Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking

P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols

Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

Anomaly Detection in BACnet/IP managed Building Automation Systems

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are often not implemented in BAS deployments, leaving systems unprotected against known network threats. This research investigated methods of detecting anomalous network traffic in BACnet/IP managed BAS in an effort to combat threats posed to these systems. This research explored the threats facing BACnet/IP devices, through analysis of Internet accessible BACnet devices, vendor-defined device specifications, investigation of the BACnet specification, and known network attacks identified in the surrounding literature. The collected data were used to construct a threat matrix, which was applied to models of BACnet devices to evaluate potential exposure. Further, two potential unknown vulnerabilities were identified and explored using state modelling and device simulation. A simulation environment and attack framework were constructed to generate both normal and malicious network traffic to explore the application of machine learning algorithms to identify both known and unknown network anomalies. To identify network patterns between the generated normal and malicious network traffic, unsupervised clustering, graph analysis with an unsupervised community detection algorithm, and time series analysis were used. The explored methods identified distinguishable network patterns for frequency-based known network attacks when compared to normal network traffic. However, as stand-alone methods for anomaly detection, these methods were found insufficient. Subsequently, Artificial Neural Networks and Hidden Markov Models were explored and found capable of detecting known network attacks. Further, Hidden Markov Models were also capable of detecting unknown network attacks in the generated datasets. The classification accuracy of the Hidden Markov Models was evaluated using the Matthews Correlation Coefficient which accounts for imbalanced class sizes and assess both positive and negative classification ability for deriving its metric. The Hidden Markov Models were found capable of repeatedly detecting both known and unknown BACnet/IP attacks with True Positive Rates greater than 0.99 and Matthews Correlation Coefficients greater than 0.8 for five of six evaluated hosts. This research identified and evaluated a range of methods capable of identifying anomalies in simulated BACnet/IP network traffic. Further, this research found that Hidden Markov Models were accurate at classifying both known and unknown attacks in the evaluated BACnet/IP managed BAS network

From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio