Flow Monitoring Explained: From Packet Capture to Data Analysis With NetFlow and IPFIX

Flow monitoring has become a prevalent method for monitoring traffic in high-speed networks. By focusing on the analysis of flows, rather than individual packets, it is often said to be more scalable than traditional packet-based traffic analysis. Flow monitoring embraces the complete chain of packet observation, flow export using protocols such as NetFlow and IPFIX, data collection, and data analysis. In contrast to what is often assumed, all stages of flow monitoring are closely intertwined. Each of these stages therefore has to be thoroughly understood, before being able to perform sound flow measurements. Otherwise, flow data artifacts and data loss can be the consequence, potentially without being observed. This paper is the first of its kind to provide an integrated tutorial on all stages of a flow monitoring setup. As shown throughout this paper, flow monitoring has evolved from the early 1990s into a powerful tool, and additional functionality will certainly be added in the future. We show, for example, how the previously opposing approaches of deep packet inspection and flow monitoring have been united into novel monitoring approaches

Tietoverkkojen valvonnan yhdenmukaistaminen

As the modern society is increasingly dependant on computer networks especially as the Internet of Things gaining popularity, a need to monitor computer networks along with associated devices increases. Additionally, the amount of cyber attacks is increasing and certain malware such as Mirai target especially network devices. In order to effectively monitor computer networks and devices, effective solutions are required for collecting and storing the information. This thesis designs and implements a novel network monitoring system. The presented system is capable of utilizing state-of-the-art network monitoring protocols and harmonizing the collected information using a common data model. This design allows effective queries and further processing on the collected information. The presented system is evaluated by comparing the system against the requirements imposed on the system, by assessing the amount of harmonized information using several protocols and by assessing the suitability of the chosen data model. Additionally, the protocol overheads of the used network monitoring protocols are evaluated. The presented system was found to fulfil the imposed requirements. Approximately 21% of the information provided by the chosen network monitoring protocols could be harmonized into the chosen data model format. The result is sufficient for effective querying and combining the information, as well as for processing the information further. The result can be improved by extending the data model and improving the information processing. Additionally, the chosen data model was shown to be suitable for the use case presented in this thesis.Yhteiskunnan ollessa jatkuvasti verkottuneempi erityisesti Esineiden Internetin kasvattaessa suosiotaan, tarve seurata sekä verkon että siihen liitettyjen laitteiden tilaa ja mahdollisia poikkeustilanteita kasvaa. Lisäksi tietoverkkohyökkäysten määrä on kasvamassa ja erinäiset haittaohjelmat kuten Mirai, ovat suunnattu erityisesti verkkolaitteita kohtaan. Jotta verkkoa ja sen laitteiden tilaa voidaan seurata, tarvitaan tehokkaita ratkaisuja tiedon keräämiseen sekä säilöntään. Tässä diplomityössä suunnitellaan ja toteutetaan verkonvalvontajärjestelmä, joka mahdollistaa moninaisten verkonvalvontaprotokollien hyödyntämisen tiedonkeräykseen. Lisäksi järjestelmä säilöö kerätyn tiedon käyttäen yhtenäistä tietomallia. Yhtenäisen tietomallin käyttö mahdollistaa tiedon tehokkaan jatkojalostamisen sekä haut tietosisältöihin. Diplomityössä esiteltävän järjestelmän ominaisuuksia arvioidaan tarkastelemalla, minkälaisia osuuksia eri verkonvalvontaprotokollien tarjoamasta informaatiosta voidaan yhdenmukaistaa tietomalliin, onko valittu tietomalli soveltuva verkonvalvontaan sekä varmistetaan esiteltävän järjestelmän täyttävän sille asetetut vaatimukset. Lisäksi työssä arvioidaan käytettävien verkonvalvontaprotokollien siirtämisen kiinteitä kustannuksia kuten otsakkeita. Työssä esitellyn järjestelmän todettiin täyttävän sille asetetut vaatimukset. Eri verkonvalvontaprotokollien tarjoamasta informaatiosta keskimäärin 21% voitiin harmonisoida tietomalliin. Saavutettu osuus on riittävä, jotta eri laitteista saatavaa informaatiota voidaan yhdistellä ja hakea tehokkaasti. Lukemaa voidaan jatkossa parantaa laajentamalla tietomallia sekä kehittämällä kerätyn informaation prosessointia. Lisäksi valittu tietomalli todettiin soveltuvaksi tämän diplomityön käyttötarkoitukseen

Characterizing the IoT ecosystem at scale

Internet of Things (IoT) devices are extremely popular with home, business, and industrial users. To provide their services, they typically rely on a backend server in- frastructure on the Internet, which collectively form the IoT Ecosystem. This ecosys- tem is rapidly growing and offers users an increasing number of services. It also has been a source and target of significant security and privacy risks. One notable exam- ple is the recent large-scale coordinated global attacks, like Mirai, which disrupted large service providers. Thus, characterizing this ecosystem yields insights that help end-users, network operators, policymakers, and researchers better understand it, obtain a detailed view, and keep track of its evolution. In addition, they can use these insights to inform their decision-making process for mitigating this ecosystem’s security and privacy risks. In this dissertation, we characterize the IoT ecosystem at scale by (i) detecting the IoT devices in the wild, (ii) conducting a case study to measure how deployed IoT devices can affect users’ privacy, and (iii) detecting and measuring the IoT backend infrastructure. To conduct our studies, we collaborated with a large European Internet Service Provider (ISP) and a major European Internet eXchange Point (IXP). They rou- tinely collect large volumes of passive, sampled data, e.g., NetFlow and IPFIX, for their operational purposes. These data sources help providers obtain insights about their networks, and we used them to characterize the IoT ecosystem at scale. We start with IoT devices and study how to track and trace their activity in the wild. We developed and evaluated a scalable methodology to accurately detect and monitor IoT devices with limited, sparsely sampled data in the ISP and IXP. Next, we conduct a case study to measure how a myriad of deployed devices can affect the privacy of ISP subscribers. Unfortunately, we found that the privacy of a substantial fraction of IPv6 end-users is at risk. We noticed that a single device at home that encodes its MAC address into the IPv6 address could be utilized as a tracking identifier for the entire end-user prefix—even if other devices use IPv6 privacy extensions. Our results showed that IoT devices contribute the most to this privacy leakage. Finally, we focus on the backend server infrastructure and propose a methodology to identify and locate IoT backend servers operated by cloud services and IoT vendors. We analyzed their IoT traffic patterns as observed in the ISP. Our analysis sheds light on their diverse operational and deployment strategies. The need for issuing a priori unknown network-wide queries against large volumes of network flow capture data, which we used in our studies, motivated us to develop Flowyager. It is a system built on top of existing traffic capture utilities, and it relies on flow summarization techniques to reduce (i) the storage and transfer cost of flow captures and (ii) query response time. We deployed a prototype of Flowyager at both the IXP and ISP.Internet-of-Things-Geräte (IoT) sind aus vielen Haushalten, Büroräumen und In- dustrieanlagen nicht mehr wegzudenken. Um ihre Dienste zu erbringen, nutzen IoT- Geräte typischerweise auf eine Backend-Server-Infrastruktur im Internet, welche als Gesamtheit das IoT-Ökosystem bildet. Dieses Ökosystem wächst rapide an und bie- tet den Nutzern immer mehr Dienste an. Das IoT-Ökosystem ist jedoch sowohl eine Quelle als auch ein Ziel von signifikanten Risiken für die Sicherheit und Privatsphäre. Ein bemerkenswertes Beispiel sind die jüngsten groß angelegten, koordinierten globa- len Angriffe wie Mirai, durch die große Diensteanbieter gestört haben. Deshalb ist es wichtig, dieses Ökosystem zu charakterisieren, eine ganzheitliche Sicht zu bekommen und die Entwicklung zu verfolgen, damit Forscher, Entscheidungsträger, Endnutzer und Netzwerkbetreibern Einblicke und ein besseres Verständnis erlangen. Außerdem können alle Teilnehmer des Ökosystems diese Erkenntnisse nutzen, um ihre Entschei- dungsprozesse zur Verhinderung von Sicherheits- und Privatsphärerisiken zu verbes- sern. In dieser Dissertation charakterisieren wir die Gesamtheit des IoT-Ökosystems indem wir (i) IoT-Geräte im Internet detektieren, (ii) eine Fallstudie zum Einfluss von benutzten IoT-Geräten auf die Privatsphäre von Nutzern durchführen und (iii) die IoT-Backend-Infrastruktur aufdecken und vermessen. Um unsere Studien durchzuführen, arbeiten wir mit einem großen europäischen Internet- Service-Provider (ISP) und einem großen europäischen Internet-Exchange-Point (IXP) zusammen. Diese sammeln routinemäßig für operative Zwecke große Mengen an pas- siven gesampelten Daten (z.B. als NetFlow oder IPFIX). Diese Datenquellen helfen Netzwerkbetreibern Einblicke in ihre Netzwerke zu erlangen und wir verwendeten sie, um das IoT-Ökosystem ganzheitlich zu charakterisieren. Wir beginnen unsere Analysen mit IoT-Geräten und untersuchen, wie diese im Inter- net aufgespürt und verfolgt werden können. Dazu entwickelten und evaluierten wir eine skalierbare Methodik, um IoT-Geräte mit Hilfe von eingeschränkten gesampelten Daten des ISPs und IXPs präzise erkennen und beobachten können. Als Nächstes führen wir eine Fallstudie durch, in der wir messen, wie eine Unzahl von eingesetzten Geräten die Privatsphäre von ISP-Nutzern beeinflussen kann. Lei- der fanden wir heraus, dass die Privatsphäre eines substantiellen Teils von IPv6- Endnutzern bedroht ist. Wir entdeckten, dass bereits ein einzelnes Gerät im Haus, welches seine MAC-Adresse in die IPv6-Adresse kodiert, als Tracking-Identifikator für das gesamte Endnutzer-Präfix missbraucht werden kann — auch wenn andere Geräte IPv6-Privacy-Extensions verwenden. Unsere Ergebnisse zeigten, dass IoT-Geräte den Großteil dieses Privatsphäre-Verlusts verursachen. Abschließend fokussieren wir uns auf die Backend-Server-Infrastruktur und wir schla- gen eine Methodik zur Identifizierung und Lokalisierung von IoT-Backend-Servern vor, welche von Cloud-Diensten und IoT-Herstellern betrieben wird. Wir analysier- ten Muster im IoT-Verkehr, der vom ISP beobachtet wird. Unsere Analyse gibt Auf- schluss über die unterschiedlichen Strategien, wie IoT-Backend-Server betrieben und eingesetzt werden. Die Notwendigkeit a-priori unbekannte netzwerkweite Anfragen an große Mengen von Netzwerk-Flow-Daten zu stellen, welche wir in in unseren Studien verwenden, moti- vierte uns zur Entwicklung von Flowyager. Dies ist ein auf bestehenden Netzwerkverkehrs- Tools aufbauendes System und es stützt sich auf die Zusammenfassung von Verkehrs- flüssen, um (i) die Kosten für Archivierung und Transfer von Flow-Daten und (ii) die Antwortzeit von Anfragen zu reduzieren. Wir setzten einen Prototypen von Flowyager sowohl im IXP als auch im ISP ein

Journal of Telecommunications and Information Technology, 2011, nr 1

kwartalni

Maritime Data Transfer Protocol (MDTP): A Proposal for a Data Transmission Protocol in Resource-Constrained Underwater Environments Involving Cyber-Physical Systems

The utilization of autonomous maritime vehicles is becoming widespread in operations that are deemed too hazardous for humans to be directly involved in them. One of the ways to increase the productivity of the tools used during missions is the deployment of several vehicles with the same objective regarding data collection and transfer, both for the benefit of human staff and policy makers. However, the interchange of data in such an environment poses major challenges, such as a low bandwidth and the unreliability of the environment where transmissions take place. Furthermore, the relevant information that must be sent, as well as the exact size that will allow understanding it, is usually not clearly established, as standardization works are scarce in this domain. Under these conditions, establishing a way to interchange information at the data level among autonomous maritime vehicles becomes of critical importance since the needed information, along with the size of the transferred data, will have to be defined. This manuscript puts forward the Maritime Data Transfer Protocol, (MDTP) a way to interchange standardized pieces of information at the data level for maritime autonomous maritime vehicles, as well as the procedures that are required for information interchange.SWARMs (Smart and Networking Underwater Robots in Cooperation Meshes) 1034 European research project. It is under Grant Agreement 1035 n.662107-SWARMs-ECSEL-2014-1 and is being partially supported by the Spanish Ministry of Economy and Competitiveness (Ref: PCIN-2014-022-C02-02) and the ECSEL JU

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges

Are Public Intrusion Datasets Fit for Purpose: Characterising the State of the Art in Intrusion Event Datasets

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In recent years cybersecurity attacks have caused major disruption and information loss for online organisations, with high profile incidents in the news. One of the key challenges in advancing the state of the art in intrusion detection is the lack of representative datasets. These datasets typically contain millions of time-ordered events (e.g. network packet traces, flow summaries, log entries); subsequently analysed to identify abnormal behavior and specific attacks [1]. Generating realistic datasets has historically required expensive networked assets, specialised traffic generators, and considerable design preparation. Even with advances in virtualisation it remains challenging to create and maintain a representative environment. Major improvements are needed in the design, quality and availability of datasets, to assist researchers in developing advanced detection techniques. With the emergence of new technology paradigms, such as intelligent transport and autonomous vehicles, it is also likely that new classes of threat will emerge [2]. Given the rate of change in threat behavior [3] datasets become quickly obsolete, and some of the most widely cited datasets date back over two decades. Older datasets have limited value: often heavily filtered and anonymised, with unrealistic event distributions, and opaque design methodology. The relative scarcity of (Intrusion Detection System) IDS datasets is compounded by the lack of a central registry, and inconsistent information on provenance. Researchers may also find it hard to locate datasets or understand their relative merits. In addition, many datasets rely on simulation, originating from academic or government institutions. The publication process itself often creates conflicts, with the need to de-identify sensitive information in order to meet regulations such as General Data Protection Act (GDPR) [4]. Another final issue for researchers is the lack of standardised metrics with which to compare dataset quality. In this paper we attempt to classify the most widely used public intrusion datasets, providing references to archives and associated literature. We illustrate their relative utility and scope, highlighting the threat composition, formats, special features, and associated limitations. We identify best practice in dataset design, and describe potential pitfalls of designing anomaly detection techniques based on data that may be either inappropriate, or compromised due to unrealistic threat coverage. Such contributions as made in this paper is expected to facilitate continuous research and development for effectively combating the constantly evolving cyber threat landscape

Developing an Advanced IPv6 Evasion Attack Detection Framework

Internet Protocol Version 6 (IPv6) is the most recent generation of Internet protocol. The transition from the current Internet Version 4 (IPv4) to IPv6 raised new issues and the most crucial issue is security vulnerabilities. Most vulnerabilities are common between IPv4 and IPv6, e.g. Evasion attack, Distributed Denial of Service (DDOS) and Fragmentation attack. According to the IPv6 RFC (Request for Comment) recommendations, there are potential attacks against various Operating Systems. Discrepancies between the behaviour of several Operating Systems can lead to Intrusion Detection System (IDS) evasion, Firewall evasion, Operating System fingerprint, Network Mapping, DoS/DDoS attack and Remote code execution attack. We investigated some of the security issues on IPv6 by reviewing existing solutions and methods and performed tests on two open source Network Intrusion Detection Systems (NIDSs) which are Snort and Suricata against some of IPv6 evasions and attack methods. The results show that both NIDSs are unable to detect most of the methods that are used to evade detection. This thesis presents a detection framework specifically developed for IPv6 network to detect evasion, insertion and DoS attacks when using IPv6 Extension Headers and Fragmentation. We implemented the proposed theoretical solution into a proposed framework for evaluation tests. To develop the framework, “dpkt” module is employed to capture and decode the packet. During the development phase, a bug on the module used to parse/decode packets has been found and a patch provided for the module to decode the IPv6 packet correctly. The standard unpack function included in the “ip6” section of the “dpkt” package follows extension headers which means following its parsing, one has no access to all the extension headers in their original order. By defining, a new field called all_extension_headers and adding each header to it before it is moved along allows us to have access to all the extension headers while keeping the original parse speed of the framework virtually untouched. The extra memory footprint from this is also negligible as it will be a linear fraction of the size of the whole set of packet. By decoding the packet, extracting data from packet and evaluating the data with user-defined value, the proposed framework is able to detect IPv6 Evasion, Insertion and DoS attacks. The proposed framework consists of four layers. The first layer captures the network traffic and passes it to second layer for packet decoding which is the most important part of the detection process. It is because, if NIDS could not decode and extract the packet content, it would not be able to pass correct information into the Detection Engine process for detection. Once the packet has been decoded by the decoding process, the decoded packet will be sent to the third layer which is the brain of the proposed solution to make a decision by evaluating the information with the defined value to see whether the packet is threatened or not. This layer is called the Detection Engine. Once the packet(s) has been examined by detection processes, the result will be sent to output layer. If the packet matches with a type or signature that system admin chose, it raises an alarm and automatically logs all details of the packet and saves it for system admin for further investigation. We evaluated the proposed framework and its subsequent process via numerous experiments. The results of these conclude that the proposed framework, called NOPO framework, is able to offer better detection in terms of accuracy, with a more accurate packet decoding process, and reduced resources usage compared to both exciting NIDs