Security and risk analysis in the cloud with software defined networking architecture

Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data

LIDF: Layered intrusion detection framework for ad-hoc networks

As ad-hoc networks have different characteristics from a wired network, the intrusion detection techniques used for wired networks are no longer sufficient and effective when adapted directly to a wireless ad-hoc network. In this article, first τhe security challenges in intrusion detection for ad-hoc networks are identified and the related work for anomaly detection is discussed. We then propose a layered intrusion detection framework, which consists of collection, detection and alert modules that are handled by local agents. The collection, detection and alert modules are uniquely enabled with the main operations of ad-hoc networking, which are found at the OSI link and network layers. The proposed modules are based on interpolating polynomials and linear threshold schemes. An experimental evaluation of these modules shows their efficiency for several attack scenarios, such as route logic compromise, traffic patterns distortion and denial of service attacks

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A

A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research

With traditional networking, users can configure control plane protocols to match the specific network configuration, but without the ability to fundamentally change the underlying algorithms. With SDN, the users may provide their own control plane, that can control network devices through their data plane APIs. Programmable data planes allow users to define their own data plane algorithms for network devices including appropriate data plane APIs which may be leveraged by user-defined SDN control. Thus, programmable data planes and SDN offer great flexibility for network customization, be it for specialized, commercial appliances, e.g., in 5G or data center networks, or for rapid prototyping in industrial and academic research. Programming protocol-independent packet processors (P4) has emerged as the currently most widespread abstraction, programming language, and concept for data plane programming. It is developed and standardized by an open community and it is supported by various software and hardware platforms. In this paper, we survey the literature from 2015 to 2020 on data plane programming with P4. Our survey covers 497 references of which 367 are scientific publications. We organize our work into two parts. In the first part, we give an overview of data plane programming models, the programming language, architectures, compilers, targets, and data plane APIs. We also consider research efforts to advance P4 technology. In the second part, we analyze a large body of literature considering P4-based applied research. We categorize 241 research papers into different application domains, summarize their contributions, and extract prototypes, target platforms, and source code availability.Comment: Submitted to IEEE Communications Surveys and Tutorials (COMS) on 2021-01-2

Intrusion Detection System against Denial of Service attack in Software-Defined Networking

Das exponentielle Wachstum der Online-Dienste und des über die Kommunikationsnetze übertragenen Datenvolumens macht es erforderlich, die Struktur traditioneller Netzwerke durch ein neues Paradigma zu ersetzen, das sich den aktuellen Anforderungen anpasst. Software-Defined Networking (SDN) ist hierfür eine fortschrittliche Netzwerkarchitektur, die darauf abzielt, das traditionelle Netzwerk in ein flexibleres Netzwerk umzuwandeln, das sich an die wachsenden Anforderungen anpasst. Im Gegensatz zum traditionellen Netzwerk ermöglicht SDN die Entkopplung von Steuer- und Datenebene, um Netzwerkressourcen effizient zu überwachen, zu konfigurieren und zu optimieren. Es verfügt über einen zentralisierten Controller mit einer globalen Netzwerksicht, der seine Ressourcen über programmierbare Schnittstellen verwaltet. Die zentrale Steuerung bringt jedoch neue Sicherheitsschwachstellen mit sich und fungiert als Single Point of Failure, den ein böswilliger Benutzer ausnutzen kann, um die normale Netzwerkfunktionalität zu stören. So startet der Angreifer einen massiven Datenverkehr, der als Distributed-Denial-of-Service Angriff (DDoSAngriff) von der SDN-Infrastrukturebene in Richtung des Controllers bekannt ist. Dieser DDoS-Angriff führt zu einer Sättigung der Steuerkanal-Bandbreite und belegt die Ressourcen des Controllers. Darüber hinaus erbt die SDN-Architektur einige Angriffsarten aus den traditionellen Netzwerken. Der Angreifer fälscht beispielweise die Pakete, um gutartig zu erscheinen, und zielt dann auf die traditionellen DDoS-Ziele wie Hosts, Server, Anwendungen und Router ab. In dieser Arbeit wird das Verhalten von böswilligen Benutzern untersucht. Anschließend wird ein Intrusion Detection System (IDS) zum Schutz der SDN-Umgebung vor DDoS-Angriffen vorgestellt. Das IDS berücksichtigt dabei drei Ansätze, um ausreichendes Feedback über den laufenden Verkehr durch die SDN-Architektur zu erhalten: die Informationen von einem externen Gerät, den OpenFlow-Kanal und die Flow-Tabelle. Daher besteht das vorgeschlagene IDS aus drei Komponenten. Das Inspector Device verhindert, dass böswillige Benutzer einen Sättigungsangriff auf den SDN-Controller starten. Die Komponente Convolutional Neural Network (CNN) verwendet eindimensionale neuronale Faltungsnetzwerke (1D-CNN), um den Verkehr des Controllers über den OpenFlow-Kanal zu analysieren. Die Komponente Deep Learning Algorithm(DLA) verwendet Recurrent Neural Networks (RNN), um die vererbten DDoS-Angriffe zu erkennen. Sie unterstützt auch die Unterscheidung zwischen bösartigen und gutartigen Benutzern als neue Gegenmaßnahme. Am Ende dieser Arbeit werden alle vorgeschlagenen Komponenten mit dem Netzwerkemulator Mininet und der Programmiersprache Python modelliert, um ihre Machbarkeit zu testen. Die Simulationsergebnisse zeigen hierbei, dass das vorgeschlagene IDS im Vergleich zu mehreren Benchmarking- und State-of-the-Art-Vorschlägen überdurchschnittliche Leistungen erbringt.The exponential growth of online services and the data volume transferred over the communication networks raises the need to change the structure of traditional networks to a new paradigm that adapts to the development’s demands. Software- Defined Networking (SDN) is an advanced network architecture aiming to evolve and transform the traditional network into a more flexible network that responds to the new requirements. In contrast to the traditional network, SDN allows decoupling of the control and data planes functionalities to monitor, configure, and optimize network resources efficiently. It has a centralized controller with a global network view to manage its resources using programmable interfaces. The central control brings new security vulnerabilities and acts as a single point of failure, which the malicious user might exploit to disrupt the network functionality. Thus, the attacker launches massive traffic known as Distributed Denial of Service (DDoS) attack from the SDN infrastructure layer towards the controller. This DDoS attack leads to saturation of control channel bandwidth and destroys the controller resources. Furthermore, the SDN architecture inherits some attacks types from the traditional networks. Therefore, the attacker forges the packets to appear benign and then targets the traditional DDoS objectives such as hosts, servers, applications, routers. This work observes the behavior of malicious users. It then presents an Intrusion Detection System (IDS) to safeguard the SDN environment against DDoS attacks. The IDS considers three approaches to obtain sufficient feedback about the ongoing traffic through the SDN architecture: the information from an external device, the OpenFlow channel, and the flow table. Therefore, the proposed IDS consists of three components; Inspector Device prevents the malicious users from launching the saturation attack towards the SDN controller. Convolutional Neural Network (CNN) Component employs the One- Dimensional Convolutional Neural Networks (1D-CNN) to analyze the controller’s traffic through the OpenFlow Channel. The Deep Learning Algorithm (DLA) component employs Recurrent Neural Networks (RNN) to detect the inherited DDoS attacks. The IDS also supports distinguishing between malicious and benign users as a new countermeasure. At the end of this work, the network emulator Mininet and the programming language python model all the proposed components to test their feasibility. The simulation results demonstrate that the proposed IDS outperforms compared several benchmarking and state-of-the-art suggestions

Achieving network resiliency using sound theoretical and practical methods

Computer networks have revolutionized the life of every citizen in our modern intercon- nected society. The impact of networked systems spans every aspect of our lives, from financial transactions to healthcare and critical services, making these systems an attractive target for malicious entities that aim to make financial or political profit. Specifically, the past decade has witnessed an astounding increase in the number and complexity of sophisti- cated and targeted attacks, known as advanced persistent threats (APT). Those attacks led to a paradigm shift in the security and reliability communities’ perspective on system design; researchers and government agencies accepted the inevitability of incidents and malicious attacks, and marshaled their efforts into the design of resilient systems. Rather than focusing solely on preventing failures and attacks, resilient systems are able to maintain an acceptable level of operation in the presence of such incidents, and then recover gracefully into normal operation. Alongside prevention, resilient system design focuses on incident detection as well as timely response. Unfortunately, the resiliency efforts of research and industry experts have been hindered by an apparent schism between theory and practice, which allows attackers to maintain the upper hand advantage. This lack of compatibility between the theory and practice of system design is attributed to the following challenges. First, theoreticians often make impractical and unjustifiable assumptions that allow for mathematical tractability while sacrificing accuracy. Second, the security and reliability communities often lack clear definitions of success criteria when comparing different system models and designs. Third, system designers often make implicit or unstated assumptions to favor practicality and ease of design. Finally, resilient systems are tested in private and isolated environments where validation and reproducibility of the results are not publicly accessible. In this thesis, we set about showing that the proper synergy between theoretical anal- ysis and practical design can enhance the resiliency of networked systems. We illustrate the benefits of this synergy by presenting resiliency approaches that target the inter- and intra-networking levels. At the inter-networking level, we present CPuzzle as a means to protect the transport control protocol (TCP) connection establishment channel from state- exhaustion distributed denial of service attacks (DDoS). CPuzzle leverages client puzzles to limit the rate at which misbehaving users can establish TCP connections. We modeled the problem of determining the puzzle difficulty as a Stackleberg game and solve for the equilibrium strategy that balances the users’ utilizes against CPuzzle’s resilience capabilities. Furthermore, to handle volumetric DDoS attacks, we extend CPuzzle and implement Midgard, a cooperative approach that involves end-users in the process of tolerating and neutralizing DDoS attacks. Midgard is a middlebox that resides at the edge of an Internet service provider’s network and uses client puzzles at the IP level to allocate bandwidth to its users. At the intra-networking level, we present sShield, a game-theoretic network response engine that manipulates a network’s connectivity in response to an attacker who is moving laterally to compromise a high-value asset. To implement such decision making algorithms, we leverage the recent advances in software-defined networking (SDN) to collect logs and security alerts about the network and implement response actions. However, the programma- bility offered by SDN comes with an increased chance for design-time bugs that can have drastic consequences on the reliability and security of a networked system. We therefore introduce BiFrost, an open-source tool that aims to verify safety and security proper- ties about data-plane programs. BiFrost translates data-plane programs into functionally equivalent sequential circuits, and then uses well-established hardware reduction, abstrac- tion, and verification techniques to establish correctness proofs about data-plane programs. By focusing on those four key efforts, CPuzzle, Midgard, sShield, and BiFrost, we believe that this work illustrates the benefits that the synergy between theory and practice can bring into the world of resilient system design. This thesis is an attempt to pave the way for further cooperation and coordination between theoreticians and practitioners, in the hope of designing resilient networked systems

Distributed reflection denial of service attack: A critical review

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks