waashk/dblp huggingface dataset:
design of the eros trusted window system. window systems are the primary mediator of user input and output in modern computing systems. they are also a commonly used interprocess communication mechanism. as a result, they play a key role in the enforcement of security policies and the protection of sensitive information. a user typing a password or passphrase must be assured that it is disclosed exclusively to the intended program. in highly secure systems, global policies concerning information flow restrictions must be honored. most window systems today, including x11 and microsoft windows, have carried forward the presumptive trust assumptions of the xerox alto from which they were conceptually derived. these assumptions are inappropriate for modern computing environments. in this paper, we present the design of a new trusted window system for the eros capability-based operating system. the eros window system (ews) provides robust traceability of user volition and is capable (with extension) of enforcing mandatory access controls. the entire implementation of ews is less than 4,500 lines, which is a factor of ten smaller than previous trusted window systems such as trusted x, and well within the range of what can feasibly be evaluated for high assurance.
graphical dictionaries and the memorable space of graphical passwords. in commonplace textual password schemes, users choose passwords that are easy to recall. since memorable passwords typically exhibit patterns, they are exploitable by brute-force password crackers using attack dictionaries. this leads us to ask what classes of graphical passwords users find memorable. we postulate one such class supported by a collection of cognitive studies on visual recall, which can be characterized as mirror symmetric (reflective) passwords. we assume that an attacker would put this class in an attack dictionary for graphical passwords and propose how an attacker might order such a dictionary. we extend the existing analysis of graphical passwords by analyzing the size of the mirror symmetric password space relative to the full password space of the graphical password scheme of jermyn et al. (1999), and show it to be exponentially smaller (assuming appropriate axes of reflection). this reduction in size can be compensated for by longer passwords: the size of the space of mirror symmetric passwords of length about l + 5 exceeds that of the full password space for corresponding length l ≤ 14 on a 5 × 5 grid. this work could be used to help in formulating password rules for graphical password users and in creating proactive graphical password checkers.
very fast containment of scanning worms. computer worms - malicious, self-propagating programs - represent a significant threat to large networks. one possible defense, containment, seeks to limit a worm's spread by isolating it in a small subsection of the network. in this work we develop containment algorithms suitable for deployment in high-speed, low-cost network hardware. we show that these techniques can stop a scanning host after fewer than 10 scans with a very low false-positive rate. we also augment this approach by devising mechanisms for cooperation that enable multiple containment devices to more effectively detect and respond to an emerging infection. finally, we discuss ways that a worm can attempt to bypass containment techniques in general, and ours in particular.
securing frame communication in browsers. many web sites embed third-party content in frames, relying on the browser's security policy to protect against malicious content. however, frames provide insufficient isolation in browsers that let framed content navigate other frames. we evaluate existing frame navigation policies and advocate a stricter policy, which we deploy in the open-source browsers. in addition to preventing undesirable interactions, the browser's strict isolation policy also affects communication between cooperating frames. we therefore analyze two techniques for interframe communication between isolated frames. the first method, fragment identifier messaging, initially provides confidentiality without authentication, which we repair using concepts from a well-known network protocol. the second method, <code>postmessage</code>, initially provides authentication, but we discover an attack that breaches confidentiality. we propose improvements in the <code>postmessage</code> api to provide confidentiality; our proposal has been standardized and adopted in browser implementations.
votebox: a tamper-evident, verifiable electronic voting system. commercial electronic voting systems have experienced many high-profile software, hardware, and usability failures in real elections. while it is tempting to abandon electronic voting altogether, we show how a careful application of distributed systems and cryptographic techniques can yield voting systems that surpass current systems and their analog forebears in trustworthiness and usability. we have developed the votebox, a complete electronic voting system that combines several recent e-voting research results into a coherent whole that can provide strong end-to-end security guarantees to voters. votebox machines are locally networked and all critical election events are broadcast and recorded by every machine on the network. votebox network data, including encrypted votes, can be safely relayed to the outside world in real time, allowing independent observers with personal computers to validate the system as it is running. we also allow any voter to challenge a votebox, while the election is ongoing, to produce proof that ballots are cast as intended. the votebox design offers a number of pragmatic benefits that can help reduce the frequency and impact of poll worker or voter errors.
lest we remember: cold boot attacks on encryption keys. contrary to widespread assumption, dynamic ram (dram), the main memory in most modern computers, retains its contents for several seconds after power is lost, even at room temperature and even if removed from a motherboard. although dram becomes less reliable when it is not refreshed, it is not immediately erased, and its contents persist sufficiently for malicious (or forensic) acquisition of usable full-system memory images. we show that this phenomenon limits the ability of an operating system to protect cryptographic key material from an attacker with physical access to a machine. it poses a particular threat to laptop users who rely on disk encryption: we demonstrate that it could be used to compromise several popular disk encryption products without the need for any special devices or materials. we experimentally characterize the extent and predictability of memory retention and report that remanence times can be increased dramatically with simple cooling techniques. we offer new algorithms for finding cryptographic keys in memory images and for correcting errors caused by bit decay. though we discuss several strategies for mitigating these risks, we know of no simple remedy that would eliminate them.
reverse-engineering a cryptographic rfid tag. the security of embedded devices often relies on the secrecy of proprietary cryptographic algorithms. these algorithms and their weaknesses are frequently disclosed through reverse-engineering software, but it is commonly thought to be too expensive to reconstruct designs from a hardware implementation alone. this paper challenges that belief by presenting an approach to reverse-engineering a cipher from a silicon implementation. using this mostly automated approach, we reveal a cipher from an rfid tag that is not known to have a software or micro-code implementation. we reconstruct the cipher from the widely used mifare classic rfid tag by using a combination of image analysis of circuits and protocol analysis. our analysis reveals that the security of the tag is even below the level that its 48-bit key length suggests due to a number of design flaws. weak random numbers and a weakness in the authentication protocol allow for pre-computed rainbow tables to be used to find any key in a matter of seconds. our approach of deducing functionality from circuit images is mostly automated, hence it is also feasible for large chips. the assumption that algorithms can be kept secret should therefore to be avoided for any type of silicon chip.
highly predictive blacklisting. the notion of blacklisting communication sources has been a well-established defensive measure since the origins of the internet community. in particular, the practice of compiling and sharing lists of the worst offenders of unwanted traffic is a blacklisting strategy that has remained virtually unquestioned over many years. but do the individuals who incorporate such blacklists into their perimeter defenses benefit from the blacklisting contents as much as they could from other list-generation strategies? in this paper, we will argue that there exist better alternative blacklist generation strategies that can produce higher-quality results for an individual network. in particular, we introduce a blacklisting system based on a relevance ranking scheme borrowed from the link-analysis community. the system produces customized blacklists for individuals who choose to contribute data to a centralized log-sharing infrastructure. the ranking scheme measures how closely related an attack source is to a contributor, using that attacker's history and the contributor's recent log production patterns. the blacklisting system also integrates substantive log prefiltering and a severity metric that captures the degree to which an attacker's alert patterns match those of common malware-propagation behavior. our intent is to yield individualized blacklists that not only produce significantly higher hit rates, but that also incorporate source addresses that pose the greatest potential threat. we tested our scheme on a corpus of over 700 million log entries produced from the dshield data center and the result shows that our blacklists not only enhance hit counts but also can proactively incorporate attacker addresses in a timely fashion. an early form of our system have been fielded to dshield contributors over the last year.
botminer: clustering analysis of network traffic for protocol- and structure-independent botnet detection. botnets are now the key platform for many internet attacks, such as spam, distributed denial-of-service (ddos), identity theft, and phishing. most of the current botnet detection approaches work only on specific botnet command and control (c&c) protocols (e.g., irc) and structures (e.g., centralized), and can become ineffective as botnets change their c&c techniques. in this paper, we present a general detection framework that is independent of botnet c&c protocol and structure, and requires no a priori knowledge of botnets (such as captured bot binaries and hence the botnet signatures, and c&c server names/addresses). we start from the definition and essential properties of botnets. we define a botnet as a coordinated group of malware instances that are controlled via c&c communication channels. the essential properties of a botnet are that the bots communicate with some c&c servers/peers, perform malicious activities, and do so in a similar or correlated way. accordingly, our detection framework clusters similar communication traffic and similar malicious traffic, and performs cross cluster correlation to identify the hosts that share both similar communication patterns and similar malicious activity patterns. these hosts are thus bots in the monitored network. we have implemented our botminer prototype system and evaluated it using many real network traces. the results show that it can detect real-world botnets (irc-based, http-based, and p2p botnets including nugache and storm worm), and has a very low false positive rate.
automatic generation of xss and sql injection attacks with goal-directed model checking. cross-site scripting (xss) and sql injection errors are two prominent examples of taint-based vulnerabilities that have been responsible for a large number of security breaches in recent years. this paper presents qed, a goal-directed model-checking system that automatically generates attacks exploiting taint-based vulnerabilities in large java web applications. this is the first time where model checking has been used successfully on real-life java programs to create attack sequences that consist of multiple http requests. qed accepts any java web application that is written to the standard servlet specification. the analyst specifies the vulnerability of interest in a specification that looks like a java code fragment, along with a range of values for form parameters. qed then generates a goal-directed analysis from the specification to perform session-aware tests, optimizes to eliminate inputs that are not of interest, and feeds the remainder to a model checker. the checker will systematically explore the remaining state space and report example attacks if the vulnerability specification is matched. qed provides better results than traditional analyses because it does not generate any false positive warnings. it proves the existence of errors by providing an example attack and a program trace showing how the code is compromised. past experience suggests this is important because it makes it easy for the application maintainer to recognize the errors and to make the necessary fixes. in addition, for a class of applications, qed can guarantee that it has found all the potential bugs in the program. we have run qed over 3 java web applications totaling 130,000 lines of code. we found 10 sql injections and 13 cross-site scripting errors.
selective versioning in a secure disk system. making vital disk data recoverable even in the event of os compromises has become a necessity, in view of the increased prevalence of os vulnerability exploits over the recent years. we present the design and implementation of a secure disk system, svsds, that performs selective, flexible, and transparent versioning of stored data, at the disk-level. in addition to versioning, svsds actively enforces constraints to protect executables and system log files. most existing versioning solutions that operate at the disk-level are unaware of the higher-level abstractions of data, and hence are not customizable. we evolve a hybrid solution that combines the advantages of disk-level and file-system--level versioning systems thereby ensuring security, while at the same time allowing flexible policies. we implemented and evaluated a software-level prototype of svsds in the linux kernel and it shows that the space and performance overheads associated with selective versioning at the disk level are minimal.
practical symmetric key cryptography on modern graphics hardware. graphics processors are continuing their trend of vastly outperforming cpus while becoming more general purpose. the latest generation of graphics processors have introduced the ability handle integers natively. this has increased the gpu's applicability to many fields, especially cryptography. this paper presents an application oriented approach to block cipher processing on gpus. a new block based conventional implementation of aes on an nvidia g80 is shown with 4-10x speed improvements over cpu implementations and 2-4x speed increase over the previous fastest aes gpu implementation. we outline a general purpose data structure for representing cryptographic client requests which is suitable for execution on a gpu. we explore the issues related to the mapping of this general structure to the gpu. finally we present the first analysis of the main encryption modes of operation on a gpu, showing the performance and behavioural implications of executing these modes under the outlined general purpose data model. our aes implementation is used as the underlying block cipher to show the overhead of moving from an optimised hard-coded approach to a generalised one.
an improved clock-skew measurement technique for revealing hidden services. the tor anonymisation network allows services, such as web servers, to be operated under a pseudonym. in previous work murdoch described a novel attack to reveal such hidden services by correlating clock skew changes with times of increased load, and hence temperature. clock skew measurement suffers from two main sources of noise: network jitter and timestamp quantisation error. depending on the target's clock frequency the quantisation noise can be orders of magnitude larger than the noise caused by typical network jitter. quantisation noise limits the previous attacks to situations where a high frequency clock is available. it has been hypothesised that by synchronising measurements to the clock ticks, quantisation noise can be reduced. we show how such synchronisation can be achieved and maintained, despite network jitter. our experiments show that synchronised sampling significantly reduces the quantisation error and the remaining noise only depends on the network jitter (but not clock frequency). our improved skew estimates are up to two magnitudes more accurate for low-resolution timestamps and up to one magnitude more accurate for high-resolution timestamps, when compared to previous random sampling techniques. the improved accuracy not only allows previous attacks to be executed faster and with less network traffic but also opens the door to previously infeasible attacks on low-resolution clocks, including measuring skew of a http server over the anonymous channel.
measurement and classification of humans and bots in internet chat. the abuse of chat services by automated programs, known as chat bots, poses a serious threat to internet users. chat bots target popular chat networks to distribute spam and malware. in this paper, we first conduct a series of measurements on a large commercial chat network. our measurements capture a total of 14 different types of chat bots ranging from simple to advanced. moreover, we observe that human behavior is more complex than bot behavior. based on the measurement study, we propose a classification system to accurately distinguish chat bots from human users. the proposed classification system consists of two components: (1) an entropy-based classifier and (2) a machine-learning-based classifier. the two classifiers complement each other in chat bot detection. the entropy-based classifier is more accurate to detect unknown chat bots, whereas the machine-learning-based classifier is faster to detect known chat bots. our experimental evaluation shows that the proposed classification system is highly effective in differentiating bots from humans.
an empirical security study of the native code in the jdk. it is well known that the use of native methods in java defeats java's guarantees of safety and security, which is why the default policy of java applets, for example, does not allow loading non-local native code. however, there is already a large amount of trusted native c/c++ code that comprises a significant portion of the java development kit (jdk). we have carried out an empirical security study on a portion of the native code in sun's jdk 1.6. by applying static analysis tools and manual inspection, we have identified in this security-critical code previously undiscovered bugs. based on our study, we describe a taxonomy to classify bugs. our taxonomy provides guidance to construction of automated and accurate bug-finding tools. we also suggest systematic remedies that can mediate the threats posed by the native code.
cloudav: n-version antivirus in the network cloud. antivirus software is one of the most widely used tools for detecting and stopping malicious and unwanted files. however, the long term effectiveness of traditional host-based antivirus is questionable. antivirus software fails to detect many modern threats and its increasing complexity has resulted in vulnerabilities that are being exploited by malware. this paper advocates a new model for malware detection on end hosts based on providing antivirus as an in-cloud network service. this model enables identification of malicious and unwanted software by multiple, heterogeneous detection engines in parallel, a technique we term 'n-version protection'. this approach provides several important benefits including better detection of malicious software, enhanced forensics capabilities, retrospective detection, and improved deployability and management. to explore this idea we construct and deploy a production quality in-cloud antivirus system called cloudav. cloudav includes a lightweight, cross-platform host agent and a network service with ten antivirus engines and two behavioral detection engines. we evaluate the performance, scalability, and efficacy of the system using data from a real-world deployment lasting more than six months and a database of 7220 malware samples covering a one year period. using this dataset we find that cloudav provides 35% better detection coverage against recent threats compared to a single antivirus engine and a 98% detection rate across the full dataset. we show that the average length of time to detect new threats by an antivirus engine is 48 days and that retrospective detection can greatly minimize the impact of this delay. finally, we relate two case studies demonstrating how the forensics capabilities of cloudav were used by operators during the deployment.
proactive surge protection: a defense mechanism for bandwidth-based attacks. large-scale bandwidth-based distributed denial-of-service (ddos) attacks can quickly knock out substantial parts of a network before reactive defenses can respond. even traffic that is not under direct attack can suffer significant collateral damage if the traffic passes through links that are common to attack routes. this paper presents a proactive surge protection (psp) mechanism that aims to provide a broad first line of defense against ddos attacks. the approach aims to minimize collateral damage by providing bandwidth isolation between traffic flows. the proposed solution is readily deployable using existing router mechanisms and does not rely on any unauthenticated packet header information. our extensive evaluation across two large commercial backbone networks, using both distributed and targeted attacks, shows that up to 95.5% of the network could suffer collateral damage, but our solution was able to significantly reduce the amount of collateral damage by up to 97.58% in terms of the number of packets dropped and 90.36% in terms of the number of flows with packet loss. further, we show that psp can maintain low packet loss rates even when the intensity of attacks is increased significantly.
the practical subtleties of biometric key generation. the inability of humans to generate and remember strong secrets makes it difficult for people to manage cryptographic keys. to address this problem, numerous proposals have been suggested to enable a human to repeatably generate a cryptographic key from her biometrics, where the strength of the key rests on the assumption that the measured biometrics have high entropy across the population. in this paper we show that, despite the fact that several researchers have examined the security of bkgs, the common techniques used to argue the security of practical systems are lacking. to address this issue we reexamine two well known, yet sometimes misunderstood, security requirements. we also present another that we believe has not received adequate attention in the literature, but is essential for practical biometric key generators. to demonstrate that each requirement has significant importance, we analyze three published schemes, and point out deficiencies in each. for example, in one case we show that failing to meet a requirement results in a construction where an attacker has a 22% chance of finding ostensibly 43-bit keys on her first guess. in another we show how an attacker who compromises a user's cryptographic key can then infer that user's biometric, thus revealing any other key generated using that biometric. we hope that by examining the pitfalls that occur continuously in the literature, we enable researchers and practitioners to more accurately analyze proposed constructions.