LAMP: Prompt Layer 7 Attack Mitigation with Programmable Data Planes

While there are various methods to detect application layer attacks or intrusion attempts on an individual end host, it is not efficient to provide all end hosts in the network with heavy-duty defense systems or software firewalls. In this work, we leverage a new concept of programmable data planes, to directly react on alerts raised by a victim and prevent further attacks on the whole network by blocking the attack at the network edge. We call our design LAMP, Layer 7 Attack Mitigation with Programmable data planes. We implemented LAMP using the P4 data plane programming language and evaluated its effectiveness and efficiency in the Behavioral Model (bmv2) environment

High-performance, Platform-Independent DDoS Detection for IoT Ecosystems

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 − 95% and a bandwidth overhead of less than 1%

Toward Network-based DDoS Detection in Software-defined Networks

To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress

Toward a real-time TCP SYN Flood DDoS mitigation using Adaptive Neuro-Fuzzy classifier and SDN Assistance in Fog Computing

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data is generated and needs to be processed in a short period of time. Therefore, the combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, Attacks against fog servers can take many forms, such as Distributed Denial of Service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of Fog computing-based SYN Flood DDoS attacks using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Software Defined Networking (SDN) Assistance (FASA). The simulation results show that FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP SYN floods DDoS attacks.Comment: 16 page

A DDoS Attack Detection and Mitigation with Software-Defined Internet of Things Framework

With the spread of Internet of Things' (IoT) applications, security has become extremely important. A recent distributed denial-of-service (DDoS) attack revealed the ubiquity of vulnerabilities in IoT, and many IoT devices unwittingly contributed to the DDoS attack. The emerging software-defined anything (SDx) paradigm provides a way to safely manage IoT devices. In this paper, we first present a general framework for software-defined Internet of Things (SD-IoT) based on the SDx paradigm. The proposed framework consists of a controller pool containing SD-IoT controllers, SD-IoT switches integrated with an IoT gateway, and IoT devices. We then propose an algorithm for detecting and mitigating DDoS attacks using the proposed SD-IoT framework, and in the proposed algorithm, the cosine similarity of the vectors of the packet-in message rate at boundary SD-IoT switch ports is used to determine whether DDoS attacks occur in the IoT. Finally, experimental results show that the proposed algorithm has good performance, and the proposed framework adapts to strengthen the security of the IoT with heterogeneous and vulnerable devices

Towards continuous threat defense: in-network traffic analysis for IoT gateways

The widespread use of IoT devices has unveiled overlooked security risks. With the advent of ultra-reliable lowlatency communications (URLLC) in 5G, fast threat defense is critical to minimize damage from attacks. IoT gateways, equipped with wireless/wired interfaces, serve as vital frontline defense against emerging threats on IoT edge. However, current gateways struggle with dynamic IoT traffic and have limited defense capabilities against attacks with changing patterns. In-network computing offers fast machine learning-based attack detection and mitigation within network devices, but leveraging its capability in IoT gateways requires new continuous learning capability and runtime model updates. In this work, we present P4Pir, a novel in-network traffic analysis framework for IoT gateways. P4Pir incorporates programmable data plane into IoT gateway, pioneering the utilization of in-network machine learning (ML) inference for fast mitigation. It facilitates continuous and seamless updates of in-network inference models within gateways. P4Pir is prototyped in P4 language on Raspberry Pi and Dell Edge Gateway. With ML inference offloaded to gateway’s data plane, P4Pir’s in-network approach achieves swift attack mitigation and lightweight deployment compared to prior ML-based solutions. Evaluation results using three public datasets show that P4Pir accurately detects and fastly mitigates emerging attacks (>30% accuracy improvement and sub-millisecond mitigation time). The proposed model updates method allows seamless runtime updates without disrupting network traffic

Telephony Denial of Service Defense at Data Plane (TDoSD@DP)

The Session Initiation Protocol (SIP) is an application-layer control protocol used to establish and terminate calls that are deployed globally. A flood of SIP INVITE packets sent by an attacker causes a Telephony Denial of Service (TDoS) incident, during which legitimate users are unable to use telephony services. Legacy TDoS defense is typically implemented as network appliances and not sufficiently deployed to enable early detection. To make TDoS defense more widely deployed and yet affordable, this paper presents TDoSD@DP where TDoS detection and mitigation is programmed at the data plane so that it can be enabled on every switch port and therefore serves as distributed SIP sensors. With this approach, the damage is isolated at a particular switch and bandwidth saved by not sending attack packets further upstream. Experiments have been performed to track the SIP state machine and to limit the number of active SIP session per port. The results show that TDoSD@DP was able to detect and mitigate ongoing INVITE flood attack, protecting the SIP server, and limiting the damage to a local switch. Bringing the TDoS defense function to the data plane provides a novel data plane application that operates at the SIP protocol and a novel approach for TDoS defense implementation.Final Accepted Versio

Container network functions: bringing NFV to the network edge

In order to cope with the increasing network utilization driven by new mobile clients, and to satisfy demand for new network services and performance guarantees, telecommunication service providers are exploiting virtualization over their network by implementing network services in virtual machines, decoupled from legacy hardware accelerated appliances. This effort, known as NFV, reduces OPEX and provides new business opportunities. At the same time, next generation mobile, enterprise, and IoT networks are introducing the concept of computing capabilities being pushed at the network edge, in close proximity of the users. However, the heavy footprint of today's NFV platforms prevents them from operating at the network edge. In this article, we identify the opportunities of virtualization at the network edge and present Glasgow Network Functions (GNF), a container-based NFV platform that runs and orchestrates lightweight container VNFs, saving core network utilization and providing lower latency. Finally, we demonstrate three useful examples of the platform: IoT DDoS remediation, on-demand troubleshooting for telco networks, and supporting roaming of network functions

Cyber deception against DDoS attack using moving target defence framework in SDN IOT-EDGE networks

Software Defined Networking (SDN) networking paradigm advancements are advantageous, but they have also brought new security concerns. The Internet of Things (IoT) Edge Computing servers provide closer access to cloud services and is also a point of target for availability attacks. The Distributed Denial of Service (DDoS) attacks on SDN IoT-Edge Computing caused by botnet of IoT hosts has compromised major services and is still an impending concern due to the Work From Home virtual office shift attributed by Covid19 pandemic. The effectiveness of a Moving Target Defense (MTD) technique based on SDN for combating DDoS attacks in IoT-Edge networks was investigated in this study with a test scenario based on a smart building. An MTD Reactive and Proactive Network Address Shuffling Mechanism was developed, tested, and evaluated with results showing successful defence against UDP, TCP SYN, and LAND DDoS attacks; preventing IoT devices from being botnet compromised due to the short-lived network address; and ensuring reliable system performance