Biblio

Network attacks, especially DoS and DDoS attacks, are a significant threat for all providers of services or infrastructure. The biggest attacks can paralyze even large-scale infrastructures of worldwide companies. Attack mitigation is a complex issue studied by many researchers and security companies. While several approaches were proposed, there is still space for improvement. This paper proposes to augment existing mitigation heuristic with knowledge of reputation score of network entities. The aim is to find a way to mitigate malicious traffic present in DDoS amplification attacks with minimal disruption to communication of legitimate traffic.

Network attacks, including Distributed Denial-of-Service (DDoS), continuously increase in terms of bandwidth along with damage (recent attacks exceed 1.7 Tbps) and have a devastating impact on the targeted companies/governments. Over the years, mitigation techniques, ranging from blackholing to policy-based filtering at routers, and on to traffic scrubbing, have been added to the network operator's toolbox. Even though these mitigation techniques provide some protection, they either yield severe collateral damage, e.g., dropping legitimate traffic (blackholing), are cost-intensive, or do not scale well for Tbps level attacks (ACL filtering, traffic scrubbing), or require cooperation and sharing of resources (Flowspec). In this paper, we propose Advanced Blackholing and its system realization Stellar. Advanced blackholing builds upon the scalability of blackholing while limiting collateral damage by increasing its granularity. Moreover, Stellar reduces the required level of cooperation to enhance mitigation effectiveness. We show that fine-grained blackholing can be realized, e.g., at a major IXP, by combining available hardware filters with novel signaling mechanisms. We evaluate the scalability and performance of Stellar at a large IXP that interconnects more than 800 networks, exchanges more than 6 Tbps traffic, and witnesses many network attacks every day. Our results show that network attacks, e.g., DDoS amplification attacks, can be successfully mitigated while the networks and services under attack continue to operate untroubled.

As Internet of things (IoT) continue to ensconce into our homes, offices, hospitals, electricity grids and other walks of life, the stakes are too high to leave security to chance. IoT devices are resource constrained devices and hence it is very easy to exhaust them of their resources or deny availability. One of the most prominent attacks on the availability is the Distributed Denial of service (DDoS) attack. Although, DDoS is not a new Internet attack but a large number of new, constrained and globally accessible IoT devices have escalated the attack surface beyond imagination. This paper provides a broad anatomy of IoT protocols and their inherent weaknesses that can enable attackers to launch successful DDoS attacks. One of the major contributions of this paper is the implementation and demonstration of UDP (User Datagram Protocol) flood attack in the Contiki operating system, an open-source operating system for the IoT. This attack has been implemented and demonstrated in Cooja simulator, an inherent feature of the Contiki operating system. Furthermore, in this paper, a rate limiting mechanism is proposed that must be incorporated in the Contiki OS to mitigate UDP flood attacks. This proposed scheme reduces CPU power consumption of the victim by 9% and saves the total transmission power of the victim by 55%.

In this paper, we propose a hardware-based defense system in Software-Defined Networking architecture to protect against the HTTP GET Flooding attacks, one of the most dangerous Distributed Denial of Service (DDoS) attacks in recent years. Our defense system utilizes per-URL counting mechanism and has been implemented on FPGA as an extension of a NetFPGA-based OpenFlow switch.

Securing Internet of Things is a challenge because of its multiple points of vulnerability. In particular, Distributed Denial of Service (DDoS) attacks on IoT devices pose a major security challenge to be addressed. In this paper, we propose a DNS query-based DDoS attack mitigation system using Software-Defined Networking (SDN) to block the network traffic for DDoS attacks. With some features provided by SDN, we can analyze traffic patterns and filter suspicious network flows out. To show the feasibility of the proposed system, we particularly implemented a prototype with Dirichlet process mixture model to distinguish benign traffic from malicious traffic and conducted experiments with the dataset collected from real network traces. We demonstrate the effectiveness of the proposed method by both simulations and experiment data obtained from the real network traffic traces.

Distributed Denial of Service (DDoS) is a sophisticated cyber-attack due to its variety of types and techniques. The traditional mitigation method of this attack is to deploy dedicated security appliances such as firewall, load balancer, etc. However, due to the limited capacity of the hardware and the potential high volume of DDoS traffic, it may not be able to defend all the attacks. Therefore, cloud-based DDoS protection services were introduced to allow the organizations to redirect their traffic to the scrubbing centers in the cloud for filtering. This solution has some drawbacks such as privacy violation and latency. More recently, Network Functions Virtualization (NFV) and edge computing have been proposed as new networking service models. In this paper, we design a framework that leverages NFV and edge computing for DDoS mitigation through two-stage processes.

Software-Defined Networking (SDN) allows for fast reactions to security threats by dynamically enforcing simple forwarding rules as counter-measures. However, in classic SDN all the intelligence resides at the controller, with the switches only capable of performing stateless forwarding as ruled by the controller. It follows that the controller, in addition to network management and control duties, must collect and process any piece of information required to take advanced (stateful) forwarding decisions. This threatens both to overload the controller and to congest the control channel. On the other hand, stateful SDN represents a new concept, developed both to improve reactivity and to offload the controller and the control channel by delegating local treatments to the switches. In this paper, we adopt this stateful paradigm to protect end-hosts from Distributed Denial of Service (DDoS). We propose StateSec, a novel approach based on in-switch processing capabilities to detect and mitigate DDoS attacks. StateSec monitors packets matching configurable traffic features (e.g., IP src/dst, port src/dst) without resorting to the controller. By feeding an entropy-based algorithm with such monitoring features, StateSec detects and mitigates several threats such as (D)DoS and port scans with high accuracy. We implemented StateSec and compared it with a state-of-the-art approach to monitor traffic in SDN. We show that StateSec is more efficient: it achieves very accurate detection levels, limiting at the same time the control plane overhead.

The 911 emergency service belongs to one of the 16 critical infrastructure sectors in the United States. Distributed denial of service (DDoS) attacks launched from a mobile phone botnet pose a significant threat to the availability of this vital service. In this paper we show how attackers can exploit the cellular network protocols in order to launch an anonymized DDoS attack on 911. The current FCC regulations require that all emergency calls be immediately routed regardless of the caller's identifiers (e.g., IMSI and IMEI). A rootkit placed within the baseband firmware of a mobile phone can mask and randomize all cellular identifiers, causing the device to have no genuine identification within the cellular network. Such anonymized phones can issue repeated emergency calls that cannot be blocked by the network or the emergency call centers, technically or legally. We explore the 911 infrastructure and discuss why it is susceptible to this kind of attack. We then implement different forms of the attack and test our implementation on a small cellular network. Finally, we simulate and analyze anonymous attacks on a model of current 911 infrastructure in order to measure the severity of their impact. We found that with less than 6K bots (or \$100K hardware), attackers can block emergency services in an entire state (e.g., North Carolina) for days. We believe that this paper will assist the respective organizations, lawmakers, and security professionals in understanding the scope of this issue in order to prevent possible 911-DDoS attacks in the future.

Recent architectures for the advanced metering infrastructure (AMI) have incorporated several back-end systems that handle billing and other smart grid control operations. The non-availability of metering data when needed or the untimely delivery of data needed for control operations will undermine the activities of these back-end systems. Unfortunately, there are concerns that cyber attacks such as distributed denial of service (DDoS) will manifest in magnitude and complexity in a smart grid AMI network. Such attacks will range from a delay in the availability of end user's metering data to complete denial in the case of a grounded network. This paper proposes a cloud-based (IaaS) firewall for the mitigation of DDoS attacks in a smart grid AMI network. The proposed firewall has the ability of not only mitigating the effects of DDoS attack but can prevent the attack before they are launched. Our proposed firewall system leverages on cloud computing technology which has an added advantage of reducing the burden of data computations and storage for smart grid AMI back-end systems. The openflow firewall proposed in this study is a better security solution with regards to the traditional on-premises DoS solutions which cannot cope with the wide range of new attacks targeting the smart grid AMI network infrastructure. Simulation results generated from the study show that our model can guarantee the availability of metering/control data and could be used to improve the QoS of the smart grid AMI network under a DDoS attack scenario.

Securing Internet of Things (IoT) systems is a challenge because of its multiple points of vulnerability. A spate of recent hacks and security breaches has unveiled glaring vulnerabilities in the IoT. Due to the computational and memory requirement constraints associated with anomaly detection algorithms in core networks, commercial in-line (part of the direct line of communication) Anomaly Detection Systems (ADSs) rely on sampling-based anomaly detection approaches to achieve line rates and truly-inline anomaly detection accuracy in real-time. However, packet sampling is inherently a lossy process which might provide an incomplete and biased approximation of the underlying traffic patterns. Moreover, commercial routers uses proprietary software making them closed to be manipulated from the outside. As a result, detecting malicious packets on the given network path is one of the most challenging problems in the field of network security. We argue that the advent of Software Defined Networking (SDN) provides a unique opportunity to effectively detect and mitigate DDoS attacks. Unlike sampling-based approaches for anomaly detection and limitation of proprietary software at routers, we use the SDN infrastructure to relax the sampling-based ADS constraints and collect traffic flow statistics which are maintained at each SDN-enabled switch to achieve high detection accuracy. In order to implement our idea, we discuss how to mitigate DDoS attacks using the features of SDN infrastructure.

Distributed Denial of Service (DDoS) attack has been bringing serious security concerns on banks, finance incorporation, public institutions, and data centers. Also, the emerging wave of Internet of Things (IoT) raises new concerns on the smart devices. Software Defined Networking (SDN) and Network Functions Virtualization (NFV) have provided a new paradigm for network security. In this paper, we propose a new method to efficiently prevent DDoS attacks, based on a SDN/NFV framework. To resolve the problem that normal packets are blocked due to the inspection on suspicious packets, we developed a threshold-based method that provides a client with an efficient, fast DDoS attack mitigation. In addition, we use open source code to develop the security functions in order to implement our solution for SDN-based network security functions. The source code is based on NETCONF protocol [1] and YANG Data Model [2].

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (\textbackslashtextless;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- \textbackslashtextbar\textbackslashtextbar

Distributed Denial-of-Service (DDoS) attacks have steadily gained in popularity over the last decade, their intensity ranging from mere nuisance to severe. The increased number of attacks, combined with the loss of revenue for the targets, has given rise to a market for DDoS Protection Service (DPS) providers, to whom victims can outsource the cleansing of their traffic by using traffic diversion. In this paper, we investigate the adoption of cloud-based DPSs worldwide. We focus on nine leading providers. Our outlook on adoption is made on the basis of active DNS measurements. We introduce a methodology that allows us, for a given domain name, to determine if traffic diversion to a DPS is in effect. It also allows us to distinguish various methods of traffic diversion and protection. For our analysis we use a long-term, large-scale data set that covers well over 50\textbackslash% of all names in the global domain namespace, in daily snapshots, over a period of 1.5 years. Our results show that DPS adoption has grown by 1.24x in our measurement period, a prominent trend compared to the overall expansion of the namespace. Our study also reveals that adoption is often lead by big players such as large Web hosters, which activate or deactivate DDoS protection for millions of domain names at once.

Distributed Denial of Service attacks against high-profile targets have become more frequent in recent years. In response to such massive attacks, several architectures have adopted proxies to introduce layers of indirection between end users and target services and reduce the impact of a DDoS attack by migrating users to new proxies and shuffling clients across proxies so as to isolate malicious clients. However, the reactive nature of these solutions presents weaknesses that we leveraged to develop a new attack - the proxy harvesting attack - which enables malicious clients to collect information about a large number of proxies before launching a DDoS attack. We show that current solutions are vulnerable to this attack, and propose a moving target defense technique consisting in periodically and proactively replacing one or more proxies and remapping clients to proxies. Our primary goal is to disrupt the attacker's reconnaissance effort. Additionally, to mitigate ongoing attacks, we propose a new client-to-proxy assignment strategy to isolate compromised clients, thereby reducing the impact of attacks. We validate our approach both theoretically and through simulation, and show that the proposed solution can effectively limit the number of proxies an attacker can discover and isolate malicious clients.