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Cross-core Microarchitectural Attacks and CountermeasuresIrazoki, Gorka 24 April 2017 (has links)
In the last decade, multi-threaded systems and resource sharing have brought a number of technologies that facilitate our daily tasks in a way we never imagined. Among others, cloud computing has emerged to offer us powerful computational resources without having to physically acquire and install them, while smartphones have almost acquired the same importance desktop computers had a decade ago. This has only been possible thanks to the ever evolving performance optimization improvements made to modern microarchitectures that efficiently manage concurrent usage of hardware resources. One of the aforementioned optimizations is the usage of shared Last Level Caches (LLCs) to balance different CPU core loads and to maintain coherency between shared memory blocks utilized by different cores. The latter for instance has enabled concurrent execution of several processes in low RAM devices such as smartphones. Although efficient hardware resource sharing has become the de-facto model for several modern technologies, it also poses a major concern with respect to security. Some of the concurrently executed co-resident processes might in fact be malicious and try to take advantage of hardware proximity. New technologies usually claim to be secure by implementing sandboxing techniques and executing processes in isolated software environments, called Virtual Machines (VMs). However, the design of these isolated environments aims at preventing pure software- based attacks and usually does not consider hardware leakages. In fact, the malicious utilization of hardware resources as covert channels might have severe consequences to the privacy of the customers. Our work demonstrates that malicious customers of such technologies can utilize the LLC as the covert channel to obtain sensitive information from a co-resident victim. We show that the LLC is an attractive resource to be targeted by attackers, as it offers high resolution and, unlike previous microarchitectural attacks, does not require core-colocation. Particularly concerning are the cases in which cryptography is compromised, as it is the main component of every security solution. In this sense, the presented work does not only introduce three attack variants that can be applicable in different scenarios, but also demonstrates the ability to recover cryptographic keys (e.g. AES and RSA) and TLS session messages across VMs, bypassing sandboxing techniques. Finally, two countermeasures to prevent microarchitectural attacks in general and LLC attacks in particular from retrieving fine- grain information are presented. Unlike previously proposed countermeasures, ours do not add permanent overheads in the system but can be utilized as preemptive defenses. The first identifies leakages in cryptographic software that can potentially lead to key extraction, and thus, can be utilized by cryptographic code designers to ensure the sanity of their libraries before deployment. The second detects microarchitectural attacks embedded into innocent-looking binaries, preventing them from being posted in official application repositories that usually have the full trust of the customer.
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Changes in rodent hypothalamic cholecystokinin receptors and associated peptide expression in acute and chronic stressHinks, Gillian Leslie January 1994 (has links)
No description available.
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Bistatic sonar and a novel form of variable depth sonar : sonar systems research studyGrimley, W. K. January 1996 (has links)
No description available.
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Role of patients' perceptions of illness and treatment in myocardial infarctionJames, Delyth Higman January 1999 (has links)
No description available.
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Understanding the Phishing EcosystemLe Page, Sophie 08 July 2019 (has links)
In “phishing attacks”, phishing websites mimic trustworthy websites in order to steal sensitive information from end-users. Despite research by both academia and the industry focusing on development of anti-phishing detection techniques, phishing has increasingly become an online threat. Our inability to slow down phishing attacks shows that we need to go beyond detection and focus more on understanding the phishing ecosystem. In this thesis, we contribute in three ways to understand the phishing ecosystem and to offer insight for future anti-phishing efforts. First, we provide a new and comparative study on the life cycle of phishing and malware attacks. Specifically, we use public click-through statistics of the Bitly URL shortening service to analyze the click-through rate and timespan of phishing and malware attacks before (and after) they were reported. We find that the efforts against phishing attacks are stronger than those against malware attacks.We also find phishing activity indicating that mitigation strategies are not taking down phishing websites fast enough. Second, we develop a method that finds similarities between the DOMs of phishing attacks, since it is known that phishing attacks are variations of previous attacks. We find that existing methods do not capture the structure of the DOM, and question whether they are failing to catch some of the similar attacks. We accordingly evaluate the feasibility of applying Pawlik and Augsten’s recent implementation of Tree Edit Distance (AP-TED)calculations as a way to compare DOMs and identify similar phishing attack instances.Our method agrees with existing ones that 94% of our phishing database are replicas. It also better discriminates the similarities, but at a higher computational cost. The high agreement between methods strengthens the understanding that most phishing attacks are variations, which affects future anti-phishing strategies.Third, we develop a domain classifier exploiting the history and internet presence of a domain with machine learning techniques. It uses only publicly available information to determine whether a known phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered. This is especially relevant due to the recent adoption of the General Data Protection Regulation (GDPR), which prevents certain registration information to be made publicly available. Our classifier achieves 94% accuracy on future malicious domains,while maintaining 88% and 92% accuracy on malicious and compromised datasets respectively from two other sources. Accurate domain classification offers insight with regard to different take-down strategies, and with regard to registrars’ prevention of fraudulent registrations.
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Effective Cost Allocation for Deterrence of TerroristsLee Quan, Eugene 01 May 2007 (has links)
The attacks on the World Trade Center in New York, the subway and bus bombings in London, and the suicide bombings in Casablanca are only a few of the examples in which in recent years, terrorists have opted to attack multiple targets at once. Often, their strong determination to attack makes it impossible to completely deter terrorists from attacking altogether, and instead, counterterrorist units must consider how to defend targets effectively to minimize damages. We attempt to model a version of this scenario by presenting a two target sequential game where two players try to attack and defend the targets respectively. The probability of successfully destroying a target is a function of resource allocations from both players, who are also subject to budget constraints. We attempt to find the defender’s strategy that will minimize expected damages by first exploring the attacker’s optimal strategy. We show that the attacker’s decision to attack only one or both targets is dependent on the size of the attacker’s allowed budget relative to other game parameters, and use that information to evaluate the defender’s strategy. We also numerically determine the optimal defender security investment, as well its sensitivity to other game parameters. We conjecture that as the damage and expected reward at a target increases, the defender’s allocation towards that target tends to increase, while an increase in the punishment results in the opposite effect. Such conjectures allow for the creation of a flexible defense policy in the more applicable bigger picture.
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Panic and self states an ethnomethodological approach /Mahoney, Donna M. January 2000 (has links) (PDF)
Dissertation (Ph.D.) -- The Institute for Clinical Social Work, 2000. / A dissertation submitted to the faculty of the Institute of Clinical Social Work in partial fulfillment for the degree of Doctor of Philosophy.
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On countermeasures of worm attacks over the InternetYu, Wei 15 May 2009 (has links)
Worm attacks have always been considered dangerous threats to the Internet since they can
infect a large number of computers and consequently cause large-scale service disruptions and
damage. Thus, research on modeling worm attacks, and defenses against them, have become
vital to the field of computer and network security. This dissertation intends to systematically
study two classes of countermeasures against worm attacks, known as traffic-based
countermeasure and non-traffic based countermeasure. Traffic-based countermeasures are those
whose means are limited to monitoring, collecting, and analyzing the traffic generated by worm
attacks. Non-traffic based countermeasures do not have such limitations.
For the traffic-based countermeasures, we first consider the worm attack that adopts feedback
loop-control mechanisms which make its overall propagation traffic behavior similar to
background non-worm traffic and circumvent the detection. We also develop a novel spectrumbased
scheme to achieve highly effective detection performance against such attacks. We then
consider worm attacks that perform probing traffic in a stealthy manner to obtain the location infrastructure of a defense system and introduce an information-theoretic based framework to
obtain the limitations of such attacks and develop corresponding countermeasures.
For the non-traffic based countermeasures, we first consider new unseen worm attacks and
develop the countermeasure based on mining the dynamic signature of worm programs’ run-time
execution. We then consider a generic worm attack that dynamically changes its propagation
patterns and develops integrated countermeasures based on the attacker’s contradicted
objectives. Lastly, we consider the real-world system setting with multiple incoming worm
attacks that collaborate by sharing the history of their interactions with the defender and develop
a generic countermeasure based on establishing the defender’s reputation of toughness in its
repeated interactions with multiple incoming attackers to optimize the long-term defense
performance.
This dissertation research has broad impacts on Internet worm research since this work is
fundamental, practical and extensible. Our developed framework can be used by researchers to
understand key features of other forms of new worm attacks and develop countermeasures
against them.
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Color Image Watermarking Technique Robust to Geometric attacks and Signal ProcessingLin, Chih-hsueh 10 July 2006 (has links)
Developing a robust method of image watermarking that resists rotation, scaling and/or translation (RST) transformations is widely considered to be a more challenging task than developing a method resistant to other attacks. Altering the orientation or size of the image, even only slightly, reduces the receiver¡¦s ability to retrieve the watermark. Protecting against both geometric distortion and signal processing with blind detection is even more problematic. This investigation proposes a novel approach, based on the properties of histograms to measure the numerous global features of all pixels in a cover image and to construct the three-dimensional feature space. The feature space is dynamically partitioned to identify several blocks used to embed the watermark. One feature of the pixels is modified to form a specially distributed histogram for embedding the watermark in a blind digital watermarking method that can be applied to color images. Experimental results demonstrate the robustness of the proposed method against common geometric attacks and signal processing.
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On traffic analysis attacks and countermeasuresFu, Xinwen 25 April 2007 (has links)
Security and privacy have gained more and more attention with the rapid growth and
public acceptance of the Internet as a means of communication and information
dissemination. Security and privacy of a computing or network system may be
compromised by a variety of well-crafted attacks.
In this dissertation, we address issues related to security and privacy in computer
network systems. Specifically, we model and analyze a special group of network attacks,
known as traffic analysis attacks, and develop and evaluate their countermeasures.
Traffic analysis attacks aim to derive critical information by analyzing traffic over a
network. We focus our study on two classes of traffic analysis attacks: link-load analysis
attacks and flow-connectivity analysis attacks.
Our research has made the following conclusions:
1. We have found that an adversary may effectively discover link load by passively
analyzing selected statistics of packet inter-arrival times of traffic flows on a
network link. This is true even if some commonly used countermeasures (e.g.,
link padding) have been deployed. We proposed an alternative effective countermeasure to counter this passive traffic analysis attack. Our extensive
experimental results indicated this to be an effective approach.
2. Our newly proposed countermeasure may not be effective against active traffic
analysis attacks, which an adversary may also use to discover the link load. We
developed methodologies in countering these kinds of active attacks.
3. To detect the connectivity of a flow, an adversary may embed a recognizable
pattern of marks into traffic flows by interference. We have proposed new
countermeasures based on the digital filtering technology. Experimental results
have demonstrated the effectiveness of our method.
From our research, it is obvious that traffic analysis attacks present a serious
challenge to the design of a secured computer network system. It is the objective of this
study to develop robust but cost-effective solutions to counter link-load analysis attacks
and flow-connectivity analysis attacks. It is our belief that our methodology can provide
a solid foundation for studying the entire spectrum of traffic analysis attacks and their
countermeasures.
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