Biblio

An often overlooked but equally important aspect of unmanned aerial system (UAS) design is the security of their networking protocols and how they deal with cyberattacks. In this context, cyberattacks are malicious attempts to monitor or modify incoming and outgoing data from the system. These attacks could target anywhere in the system where a transfer of data occurs but are most common in the transfer of data between the control station and the UAS. A compromise in the networking system of a UAS could result in a variety of issues including increased network latency between the control station and the UAS, temporary loss of control over the UAS, or a complete loss of the UAS. A complete loss of the system could result in the UAS being disabled, crashing, or the attacker overtaking command and control of the platform, all of which would be done with little to no alert to the operator. Fortunately, the majority of higher-end, enterprise, and government UAS platforms are aware of these threats and take actions to mitigate them. However, as the consumer market continues to grow and prices continue to drop, network security may be overlooked or ignored in favor of producing the lowest cost product possible. Additionally, these commercial off-the-shelf UAS often use uniform, standardized frequency bands, autopilots, and security measures, meaning a cyberattack could be developed to affect a wide variety of models with minimal changes. This paper will focus on a low-cost educational-use UAS and test its resilience to a variety of cyberattack methods, including man-in-the-middle attacks, spoofing of data, and distributed denial-of-service attacks. Following this experiment will be a discussion of current cybersecurity practices for counteracting these attacks and how they can be applied onboard a UAS. Although in this case the cyberattacks were tested against a simpler platform, the methods discussed are applicable to any UAS platform attempting to defend against such cyberattack methods.

In the ever evolving Internet threat landscape, Distributed Denial-of-Service (DDoS) attacks remain a popular means to invoke service disruption. DDoS attacks, however, have evolved to become a tool of deceit, providing a smokescreen or distraction while some other underlying attack takes place, such as data exfiltration. Knowing the intent of a DDoS, and detecting underlying attacks which may be present concurrently with it, is a challenging problem. An entity whose network is under a DDoS attack may not have the support personnel to both actively fight a DDoS and try to mitigate underlying attacks. Therefore, any system that can detect such underlying attacks should do so only with a high degree of confidence. Previous work utilizing flow aggregation techniques with multi-class anomaly detection showed promise in both DDoS detection and detecting underlying attacks ongoing during an active DDoS attack. In this work, we head in the opposite direction, utilizing flow segmentation and concurrent flow feature aggregation, with the primary goal of greatly reduced detection times of both DDoS and underlying attacks. Using the same multi-class anomaly detection approach, we show greatly improved detection times with promising detection performance.

Recently Distributed Denial-of-Service (DDoS) are becoming more and more sophisticated, which makes the existing defence systems not capable of tolerating by themselves against wide-ranging attacks. Thus, collaborative protection mitigation has become a needed alternative to extend defence mechanisms. However, the existing coordinated DDoS mitigation approaches either they require a complex configuration or are highly-priced. Blockchain technology offers a solution that reduces the complexity of signalling DDoS system, as well as a platform where many autonomous systems (Ass) can share hardware resources and defence capabilities for an effective DDoS defence. In this work, we also used a Deep learning DDoS detection system; we identify individual DDoS attack class and also define whether the incoming traffic is legitimate or attack. By classifying the attack traffic flow separately, our proposed mitigation technique could deny only the specific traffic causing the attack, instead of blocking all the traffic coming towards the victim(s).

With the recent advances in software-defined networking (SDN), the multi-tenant data centers provide more efficient and flexible cloud platform to their subscribers. However, as the number, scale, and diversity of distributed denial-of-service (DDoS) attack is dramatically escalated in recent years, the availability of those platforms is still under risk. We note that the state-of-art DDoS protection architectures did not fully utilize the potential of SDN and network function virtualization (NFV) to mitigate the impact of attack traffic on data center network. Therefore, in this paper, we exploit the flexibility of SDN and NFV to propose FlexProtect, a flexible distributed DDoS protection architecture for multi-tenant data centers. In FlexProtect, the detection virtual network functions (VNFs) are placed near the service provider and the defense VNFs are placed near the edge routers for effectively detection and avoid internal bandwidth consumption, respectively. Based on the architecture, we then propose FP-SYN, an anti-spoofing SYN flood protection mechanism. The emulation and simulation results with real-world data demonstrates that, compared with the traditional approach, the proposed architecture can significantly reduce 46% of the additional routing path and save 60% internal bandwidth consumption. Moreover, the proposed detection mechanism for anti-spoofing can achieve 98% accuracy.

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.

Distributed Denial-of-Service (DDoS) attacks continue to be a major threat on the Internet today. DDoS attacks overwhelm target services with requests or other traffic, causing requests from legitimate users to be shut out. A common defense against DDoS is to replicate a service in multiple physical locations/sites. If all sites announce a common prefix, BGP will associate users around the Internet with a nearby site, defining the catchment of that site. Anycast defends against DDoS both by increasing aggregate capacity across many sites, and allowing each site's catchment to contain attack traffic, leaving other sites unaffected. IP anycast is widely used by commercial CDNs and for essential infrastructure such as DNS, but there is little evaluation of anycast under stress. This paper provides the first evaluation of several IP anycast services under stress with public data. Our subject is the Internet's Root Domain Name Service, made up of 13 independently designed services ("letters", 11 with IP anycast) running at more than 500 sites. Many of these services were stressed by sustained traffic at 100× normal load on Nov. 30 and Dec. 1, 2015. We use public data for most of our analysis to examine how different services respond to stress, and identify two policies: sites may absorb attack traffic, containing the damage but reducing service to some users, or they may withdraw routes to shift both good and bad traffic to other sites. We study how these deployment policies resulted in different levels of service to different users during the events. We also show evidence of collateral damage on other services located near the attacks.

Volume anomaly such as distributed denial-of-service (DDoS) has been around for ages but with advancement in technologies, they have become stronger, shorter and weapon of choice for attackers. Digital forensic analysis of intrusions using alerts generated by existing intrusion detection system (IDS) faces major challenges, especially for IDS deployed in large networks. In this paper, the concept of automatically sifting through a huge volume of alerts to distinguish the different stages of a DDoS attack is developed. The proposed novel framework is purpose-built to analyze multiple logs from the network for proactive forecast and timely detection of DDoS attacks, through a combined approach of Shannon-entropy concept and clustering algorithm of relevant feature variables. Experimental studies on a cyber-range simulation dataset from the project industrial partners show that the technique is able to distinguish precursor alerts for DDoS attacks, as well as the attack itself with a very low false positive rate (FPR) of 22.5%. Application of this technique greatly assists security experts in network analysis to combat DDoS attacks.

In our previous work [1], we presented a study of using performance escalation to automatic detect Distributed Denial of Service (DDoS) types of attacks. We propose to enhance the work of security threat detection by using mobile phones as the detector to identify outliers of normal traffic patterns as threats. The mobile solution makes detection portable to any services. This paper also shows that the same detection method works for advanced persistent threats.