Biblio

A slow-paced persistent attack, such as slow worm or bot, can bewilder the detection system by slowing down their attack. Detecting such attacks based on traditional anomaly detection techniques may yield high false alarm rates. In this paper, we frame our problem as detecting slow-paced persistent attacks from a time series obtained from network trace. We focus on time series spectrum analysis to identify peculiar spectral patterns that may represent the occurrence of a persistent activity in the time domain. We propose a method to adaptively detect slow-paced persistent attacks in a time series and evaluate the proposed method by conducting experiments using both synthesized traffic and real-world traffic. The results show that the proposed method is capable of detecting slow-paced persistent attacks even in a noisy environment mixed with legitimate traffic.

In this article, researcher collaboration patterns and research topics on Intelligence and Security Informatics (ISI) are investigated using social network analysis approaches. The collaboration networks exhibit scale-free property and small-world effect. From these networks, the authors obtain the key researchers, institutions, and three important topics.

Online social networks are attracting billions of nowadays, both on a global scale as well as in social enterprise networks. Using distributed hash tables and peer-to-peer technology allows online social networks to be operated securely and efficiently only by using the resources of the user devices, thus alleviating censorship or data misuse by a single network operator. In this paper, we address the challenges that arise in implementing reliably and conveniently to use distributed data structures, such as lists or sets, in such a distributed hash-table-based online social network. We present a secure, distributed list data structure that manages the list entries in several buckets in the distributed hash table. The list entries are authenticated, integrity is maintained and access control for single users and also groups is integrated. The approach for secure distributed lists is also applied for prefix trees and sets, and implemented and evaluated in a peer-to-peer framework for social networks. Evaluation shows that the distributed data structure is convenient and efficient to use and that the requirements on security hold.

The longstanding debate on a fundamental science of security has led to advances in systems, software, and network security. However, existing efforts have done little to inform how an environment should react to emerging and ongoing threats and compromises. The authors explore the goals and structures of a new science of cyber-decision-making in the Cyber-Security Collaborative Research Alliance, which seeks to develop a fundamental theory for reasoning under uncertainty the best possible action in a given cyber environment. They also explore the needs and limitations of detection mechanisms; agile systems; and the users, adversaries, and defenders that use and exploit them, and conclude by considering how environmental security can be cast as a continuous optimization problem.

Today’s quality of life is highly dependent on the successful operation of many large-scale industrial control systems. To enhance their protection against cyber-attacks and operational errors, we develop a simulation-based verification framework with cross-layer verification techniques that allow comprehensive analysis of the entire ICS-specific stack, including application, protocol, and network layers.

In SDN, the underlying infrastructure is usually abstracted for applications that can treat the network as a logical or virtual entity. Commonly, the “mappings” between virtual abstractions and their actual physical implementations are not one-to-one, e.g., a single “big switch” abstract object might be implemented using a distributed set of physical devices. A key question is, what abstractions could be mapped to multiple physical elements while faithfully preserving their native semantics? E.g., can an application developer always expect her abstract “big switch” to act exactly as a physical big switch, despite being implemented using multiple physical switches in reality? We show that the answer to that question is “no” for existing virtual-to-physical mapping techniques: behavior can differ between the virtual “big switch” and the physical network, providing incorrect application-level behavior.

We also show that that those incorrect behaviors occur despite the fact that the most pervasive correctness invariants, such as per-packet consistency, are preserved throughout. These examples demonstrate that for practical notions of correctness, new systems and a new analytical framework are needed. We take the first steps by defining end-to-end correctness, a correctness condition that focuses on applications only, and outline a research vision to obtain virtualization systems with correct virtual to physical mappings.

In network intrusion detection research, one popular strategy for finding attacks is monitoring a network's activity for anomalies: deviations from profiles of normality previously learned from benign traffic, typically identified using tools borrowed from the machine learning community. However, despite extensive academic research one finds a striking gap in terms of actual deployments of such systems: compared with other intrusion detection approaches, machine learning is rarely employed in operational "real world" settings. We examine the differences between the network intrusion detection problem and other areas where machine learning regularly finds much more success. Our main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively. We support this claim by identifying challenges particular to network intrusion detection, and provide a set of guidelines meant to strengthen future research on anomaly detection.

Security issues in computer networks have focused on attacks on end systems and the control plane. An entirely new class of emerging network attacks aims at the data plane of the network. Data plane forwarding in network routers has traditionally been implemented with custom-logic hardware, but recent router designs increasingly use software-programmable network processors for packet forwarding. These general-purpose processing devices exhibit software vulnerabilities and are susceptible to attacks. We demonstrate-to our knowledge the first-practical attack that exploits a vulnerability in packet processing software to launch a devastating denial-of-service attack from within the network infrastructure. This attack uses only a single attack packet to consume the full link bandwidth of the router's outgoing link. We also present a hardware-based defense mechanism that can detect situations where malicious packets try to change the operation of the network processor. Using a hardware monitor, our NetFPGA-based prototype system checks every instruction executed by the network processor and can detect deviations from correct processing within four clock cycles. A recovery system can restore the network processor to a safe state within six cycles. This high-speed detection and recovery system can ensure that network processors can be protected effectively and efficiently from this new class of attacks.

As mobile technology begins to dominate computing, understanding how their use impacts security becomes increasingly important. Fortunately, this challenge is also an opportunity: the rich set of sensors with which most mobile devices are equipped provide a rich contextual dataset, one that should enable mobile user behavior to be modeled well enough to predict when users are likely to act insecurely, and provide cognitively grounded explanations of those behaviors. We will evaluate this hypothesis with a series of experiments designed first to confirm that mobile sensor data can reliably predict user stress, and that users experiencing such stress are more likely to act insecurely.

One of the biggest challenges in mobile security is human behavior. The most secure password may be useless if it is sent as a text or in an email. The most secure network is only as secure as its most careless user. Thus, in the current project we sought to discover the conditions under which users of mobile devices were most likely to make security errors. This scaffolds a larger project where we will develop automatic ways of detecting such environments and eventually supporting users during these times to encourage safe mobile behaviors.

The Symposium and Bootcamp on the Science of Security (HotSoS), is a research event centered on the Science of Security (SoS). Following a successful invitational SoS Community Meeting in December 2012, HotSoS 2014 was the first open research event in what we expect will be a continuing series of such events. The key motivation behind developing a Science of Security is to address the fundamental problems of cybersecurity in a principled manner. Security has been intensively studied, but a lot of previous research emphasizes the engineering of specific solutions without first developing the scientific understanding of the problem domain. All too often, security research conveys the flavor of identifying specific threats and removing them in an apparently ad hoc manner. The motivation behind the nascent Science of Security is to understand how computing systems are architected, built, used, and maintained with a view to understanding and addressing security challenges systematically across their life cycle. In particular, two features distinguish the Science of Security from previous research programs on cybersecurity. Scope. The Science of Security considers not just computational artifacts but also incorporates the human, social, and organizational aspects of computing within its purview. Approach. The Science of Security takes a decidedly scientific approach, based on the understanding of empirical evaluation and theoretical foundations as developed in the natural and social sciences, but adapted as appropriate for the "artificial science" (paraphrasing Herb Simon's term) that is computing.