A Macroscopic Study of Network Security Threats at the Organizational Level.

Defenders of today's network are confronted with a large number of malicious activities such as spam, malware, and denial-of-service attacks. Although many studies have been performed on how to mitigate security threats, the interaction between attackers and defenders is like a game of Whac-a-Mole, in which the security community is chasing after attackers rather than helping defenders to build systematic defensive solutions. As a complement to these studies that focus on attackers or end hosts, this thesis studies security threats from the perspective of the organization, the central authority that manages and defends a group of end hosts. This perspective provides a balanced position to understand security problems and to deploy and evaluate defensive solutions. This thesis explores how a macroscopic view of network security from an organization's perspective can be formed to help measure, understand, and mitigate security threats. To realize this goal, we bring together a broad collection of reputation blacklists. We first measure the properties of the malicious sources identified by these blacklists and their impact on an organization. We then aggregate the malicious sources to Internet organizations and characterize the maliciousness of organizations and their evolution over a period of two and half years. Next, we aim to understand the cause of different maliciousness levels in different organizations. By examining the relationship between eight security mismanagement symptoms and the maliciousness of organizations, we find a strong positive correlation between mismanagement and maliciousness. Lastly, motivated by the observation that there are organizations that have a significant fraction of their IP addresses involved in malicious activities, we evaluate the tradeoff of one type of mitigation solution at the organization level --- network takedowns.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116714/1/jingzj_1.pd

Common Mechanism for Detecting Multiple DDoS Attacks

An important principle of an internet-based system is information security. Information security is a very important aspect of distributed systems and IoT (Internet of Things) based wireless systems. The attack which is more harmful to the distributed system and IoT-based wireless system is a DDoS (Distributed Denial of Service) attack since in this attack, an attacker can stop the work of all other connected devices or users to the network. For securing distributed applications, various intrusion detection mechanisms are used. But most existing mechanisms are only concentrated on one kind of DDoS attack. This paper focuses on the basic architecture of IoT systems and an overview of single intrusion detection systems. This paper presents a single detection method for different DDoS attacks on distributed systems with an IoT interface. In the future, the system will provide support for detecting and preventing different DDoS attacks in IoT-based systems

Development of protection mechanisms against DRDoS-attacks and combined DRDoS-attacks

Distributed “denial of service” attacks based on the traffic reflection and amplification (DRDoS attacks) still are a powerful threat for computer networks. More than half of all attacks were executed by using multiple types of attacks. Development of new protection mechanisms against such attacks is one of the most important tasks in the field of computer security. In this paper, we present experiments on DNS attack, NTP attacks and combined DRDoS-attack simulation. We simulated several protection mechanisms as well as a mechanism developed by us. We compared these protection mechanisms for different kinds of attacks

DDoS Mitigation:A Measurement-Based Approach

Society heavily relies upon the Internet for global communications. Simultaneously, Internet stability and reliability are continuously subject to deliberate threats. These threats include (Distributed) Denial-of-Service (DDoS) attacks, which can potentially be devastating. As a result of DDoS, businesses lose hundreds of millions of dollars annually. Moreover, when it comes to vital infrastructure, national safety and even lives could be at stake. Effective defenses are therefore an absolute necessity. Prospective users of readily available mitigation solutions find themselves having many shapes and sizes to choose from, the right fit of which may, however, not always be apparent. In addition, the deployment and operation of mitigation solutions may come with hidden hazards that need to be better understood. Policy makers and governments also find themselves facing questions concerning what needs to be done to promote cybersafety on a national level. Developing an optimal course of action to deal with DDoS, therefore, also brings about societal challenges. Even though the DDoS problem is by no means new, the scale of the problem is still unclear. We do not know exactly what it is we are defending against and getting a better understanding of attacks is essential to addressing the problem head-on. To advance situational awareness, many technical and societal challenges need still to be tackled. Given the central importance of better understanding the DDoS problem to improve overall Internet security, the thesis that we summarize in this paper has three main contributions. First, we rigorously characterize attacks and attacked targets at scale. Second, we advance knowledge about the Internet-wide adoption, deployment and operational use of various mitigation solutions. Finally, we investigate hidden hazards that can render mitigation solutions altogether ineffective

A Survey on Enterprise Network Security: Asset Behavioral Monitoring and Distributed Attack Detection

Enterprise networks that host valuable assets and services are popular and frequent targets of distributed network attacks. In order to cope with the ever-increasing threats, industrial and research communities develop systems and methods to monitor the behaviors of their assets and protect them from critical attacks. In this paper, we systematically survey related research articles and industrial systems to highlight the current status of this arms race in enterprise network security. First, we discuss the taxonomy of distributed network attacks on enterprise assets, including distributed denial-of-service (DDoS) and reconnaissance attacks. Second, we review existing methods in monitoring and classifying network behavior of enterprise hosts to verify their benign activities and isolate potential anomalies. Third, state-of-the-art detection methods for distributed network attacks sourced from external attackers are elaborated, highlighting their merits and bottlenecks. Fourth, as programmable networks and machine learning (ML) techniques are increasingly becoming adopted by the community, their current applications in network security are discussed. Finally, we highlight several research gaps on enterprise network security to inspire future research.Comment: Journal paper submitted to Elseive

Using honeypots to trace back amplification DDoS attacks

In today’s interconnected world, Denial-of-Service attacks can cause great harm by simply rendering a target system or service inaccessible. Amongst the most powerful and widespread DoS attacks are amplification attacks, in which thousands of vulnerable servers are tricked into reflecting and amplifying attack traffic. However, as these attacks inherently rely on IP spoofing, the true attack source is hidden. Consequently, going after the offenders behind these attacks has so far been deemed impractical. This thesis presents a line of work that enables practical attack traceback supported by honeypot reflectors. To this end, we investigate the tradeoffs between applicability, required a priori knowledge, and traceback granularity in three settings. First, we show how spoofed attack packets and non-spoofed scan packets can be linked using honeypot-induced fingerprints, which allows attributing attacks launched from the same infrastructures as scans. Second, we present a classifier-based approach to trace back attacks launched from booter services after collecting ground-truth data through self-attacks. Third, we propose to use BGP poisoning to locate the attacking network without prior knowledge and even when attack and scan infrastructures are disjoint. Finally, as all of our approaches rely on honeypot reflectors, we introduce an automated end-to-end pipeline to systematically find amplification vulnerabilities and synthesize corresponding honeypots.In der heutigen vernetzten Welt können Denial-of-Service-Angriffe große Schäden verursachen, einfach indem sie ihr Zielsystem unerreichbar machen. Zu den stärksten und verbreitetsten DoS-Angriffen zählen Amplification-Angriffe, bei denen tausende verwundbarer Server missbraucht werden, um Angriffsverkehr zu reflektieren und zu verstärken. Da solche Angriffe jedoch zwingend gefälschte IP-Absenderadressen nutzen, ist die wahre Angriffsquelle verdeckt. Damit gilt die Verfolgung der Täter bislang als unpraktikabel. Diese Dissertation präsentiert eine Reihe von Arbeiten, die praktikable Angriffsrückverfolgung durch den Einsatz von Honeypots ermöglicht. Dazu untersuchen wir das Spannungsfeld zwischen Anwendbarkeit, benötigtem Vorwissen, und Rückverfolgungsgranularität in drei Szenarien. Zuerst zeigen wir, wie gefälschte Angriffs- und ungefälschte Scan-Datenpakete miteinander verknüpft werden können. Dies ermöglicht uns die Rückverfolgung von Angriffen, die ebenfalls von Scan-Infrastrukturen aus durchgeführt wurden. Zweitens präsentieren wir einen Klassifikator-basierten Ansatz um Angriffe durch Booter-Services mittels vorher durch Selbstangriffe gesammelter Daten zurückzuverfolgen. Drittens zeigen wir auf, wie BGP Poisoning genutzt werden kann, um ohne weiteres Vorwissen das angreifende Netzwerk zu ermitteln. Schließlich präsentieren wir einen automatisierten Prozess, um systematisch Schwachstellen zu finden und entsprechende Honeypots zu synthetisieren

DDoS cyber-incident detection in smart grids

The smart grid (SG) offers potential benefits for utilities, electric generators, and customers alike. However, the prevalence of cyber-attacks targeting the SG emphasizes its dark side. In particular, distributed denial-of-service (DDoS) attacks can affect the communication of different devices, interrupting the SG’s operation. This could have profound implications for the power system, including area blackouts. The problem is that few operational technology tools provide reflective DDoS protection. Furthermore, such tools often fail to classify the types of attacks that have occurred. Defensive capabilities are necessary to identify the footprints of attacks in a timely manner, as they occur, and to make these systems sustainable for delivery of the services as expected. To meet this need for defensive capabilities, we developed a situational awareness tool to detect system compromise by monitoring the indicators of compromise (IOCs) of amplification DDoS attacks. We achieved this aim by finding IOCs and exploring attack footprints to understand the nature of such attacks and their cyber behavior. Finally, an evaluation of our approach against a real dataset of DDoS attack instances indicated that our tool can distinguish and detect different types of amplification DDoS attacks

A composable approach to design of newer techniques for large-scale denial-of-service attack attribution

Since its early days, the Internet has witnessed not only a phenomenal growth, but also a large number of security attacks, and in recent years, denial-of-service (DoS) attacks have emerged as one of the top threats. The stateless and destination-oriented Internet routing combined with the ability to harness a large number of compromised machines and the relative ease and low costs of launching such attacks has made this a hard problem to address. Additionally, the myriad requirements of scalability, incremental deployment, adequate user privacy protections, and appropriate economic incentives has further complicated the design of DDoS defense mechanisms. While the many research proposals to date have focussed differently on prevention, mitigation, or traceback of DDoS attacks, the lack of a comprehensive approach satisfying the different design criteria for successful attack attribution is indeed disturbing. Our first contribution here has been the design of a composable data model that has helped us represent the various dimensions of the attack attribution problem, particularly the performance attributes of accuracy, effectiveness, speed and overhead, as orthogonal and mutually independent design considerations. We have then designed custom optimizations along each of these dimensions, and have further integrated them into a single composite model, to provide strong performance guarantees. Thus, the proposed model has given us a single framework that can not only address the individual shortcomings of the various known attack attribution techniques, but also provide a more wholesome counter-measure against DDoS attacks. Our second contribution here has been a concrete implementation based on the proposed composable data model, having adopted a graph-theoretic approach to identify and subsequently stitch together individual edge fragments in the Internet graph to reveal the true routing path of any network data packet. The proposed approach has been analyzed through theoretical and experimental evaluation across multiple metrics, including scalability, incremental deployment, speed and efficiency of the distributed algorithm, and finally the total overhead associated with its deployment. We have thereby shown that it is realistically feasible to provide strong performance and scalability guarantees for Internet-wide attack attribution. Our third contribution here has further advanced the state of the art by directly identifying individual path fragments in the Internet graph, having adopted a distributed divide-and-conquer approach employing simple recurrence relations as individual building blocks. A detailed analysis of the proposed approach on real-life Internet topologies with respect to network storage and traffic overhead, has provided a more realistic characterization. Thus, not only does the proposed approach lend well for simplified operations at scale but can also provide robust network-wide performance and security guarantees for Internet-wide attack attribution. Our final contribution here has introduced the notion of anonymity in the overall attack attribution process to significantly broaden its scope. The highly invasive nature of wide-spread data gathering for network traceback continues to violate one of the key principles of Internet use today - the ability to stay anonymous and operate freely without retribution. In this regard, we have successfully reconciled these mutually divergent requirements to make it not only economically feasible and politically viable but also socially acceptable. This work opens up several directions for future research - analysis of existing attack attribution techniques to identify further scope for improvements, incorporation of newer attributes into the design framework of the composable data model abstraction, and finally design of newer attack attribution techniques that comprehensively integrate the various attack prevention, mitigation and traceback techniques in an efficient manner