Jelena Mirkovic

Bio: Jelena Mirkovic is an academic researcher from Information Sciences Institute. The author has contributed to research in topics: Denial-of-service attack & Computer science. The author has an hindex of 28, co-authored 89 publications receiving 4710 citations. Previous affiliations of Jelena Mirkovic include University of California, Los Angeles & University of Southern California.

A realistic simulation of internet-scale events

Abstract: Internet-scale security incidents are becoming increasingly common, and the researchers need tools to replicate and study them in a controlled setting. Current network simulators, mathematical event models and testbed emulation cannot faithfully replicate events at such a large scale. They either omit or simplify the relevant features of the Internet environment to meet the scale challenge, thus compromising fidelity. We present a distributed worm spread simulator, called PAWS, that builds a realistic Internet model, including the AS-level topology, the limited link bandwidths, and the legitimate traffic patterns. PAWS can support diversity of Internet participants at any desired granularity, because it simulates each vulnerable host individually. Faithful replication of Internet environment, its diversity and its interaction with the simulated event, all lead to a high-fidelity simulation that can be used to study event dynamics and evaluate possible defenses. While PAWS is customized for worm spread simulation, it is a modular large-scale simulator with a realistic Internet model, that can be easily extended to simulate other Internet-scale events.

Evaluation Theory and Practice Applied to Cybersecurity Education

Abstract: As more institutions, organizations, schools, and programs launch cybersecurity education programs in an attempt to meet needs that are emerging in a rapidly changing environment, evaluation will be important to ensure that programs are having the desired impact.

Hiding debuggers from malware with apate

Abstract: Malware analysis uses debuggers to understand and manipulate the behaviors of stripped binaries. To circumvent analysis, malware applies a variety of anti-debugging techniques, such as self-modifying, checking for or removing breakpoints, hijacking keyboard and mouse events, escaping the debugger, etc. Most state-of-the-art debuggers are vulnerable to these anti-debugging techniques. In this paper, we first systematically analyze the spectrum of possible anti-debugging techniques and compile a list of 79 attack vectors. We then propose a framework, called Apate, which detects and defeats each of these attack vectors, by performing: (1) just-in-time disassembling based on single-stepping, (2) careful monitoring of the debuggee's execution and, when needed, modification of the debuggee's states to hide the debugger's presence. We implement Apate as an extension to WinDbg and extensively evaluate it using five different datasets, with known and new malware samples. Apate outperforms other debugger-hiding technologies by a wide margin, addressing 58+--465+ more attack vectors.

Handling Anti-Virtual Machine Techniques in Malicious Software

Abstract: Malware analysis relies heavily on the use of virtual machines (VMs) for functionality and safety. There are subtle differences in operation between virtual and physical machines. Contemporary malware checks for these differences and changes its behavior when it detects a VM presence. These anti-VM techniques hinder malware analysis. Existing research approaches to uncover differences between VMs and physical machines use randomized testing, and thus cannot guarantee completeness.In this article, we propose a detect-and-hide approach, which systematically addresses anti-VM techniques in malware. First, we propose cardinal pill testing—a modification of red pill testing that aims to enumerate the differences between a given VM and a physical machine through carefully designed tests. Cardinal pill testing finds five times more pills by running 15 times fewer tests than red pill testing. We examine the causes of pills and find that, while the majority of them stem from the failure of VMs to follow CPU specifications, a small number stem from under-specification of certain instructions by the Intel manual. This leads to divergent implementations in different CPU and VM architectures. Cardinal pill testing successfully enumerates the differences that stem from the first cause. Finally, we propose VM Cloak—a WinDbg plug-in which hides the presence of VMs from malware. VM Cloak monitors each execute malware command, detects potential pills, and at runtime modifies the command’s outcomes to match those that a physical machine would generate. We implemented VM Cloak and verified that it successfully hides VM presence from malware.

Evaluating Cybersecurity Education Interventions: Three Case Studies

Abstract: The authors collaborate with cybersecurity faculty members from different universities to apply a five-step approach in designing an evaluation for education interventions. The goals of this exercise were to show how to design an evaluation for a real intervention from beginning to end, to highlight the common intervention goals and propose suitable evaluation instruments, and to discuss the expected investment of time and effort in preparing and performing the education evaluations.

A taxonomy of DDoS attack and DDoS defense mechanisms

Abstract: Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space. The attack classification criteria was selected to highlight commonalities and important features of attack strategies, that define challenges and dictate the design of countermeasures. The defense taxonomy classifies the body of existing DDoS defenses based on their design decisions; it then shows how these decisions dictate the advantages and deficiencies of proposed solutions.

Privacy integrated queries: an extensible platform for privacy-preserving data analysis

Abstract: We report on the design and implementation of the Privacy Integrated Queries (PINQ) platform for privacy-preserving data analysis. PINQ provides analysts with a programming interface to unscrubbed data through a SQL-like language. At the same time, the design of PINQ's analysis language and its careful implementation provide formal guarantees of differential privacy for any and all uses of the platform. PINQ's unconditional structural guarantees require no trust placed in the expertise or diligence of the analysts, substantially broadening the scope for design and deployment of privacy-preserving data analysis, especially by non-experts.

Understanding the mirai botnet

Abstract: The Mirai botnet, composed primarily of embedded and IoT devices, took the Internet by storm in late 2016 when it overwhelmed several high-profile targets with massive distributed denial-of-service (DDoS) attacks. In this paper, we provide a seven-month retrospective analysis of Mirai's growth to a peak of 600k infections and a history of its DDoS victims. By combining a variety of measurement perspectives, we analyze how the botnet emerged, what classes of devices were affected, and how Mirai variants evolved and competed for vulnerable hosts. Our measurements serve as a lens into the fragile ecosystem of IoT devices. We argue that Mirai may represent a sea change in the evolutionary development of botnets--the simplicity through which devices were infected and its precipitous growth, demonstrate that novice malicious techniques can compromise enough low-end devices to threaten even some of the best-defended targets. To address this risk, we recommend technical and nontechnical interventions, as well as propose future research directions.

A Survey of Defense Mechanisms Against Distributed Denial of Service (DDoS) Flooding Attacks

Abstract: Distributed Denial of Service (DDoS) flooding attacks are one of the biggest concerns for security professionals. DDoS flooding attacks are typically explicit attempts to disrupt legitimate users' access to services. Attackers usually gain access to a large number of computers by exploiting their vulnerabilities to set up attack armies (i.e., Botnets). Once an attack army has been set up, an attacker can invoke a coordinated, large-scale attack against one or more targets. Developing a comprehensive defense mechanism against identified and anticipated DDoS flooding attacks is a desired goal of the intrusion detection and prevention research community. However, the development of such a mechanism requires a comprehensive understanding of the problem and the techniques that have been used thus far in preventing, detecting, and responding to various DDoS flooding attacks. In this paper, we explore the scope of the DDoS flooding attack problem and attempts to combat it. We categorize the DDoS flooding attacks and classify existing countermeasures based on where and when they prevent, detect, and respond to the DDoS flooding attacks. Moreover, we highlight the need for a comprehensive distributed and collaborative defense approach. Our primary intention for this work is to stimulate the research community into developing creative, effective, efficient, and comprehensive prevention, detection, and response mechanisms that address the DDoS flooding problem before, during and after an actual attack.