Sequential Attack Detection in Recommender Systems

Community detection is a dominant research field in social media mining. Numerous algorithms have been developed to detect community structure in network. Each of them focuses on different characteristic of data and generates different communities for same dataset. So cluster validation or comparative analysis of these algorithms is required to compare the efficiency of these algorithms. There are two known ways for cluster validation: Internal Quality Measure (IQM) and External Quality Measure (EQM). IQM evaluates the cluster without reference to external information while EQM requires already known clusters i.e. ground truth for comparison. Most of the researchers have compared Community Detection algorithms on the basis of either IQM or EQM but not both. In this paper, we have tried to compare nine existing sequential community detection algorithms on the basis of both IQM and EQM and we observed that overall performance of Louvain, Fastgreedy and Walktrap algorithm is more efficient than other algorithms. From this paper, researchers working in this area can get information about major sequential community detection algorithms and their efficiency on small real world datasets. They can incorporate the findings to develop more efficient algorithms.

A sequential two-dimensional (2D) partial response maximum likelihood (PRML) detection scheme for holographic data storage (HDS) systems is proposed. We use two complexity reduction schemes, a reduced-state trellis and a constant-weight (CW) constraint. In the reduced-state trellis, the limited candidate bits surrounding the target bit are considered for the 2D PRML detector. In the CW constraint, the trellis transitions that violate the CW condition that each code-word block has only one white bit are eliminated. However, the 2D PRML detector using the complexity reduction schemes, which operates on 47 states and 169 branches, has performance degradation. To overcome performance degradation, a sequential detection algorithm uses the estimated a priori probability. By the sequential procedure, we mitigate 2D intersymbol interference with an enhanced reliability of the branch metric. Simulation results show that the proposed 2D PRML detection scheme yields about 3 dB gains over the one-dimensional PRML detection scheme.

Motivated from a molecular dynamics context we propose a sequential change point detection algorithm for vector-valued autoregressive models based on Bayesian model selection. The algorithm does not rely on any sampling procedure or assumptions underlying the dynamics of the transitions and is designed to cope with high-dimensional data. We show the applicability of the algorithm on a time series obtained from numerical simulation of a penta-peptide molecule.

Biased ratings of attack profiles have a significant impact on the effectiveness of collaborative recommender systems. Previous work has shown standard memory-based recommendation algorithms, such as k-nearest neighbor (kNN), susceptible to the attacks compared with model-based collaborative filtering (CF) algorithms. An obfuscated diverse attack strategy made model-based algorithms vulnerable to attacks. Attack profiles generated with this strategy are also able to avoid principal component analysis (PCA)-based detection. This paper proposes an algorithm to detect obfuscated diverse attack profiles. Profiles' pairwise covariance with each other is used to separate attack profiles from genuine profiles. Through extensive experiments, we demonstrate that our algorithm detects these attack profiles with high accuracy.

It is supposed that there is a multisensor system in which each sensor performs sequential detection of a target. Then the binary decisions on target presence and absence are transmitted to a fusion center, which combines them to improve the performance of the system. We assume that sensors represent multichannel systems with possibly each one having different number of channels. Sequential detection of a target in each sensor is done by implementing a generalized Wald's sequential probability ratio test which is based on the maximum likelihood ratio statistic and allows one to fix the false alarm rate and the mis-detection rate at specified levels. We first show that this sequential detection procedure is asymptotically optimal for general statistical models in the sense of minimizing the expected sample size when the probabilities of errors vanish. We then construct the optimal non-sequential fusion rule that waits until all the local decisions in all sensors are made and then fuses them. It is optimal in the sense of maximizing the probability of target detection for a fixed probability of a false alarm or minimizing the maximal probability of error (minimax criterion). An analysis shows that the final decision can be made substantially more reliable even for a small number of sensors (3-5). The performance of the system is illustrated by the example of detecting a deterministic signal in correlated (color) Gaussian noise. In this example, we provide both the results of theoretical analysis and the results of Monte Carlo experiment. These results allow us to conclude that the use of the sequential detection algorithm substantially reduces the required resources of the system compared to the best non-sequential algorithm.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We consider the problem of detecting spontaneous changes in the characteristics of a time series (a time series breakdown), when the breakdown manifests itself in the form of a stepwise change in the value of the location parameter (expectation or median) of the probability distribution function of the controlled process. It is proposed to solve this problem in real time applying a sequential nonparametric detection algorithm based on using the random walk theory mechanism. By computer simulation we investigate the probabilistic characteristics of the proposed algorithm and its efficiency in comparison with the well-known CUSUM-algorithm of parametric type. A procedure for synthesizing a control algorithm with the specified properties is given. It emphasizes the prospects of using such an algorithm in monitoring systems for various purposes, usually created in conditions of shortage of a priori information about the properties of the controlled process.

Abnormal event detection with the lowest latency is an indispensable function for safety-critical systems, such as cyber defense systems. However, as systems become increasingly complicated, conventional sequential event detection methods become less effective, especially when we need to define indicator metrics from complicated data manually. Although deep neural networks (DNNs) have been used to handle heterogeneous data, the theoretic assurability and explainability are still insufficient. This article provides a holistic framework for the quickest and sequential detection of abnormalities and time-dependent abnormal events. We explore the latent space characteristics of zero-bias neural networks considering the classification boundaries and abnormalities. We then provide a novel method to convert zero-bias DNN classifiers into performance-assured binary abnormality detectors. Finally, we provide a sequential quickest detection (QD) scheme that provides the theoretically assured lowest abnormal event detection delay under false alarm constraints using the converted abnormality detector. We verify the effectiveness of the framework using real massive signal records in aviation communication systems and simulation. Codes and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pcwhy/AbnormalityDetectionInZbDNN</uri> .

We describe a fully automated method for quantification of left ventricular performance by equilibrium radionuclide ventriculographic studies, based on subdivision of the left ventricular region into 9 equiangular sectors. The precise identification of the left ventricular contours is achieved by the use of morphological and functional criteria in a sequential edge detection algorithm with a success rate of 96%. In addition to left ventricular global and sectorial ejection fraction the first harmonic of the corresponding Fourier spectrum is approximated to each sectorial time-activity curve and to the global one. Sectorial phase is calculated as the difference between the phase of the sectorial and global first Fourier component. Computerized comparison between the sectorial parameters at rest and during peak exercise localizes and classifies the degree of global and regional impairment in response to exercise. The processing time of 60 sec makes this method suitable for routine use. The validity of our procedure has been tested in 34 patients before and after successful transluminal coronary angioplasty. In these patients, 73% of the stenosed vessels before dilatation were localized by sectorial ejection fraction, 77% by sectorial phases, and 88% by the combination of both.

Cognitive Radio is an enabling technology that is expected to address the problem of scarcity of spectrum by intelligent access, mobility and management of spectrum and pave way for efficient usage of the same. Increasingly, it is seen that most research is directed towards building theoretic models for spectrum access and management. Though some of these are backed by simulations, certainty of any method can be proven only by practical implementation on hardware testbeds in real-world environments. Sometimes, assumptions that are made to satisfy simulation requirements are difficult to realize in practical scenarios, thereby, their implementation in real-world is even more challenging. In this paper, taking cue from our earlier implementation work on simple Snapshot ED and CUSUM based algorithms for single node spectrum sensing, we explain the implementation of Sequential change detection algorithm in co-operative mode, the Dual-CUSUM. Single node sensing is not reliable, especially, when the nodes are subjected to shadow fading due to obstacles (hidden node problem). This is mitigated by using co-operative sensing that exploits the inherent spatial diversity and it is seen that with certain fusion rules, Cooperative CUSUM (aka Dual-CUSUM) is better than its counterpart Co-operative Snapshot ED, especially, under low SNR regimes.

Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI

In this paper, we propose a secure framework for healthcare monitoring using the Internet of Medical Things (IoMT). In spite of their deployment, these devices still vulnerable to several cyber-attacks, ranging from unauthorized access to private medical data to data modification and injection. These attacks can compromise the privacy of the monitored patient, reduce the reliability of the monitoring system and may harm the life of monitored patient. In this paper, we propose a new framework to detect attacks and secure the communications in IoMT. To prevent eavesdropping and modification attacks, we propose the Ephemeral Elliptic Curves Diffie-Hellman (EECDH) to derive a session key used to provide confidentiality and authenticity. To detect injected measurements, flooding triggered by compromised devices and medical changes in physiological data, we applied the sequential change point detection algorithm Pruned Exact Linear Time (PELT) followed by the boxplot. Our experimental results show that our approach is able to increase the reliability and the accuracy of remote monitoring system, while reducing the false alarms triggered by injected measurements.

The amount of data generated by online social networks such as Facebook, Twitter, etc., has recently experienced an enormous growth. Extracting useful information such as community structure, from such large networks is very important in many applications. Community is a collection of nodes, having dense internal connections and sparse external connections. Community detection algorithms aim to group nodes into different communities by extracting similarities and social relations between nodes. Although, many community detection algorithms in literature, they are not scalable enough to handle large volumes of data generated by many of the today's big data applications. So, researchers are focusing on developing parallel community detection algorithms, which can handle networks consisting of millions of edges and vertices. In this article, we present a comprehensive survey of parallel community detection algorithms, which is the first ever survey in this domain, although, multiple papers exist in literature related to sequential community detection algorithms.

Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task. We introduce the KarmaLegoSification (KLS) framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals; (2) mining these symbolic time intervals to discover frequent time-interval-related patterns (TIRPs), using Allen’s temporal relations; and (3) using the TIRPs as features to induce a classifier. To efficiently detect multiple TIRPs (features) in a single entity to be classified, we introduce a new algorithm, SingleKarmaLego, which can be shown to be superior for that purpose over a Sequential TIRPs Detection algorithm. We evaluated the KLS framework on datasets in the domains of diabetes, intensive care, and infectious hepatitis, assessing the effects of the various settings of the KLS framework. Discretization using Symbolic Aggregate approXimation (SAX) led to better performance than using the equal-width discretization (EWD); knowledge-based cut-off definitions when available were superior to both. Using three abstract temporal relations was superior to using the seven core temporal relations. Using an epsilon value larger than zero tended to result in a slightly better accuracy when using the SAX discretization method, but resulted in a reduced accuracy when using EWD, and overall, does not seem beneficial. No feature selection method we tried proved useful. Regarding feature (TIRP) representation, mean duration performed better than horizontal support, which in turn performed better than the default Binary (existence) representation method.

Knowledge distillation-enhanced multitask framework for recommendation

We present a computationally efficient sequential detection scheme using a modified Fano algorithm (MFA) for V-BLAST systems. The proposed algorithm consists of the following three steps: initialization, tree searching, and optimal selection. In the first step, the proposed detection scheme chooses several candidate symbols at the tree level of one. Based on these symbols, the MFA then finds the remaining transmitted symbols from the second tree level in the original tree structure. Finally, an optimal symbol sequence is decided among the most likely candidate sequences searched in the previous step. Computer simulation shows that the proposed scheme yields significant saving in complexity with very small performance degradation compared with that of sphere detection (SD).