303 research outputs found
A local bus for MCM-based microinstrumentation systems
A local bus is described which is designed for use in a multi-chipcomposed
microinstrumentation system. The bus is able to transmit a digital
code, bitstream, analog voltage, frequency, duty-cycle and also provides
calibration facilities, service request and interrupt request for the smart
sensors. Corresponding sensor bus interface was implemented in a 1.6 m
CMOS process and successfully tested in a local sensor network.Junta Nacional de Investigação Científica e Tecnológica - Praxis XXI-BD/5181/95.
STW - Project DEL 55.3733.
TUDelft
A local bus for multi-chip-module-based microinstrumentation systems
A local smart bus is described which is designed for use in muti-chip-composed microinstrumentation system. The bus is able to transmit a digital code, bitstream, analog voltage, frquency, duty-cycle and also provides calibration facilities, service request and interrupt request modes.FC
A low-power low-voltage digital bus interface for MCM-based microsystems
Comunicação apresentada na 23rd European Solid-State Circuits Conference (ESSCIRC '97), Southampton, UK, 16-18 September 1997.This paper describes a digital local bus interface, which is designed for use
in a multi-chip-composed microsystem. The chip area using a CMOS 1.6mm
n-well technology is 1mm2. Power consumption at 5V@100kHz is less than
500mW and for 5V@4MHz less than 2mW due to a smart power management
of all functional blocks. The bus interface is able to transmit a digital code,
bitstream, analog voltage, frequency, duty-cycle and also provides calibration
facilities, service request and interrupt request for the smart sensors or
microactuators.Junta Nacional de Investigação Científica e Tecnológica - Praxis XXI-BD/5181/95.
STW - Project DEL 55.3733.
TUDelft
Quotient probabilistic normed spaces and completeness results
Quotient spaces of probabilistic normed spaces have never been considered. This note is a first attempt to fill this gap: the quotient space of a PN space with respect to one of its subspaces is introduced and its properties are studied. Finally, we investigate the completeness relationship among the PN spaces considered
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Post-Patch Retraining for Host-Based Anomaly Detection
Applying patches, although a disruptive activity, remains a vital part of software maintenance and defense. When host-based anomaly detection (AD) sensors monitor an application, patching the application requires a corresponding update of the sensor's behavioral model. Otherwise, the sensor may incorrectly classify new behavior as malicious (a false positive) or assert that old, incorrect behavior is normal (a false negative). Although the problem of "model drift" is an almost universally acknowledged hazard for AD sensors, relatively little work has been done to understand the process of re-training a "live" AD model --- especially in response to legal behavioral updates like vendor patches or repairs produced by a self-healing system. We investigate the feasibility of automatically deriving and applying a "model patch" that describes the changes necessary to update a "reasonable" host-based AD behavioral model ({\it i.e.,} a model whose structure follows the core design principles of existing host--based anomaly models). We aim to avoid extensive retraining and regeneration of the entire AD model when only parts may have changed --- a task that seems especially undesirable after the exhaustive testing necessary to deploy a patch
Adaptive Anomaly Detection via Self-Calibration and Dynamic Updating
The deployment and use of Anomaly Detection (AD) sensors often requires the intervention of a human expert to manually calibrate and optimize their performance. Depending on the site and the type of traffic it receives, the operators might have to provide recent and sanitized training data sets, the characteristics of expected traffic (i.e. outlier ratio), and exceptions or even expected future modifications of system's behavior. In this paper, we study the potential performance issues that stem from fully automating the AD sensors' day-to-day maintenance and calibration. Our goal is to remove the dependence on human operator using an unlabeled, and thus potentially dirty, sample of incoming traffic. To that end, we propose to enhance the training phase of AD sensors with a self-calibration phase, leading to the automatic determination of the optimal AD parameters. We show how this novel calibration phase can be employed in conjunction with previously proposed methods for training data sanitization resulting in a fully automated AD maintenance cycle. Our approach is completely agnostic to the underlying AD sensor algorithm. Furthermore, the self-calibration can be applied in an online fashion to ensure that the resulting AD models reflect changes in the system's behavior which would otherwise render the sensor's internal state inconsistent. We verify the validity of our approach through a series of experiments where we compare the manually obtained optimal parameters with the ones computed from the self-calibration phase. Modeling traffic from two different sources, the fully automated calibration shows a 7.08% reduction in detection rate and a 0.06% increase in false positives, in the worst case, when compared to the optimal selection of parameters. Finally, our adaptive models outperform the statically generated ones retaining the gains in performance from the sanitization process over time
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Online Training and Sanitization of AD Systems
In this paper, we introduce novel techniques that enhance the training phase of Anomaly Detection (AD) sensors. Our aim is to both improve the detection performance and protect against attacks that target the training dataset. Our approach is two pronged: we employ a novel sanitization method for large training datasets that removes attacks and traffic artifacts by measuring their frequency and position inside the dataset. Furthermore, we extend the training phase in the spatial dimension to include model information from other collaborative systems. We demonstrate that by doing so we can protect all the participating systems against targeted training attacks. Another aspect of our system is its ability to adapt and update the normality model when there is a shift in the nature of inspected traffic that reflects actual changes in the back-end servers. Such "on-line" training appears to be the "Achilles' heel" of AD sensors because they fail to adapt when there is a legitimate deviation in the traffic behavior, thereby flooding the operator with false positives. To counter that, we discuss the integration of what we call a shadow sensor with the AD system. This sensor complements our techniques by acting as an oracle to analyze and classify the resulting "suspect data" identified by the AD sensor. We show that our techniques can be applied to a wide range of unmodified AD sensors without incurring significant additional computational cost beyond the initial training phase
A microinstrumenation system for industrial applications
This paper describes the development of a
microinstrumentation system in silicon containing all the
components of the data acquisition system, such as sensors,
signal-conditioning circuits, analog-digital converter, interface
circuits, sensor bus interface, and an embedded
microcontroller (MCU). The microinstrumentation system is
to be fabricated using the Multi-Chip-Module (MCM)
technology based on a chip-level infrastructure. A standard
silicon platform is the floorplan for individual smart sensor die
attachment and an on-chip local sensor bus interface, testing
facilities, optional compatible sensors (such as thermal
sensors). The microinstrumentation system is controlled by a
MCU with several modes of low-power operation (inclusive
stand-by mode). As the intended application requires a huge
amount of data-processing, a RISC-type MCU architecture is
to be used. The MCU communicates with the front-end sensors
via a two-line (clock and data lines) intramodule sensor bus
(Integrated Smart Sensor bus). The sensor scan rate is
adaptive and can be event triggered. This upgraded version of
the ISS bus allows: service and interrupt request from the
sensors, test and calibration facilities. However, the additional
functionality requires a third line. The MCU also controls the
power consumption and the thermal budget of all system. This
paper also presents three applications for the
microinstrumentation system: condition monitoring of
machines, an inertial navigation system and a miniature
spectrometer.STW - Project DEL55.3733.
TUDelft.
Junta Nacional de Investigação Científica e Tecnológica - Praxis XXI-BD/5181/95
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From STEM to SEAD: Speculative Execution for Automated Defense
Most computer defense systems crash the process that they protect as part of their response to an attack. In contrast, self-healing software recovers from an attack by automatically repairing the underlying vulnerability. Although recent research explores the feasibility of the basic concept, self-healing faces four major obstacles before it can protect legacy applications and COTS software. Besides the practical issues involved in applying the system to such software (e.g., not modifying source code), self-healing has encountered a number of problems: knowing when to engage, knowing how to repair, and handling communication with external entities. Our previous work on a self-healing system, STEM, left these challenges as future work. STEM provides self-healing by speculatively executing "slices" of a process. This paper improves STEM's capabilities along three lines: (1) applicability of the system to COTS software (STEM does not require source code, and it imposes a roughly 73% performance penalty on Apache's normal operation), (2) semantic correctness of the repair (we introduce virtual proxies and repair policy to assist the healing process), and (3) creating a behavior profile based on aspects of data and control flow
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