patterns. We use PCFGs to detect P2P botnet traffic. Experimentation on real-world traffic data shows that PCFGs can accurately differentiate between P2P botnet traffic and normal Internet traffic

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Distributed Internet security and measurement

The Internet has developed into an important economic, military, academic, and social resource. It is a complex network, comprised of tens of thousands of independently operated networks, called Autonomous Systems (ASes). A significant strength of the Internet\u27s design, one which enabled its rapid growth in terms of users and bandwidth, is that its underlying protocols (such as IP, TCP, and BGP) are distributed. Users and networks alike can attach and detach from the Internet at will, without causing major disruptions to global Internet connectivity. This dissertation shows that the Internet\u27s distributed, and often redundant structure, can be exploited to increase the security of its protocols, particularly BGP (the Internet\u27s interdomain routing protocol). It introduces Pretty Good BGP, an anomaly detection protocol coupled with an automated response that can protect individual networks from BGP attacks. It also presents statistical measurements of the Internet\u27s structure and uses them to create a model of Internet growth. This work could be used, for instance, to test upcoming routing protocols on ensemble of large, Internet-like graphs. Finally, this dissertation shows that while the Internet is designed to be agnostic to political influence, it is actually quite centralized at the country level. With the recent rise in country-level Internet policies, such as nation-wide censorship and warrantless wiretaps, this centralized control could have significant impact on international reachability

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

APIC: A method for automated pattern identification and classification

Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML