Previous Detection Schemes Research Articles

A Study on the Impact of Memory DoS Attacks on Cloud Applications and Exploring Real-Time Detection Schemes

Even though memory denial-of-service attacks can cause severe performance degradations on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co-located</i> virtual machines, a previous detection scheme against such attacks cannot accurately detect the attacks and also generates high detection delay and high performance overhead since it assumes that cache-related statistics of an application follow the same probability distribution at all times, which may not be true for all types of applications. In this paper, we present the experimental results showing the impacts of memory DoS attacks on different types of cloud-based applications. Based on these results, we propose two lightweight and responsive Statistical based Detection Schemes (SDS/B and SDS/P) that can detect such attacks accurately. SDS/B constructs a profile of normal range of cache-related statistics for all applications and use statistical methods to infer an attack when the real-time collected statistics exceed this normal range, while SDS/P exploits the increased periods of access patterns for periodic applications to infer an attack. Upon SDS, we further leverage deep neural network (DNN) techniques to design a DNN-based detection scheme that is general to various types of applications and more robust to adaptive attack scenarios. Our evaluation results show that SDS/B, SDS/P and DNN outperform the state-of-the-art detection scheme, e.g., with 65% higher specificity, 40% shorter detection delay, and 7% less performance overhead. We also discuss how to use SDS and DNN-based detection schemes under different situations.

Early Tsunami Detection With Near‐Fault Ocean‐Bottom Pressure Gauge Records Based on the Comparison With Seismic Data

AbstractOffshore real‐time ocean bottom networks of seismometers and ocean bottom pressure (OBP) gauges have been recently established such as DONET and S‐net around the Japanese islands. One of their purposes is to practice rapid and accurate tsunami forecasting. Near‐fault OBP records, however, are always contaminated by nontsunami components such as sea‐bottom acceleration change until an earthquake stops its fault or sea‐floor motions. This study proposes a new method to separate tsunami and ocean bottom displacement components from coseismic OBP records in a real‐time basis. Associated with the Off‐Mie earthquake of 2016 April 1, we first compared OBP data with acceleration, velocity, and displacement seismograms recorded by seismometers at common ocean bottom sites in both time and frequency domains. Based on this comparison, we adopted a band‐pass filter of 0.05–0.15 Hz to remove ocean‐bottom acceleration components from the OBP data. Resulting OBP waveforms agree well with the tsunami components estimated by a 100‐s low‐pass filter with records of several hundred seconds in length. Our method requires only an early portion of a given OBP record after 30 s of an origin time in order to estimate its tsunami component accurately. Our method enhances early tsunami detections with near‐fault OBP data; that is, it will make a tsunami forecasting system faster and more reliable than the previous detection schemes that require data away from source regions or after coseismic motions are over.

Pixel Based Image Forensic Technique for Copy-move Forgery Detection Using Auto Color Correlogram

The credibility of a digital image plays a generous role in blind Image forensics. We focus on detection of a typical kind of forgery, referred to as copy-move forgery, which is commonly practiced in the field of blind image forensics. In this kind of forgery as the forged region belongs to same image, it introduces inconsistency at pixel level in the tampered image. In the proposed work, we exploit a content based image retrieval feature extraction technique for detection of forgery. Auto Color Correlogram, which is a low complexity feature extraction technique, is employed to obtain feature vectors from the forged image. ACC is not used for detection of copy-move forgery in the previous detection schemes. The scheme is also successful in detecting forged region which is scaled or rotated on pasting, also effectively detects multiple region duplication within the image.

High-sensitivity ultrasound-modulated optical tomography with a photorefractive polymer

By detecting ultrasonically tagged diffuse light, ultrasound-modulated optical tomography images optical contrast with ultrasonic resolution deep in turbid media, such as biological tissue. However, small detection etendues and weak tagged light submerged in strong untagged background light limit the signal detection sensitivity. In this Letter, we report the use of a large-area (~5 cm×5 cm) photorefractive polymer film that yields more than 10 times detection etendue over previous detection schemes. Our polymer-based system enabled us to resolve absorbing objects embedded inside diffused media thicker than 80 transport mean free paths, by using moderate light power and short ultrasound pulses.

Mitigating colluding injected attack using monitoring verification in mobile ad‐hoc networks

ABSTRACTMobile ad‐hoc networks (MANETs) have attracted significant research attention recently because of the fast growth of laptops, personal digital assistant, and 802.11/Wi‐Fi wireless networking. However, the flexible deployment nature and the lack of fixed infrastructure make MANETs suffer from a variety of security attacks. In this paper, we show how an adversary can utilize a colluding injected attack (CIA) in MANET by injecting malicious nodes in the network, while hiding their identities from other legitimate nodes. These injected nodes will work together (colluding) to create a collision at an arbitrary node, thus preventing it from receiving or relaying any packet. Because of this collision, a legitimate node could be reported as malicious nodes by monitoring nodes in the neighborhood. In this work, we propose a monitoring verification scheme to mitigate the effect of the CIA attack. Our proposed scheme is able to accurately detect malicious nodes in the network compared with previous detection schemes. Through simulations, we show that our proposed scheme outperforms previous detection schemes in terms of true/false detection of any malicious behavior in the network caused by the CIA attack. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Simplified multi-track detection schemes using a priori information for bit patterned media recording

Simplified multi-track detection schemes using a priori information for bit patterned magnetic recording (BPMR) are proposed in this paper. The proposed detection schemes adopt the simplified trellis diagram, use a priori information, and detect the main-track data in the along- and cross-track directions. The simplified trellis diagram, which has 4 states and 8 branches, can be obtained by setting the corner entries of the generalized partial response (GPR) target to zero and replacing the four parallel branches with a single branch. However, these simplified techniques seriously suffer from performance degradation in high density BPMR channels. To overcome the performance degradation, a priori information is used to give higher reliability to the branch metric. In addition, to fully use the characteristics of channel detection with a two-dimensional (2D) GPR target, the proposed schemes estimate a priori information and detect the main-track data in the along- and cross-track directions by using a 2D equalizer with a 2D GPR target. The bit error rate performances of the proposed schemes are compared with the previous detection schemes when areal density is 3 Tb/in2. Simulation results show that the proposed schemes with simpler structures have more than 2 dB gains compared with the other detection schemes.

Computer-aided detection of clustered microcalcifications: an improved method for grouping detected signals.

A computerized scheme for the automated detection of clustered microcalcifications from digital mammograms is being developed. This scheme is one part of an overall package for computer-aided diagnosis (CAD), the purpose of which is to assist radiologists in detecting and diagnosing breast cancer. One important step in the computer detection scheme is to group or cluster microcalcifications, since clustered microcalcifications are more clinically significant than are isolated microcalcifications. Previously a "growing" technique in which signals (possible microcalcifications) were clustered by grouping those that were within some predefined distance from the center of the growing cluster was used. In this paper, a new technique for grouping signals, which consists of two steps, is introduced. First, signals that may be several pixels in area are reduced to single pixels by means of a recursive transformation. Second, the number of signals (nonzero pixels) within a small region, typically 3.2 x 3.2 mm, are counted. Only if three or more signals are present within such a region are they preserved in the output image. In this way, isolated signals are eliminated. Furthermore, this method can eliminate falsely detected clusters, which were identified by a previous detection scheme, based on the spatial distribution of signals within the cluster. The differences in performance of the CAD scheme for detecting clustered microcalcifications using the old and new clustering techniques was measured using 78 mammograms, containing 41 clusters.(ABSTRACT TRUNCATED AT 250 WORDS)