A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (2023)

I Introduction

We consider the problem of observing a given set of processes to detect the anomalies among them via controlled sensing. Here, the decision maker does not observe all the processes at each time instant, but sequentially selects and observes one process at a time. The sequential control of the observation process is referred to as controlled sensing. The challenge here is to devise a selection policy to sequentially choose the processes to be observed so that the decision is accurate and fast. This problem arises, for instance, in sensor networks used for remote health monitoring, structural health monitoring, etc[chung2006remote, bujnowski2013enhanced]. Such systems are equipped with different types of sensors to monitor different functionalities (or processes) of the system. The sensors send their measurements to a common decision maker that identifies any potential system malfunction. These sensor measurements can be noisy due to faulty hardware or unreliable communication links. Therefore, to ensure the accuracy of the decision, we employ a sequential process selection strategy that observes the set of processes one at a time over multiple time instants before the final decision is made. Further, the different processes can be dependent on each other, and therefore, observing one process also gives information about other dependent processes. Our goal is to derive a selection policy that accurately identifies the anomalous processes with minimum delay by exploiting the underlying statistical dependence among the processes.

A popular approach for solving the anomaly detection problem is to use the active hypothesis testing framework[zhong2019deep, joseph2020anomaly]

. Here, the decision maker defines a hypothesis corresponding to each of the possible states of the processes and computes the posterior probabilities over the hypothesis set using the observations. The decision maker continues to collect observations until the probability corresponding to one of the hypotheses exceeds the desired confidence level. This framework of active hypothesis testing was introduced by Chernoff in

[chernoff1959sequential], and it was followed by several other studies in the literature[bessler1960theory, nitinawarat2013controlled, naghshvar2013active, huang2018active]. Recently, some researchers have combined the active hypothesis testing framework with deep learning algorithms to design data-driven anomaly detection algorithms[kartik2018policy, zhong2019deep, joseph2020anomaly, joseph2020anomaly2]. These algorithms learn from a training dataset and come with an added advantage of adaptability to the underlying statistical dependence among the processes. The state-of-the-art algorithms in this direction employ reinforcement learning (RL) algorithms such as Q-learning[kartik2018policy] and actor-critic[zhong2019deep, joseph2020anomaly], and the active inference framework[joseph2020anomaly2]. However, the major drawback of this solution strategy is the heavy computational burden that arises due to the large number of hypotheses. Since each process can either be normal or anomalous, the number of hypotheses increases exponentially with the number of processes. Therefore, in this paper, we attempt to devise a learning-based controlled sensing framework for anomaly detection with polynomial complexity in the number of processes.

The specific contributions of the paper are as follows: we first reformulate the problem of anomaly detection in terms of the marginal (not joint) probability of each process being normal or anomalous, conditioned on the observations. Consequently, the number of posterior probabilities computed by the algorithm at every time instant is linear in the number of processes. Based on these marginal posterior probabilities, we define the notion of a confidence level that is proportional to the decision accuracy, and a reward function that monotonically increases with the decision accuracy and decreases with the duration of the observation acquisition phase. These definitions allow us to reformulate the anomaly detection problem as a long-term average reward maximization of a Markov decision process (MDP). This problem is solved using a policy gradient RL algorithm called the actor-critic method, and the algorithm is implemented using deep neural networks. Using numerical results, we show that our algorithm is able to learn and adapt to the statistical dependence among the processes. Further, the polynomial complexity of the algorithms makes it scalable, and hence, practically more useful.

Ii Anomaly Detection Problem

We consider a set of N

processes where the state of each process is a binary random variable. The process state vector is denoted by

s{0,1}N whose ith entry being 0 and 1 indicates that the ith process is in the normal state and the anomalous state, respectively. We aim to detect the anomalous processes, which is equivalent to estimating the random binary vector s.

We estimate the process state vector s by selecting and observing one process at every time instant, and obtaining a state estimate of the corresponding process which has a finite probability of being erroneous. Let the process observed at time k be a(k){1,2,,N} and the corresponding observation be ya(k)(k){0,1}. The uncertainty in the observation is modeled using the following probabilistic model:

ya(k)(k)={sa(k)with probability1p,1sa(k)with probabilityp,(1)

where p[0,1] is called the flipping probability. Further, we assume that conditioned on the value of s, the observations obtained across different timeinstants are jointly (conditionally) independent, i.e., for any k,

P[{yi(l),i=1,2,,N}kl=1s]=Ni=1kl=1P[yi(l)|si].(2)

Therefore, the ith process {yi(k){0,1}}k=1 is a sequence of independent and identically distributed (i.i.d.) binary random variables parameterized by si{0,1}.

After each observation arrives, the decision maker computes an estimate of s along with the confidence in the estimate. The decision maker continues to observe the processes until the confidence exceeds the desired level denoted by πupper(0,1). Therefore, we have two interrelated tasks: one, to develop an algorithm to estimate the process state vector and the associated confidence in the estimate; and two, to derive a policy that decides the process to be observed at each time instant and the criterion to stop collecting observations. We seek the estimation algorithm and the policy that jointly minimize the stopping time K while maximizing the accuracy level. Here, the stopping time refers to the time instant at which the observation acquisition phase ends. We next present our estimation algorithm and policy design.

Iii Estimation Algorithm

In this section, we derive an algorithm to estimate the process state vector from the observations. We note that the observations depend on the selection policy, and the policy design, in turn, depends on the estimation algorithm. Therefore, we first present the estimation algorithm and then derive a selection policy based on the estimation objectives in the next section.

To estimate the process state vector, we first compute the belief vector σ(k)[0,1]N at time k whose ith entry σi(k) is the posterior probability that the ith process is normal (si=0). Therefore, the probability that the ith process is anomalous (si=1) is 1σi(k). As each observation arrives, we recursively update the belief vector as follows.

σi(k)=P[si=0{ya(l)(l)}kl=1]
=P[{ya(l)(l)}kl=1si=0]P[si=0]P[{ya(l)(l)}kl=1].(3)

Here, we approximate the joint probability distribution by assuming that the observation

ya(k)(k) is independent of the past observations {ya(l)(l)}k1l=1 conditioned on the process state si:

(Video) Anomaly Detection: Algorithms, Explanations, Applications

P[{ya(l)(l)}kl=1si=0]P[si=0]
=σi(k1)P[{ya(l)(l)}k1l=1]P[ya(k)(k)si=0].(4)

From (2), the observation ya(k)(k) is independent of all other observations, conditioned on the value of sa(k).Therefore, the approximation is exact when sa(k) is a deterministic function of si. Some examples of such cases are P[sa(k)=si]=1, and P[sa(k)=1si]=1.

Substituting (4) into (3), we obtain

σi(k)=σi(k1)P[ya(k)(k)si=0]Σi(k),(5)

Here, following the approximation in (4), the normalization constant is

Σi(k)=σi(k1)P[ya(k)(k)si=0]+(1σi(k1))P[ya(k)(k)si=1].(6)

Further, the conditional probability P[ya(k)(k)si=s] for s=0,1 is given by

P[ya(k)(k)si=s]
=s=0,1P[ya(k)(k)sa(k)=s]P[sa(k)=ssi=s]
=s=0,1[psya(k)(k)(1p)1sya(k)(k)
×P[sa(k)=ssi=s]],(7)

which follows from (1). Here, the term P[sj=ssi=s] can be easily estimated from the training data111During the training phase, the true value of s is provided, but the optimal selection at each time instant is unknown.for every pair (i,j). Hence, (5), (6), and (7) give the recursive update of σ(k).

We note that when sa(k) and si are independent processes,

P[ya(k)(k)si=s]=P[ya(k)(k)]s=0,1.(8)

Consequently, (5) reduces to σi(k)=σi(k1). This update is intuitive since an observation from process sa(k) does not change the probabilities associated with an independent process si. In other words, the recursive relation is exact when si and sa(k) are either independent or sa(k) can be exactly determined from si. We discuss this point in detail in Sec. VI.

Once σ(k) is obtained, the computation of the process state vector estimate denoted by ^s(k) is straightforward:

^si={0ifσi(k)1σi(k)1ifσi(k)<1σi(k).(9)

Hence, the derivation of the estimation algorithm is complete. We next discuss the design of the selection policy.

Iv Selection Policy

The design of the selection policy is a sequential decision making problem, and therefore, this problem can be formulated using the mathematical framework of an MDP. This formulation allows us to obtain the selection policy via reward maximization of the MDP using RL algorithms. In the following subsections, we define the MDP framework and describe the RL algorithm using the deep actor-critic method.

Iv-a Markov Decision Process

An MDP has four components: state space, action space, state transition probabilities, reward function. In our case, these components are defined as follows:

  • [leftmargin=0.3cm]

  • MDP state: Our estimation algorithm is based on the belief vector σ(k) that changes with time after each observation arrives. Therefore, we define σ(k)[0,1]N as the state of the MDP at time k. We note that the MDP state vector σ(k) is different from the process state vectors.

  • Action: The state of MDP depends on the observation which in turn depends on the process selected by the policy. Thus, the action taken by the decision maker at time instant k is the selected process a(k){1,2,,N}.

  • MDP State Transition: For our problem, the MDP state σ(k) at time k is a deterministic function of the previous MDP state σ(k1), the action a(k), and the observation ya(k)(k). Therefore, the MDP state transition is modeled by (5), (6), and (7).

    (Video) Anomaly detection with TensorFlow | Workshop

  • Reward Function: We seek a policy that maximizes the decision accuracy and minimizes the stopping time K. Here, we capture the decision accuracy using the uncertainty associated with each process conditioned on the observations. The uncertainty associated with the ith process can be quantified using the entropy of its posterior distribution [σi(k)1σi(k)]. Therefore, the instantaneous reward of the MDP is

    r(k)=Ni=1H(σi(k1))H(σi(k)),(10)

    where H(x)=xlogx(1x)log(1x) is the entropy. Then, the long term reward can be defined as the expected discounted reward of the MDP:¯R(k)=Kl=kγlkr(l),where γ(0,1) is the discount factor. The discounted reward formulation implies that a reward received l time steps in the future is worth only γl times what it would be worth if it were received immediately. Thus, this formulation minimizes the stopping time.

Having defined the MDP, we next describe the actor-critic RL algorithm that solves the long-term average reward maximization problem.

Iv-B Deep Actor-Critic Algorithm

The deep actor-critic algorithm is a deep learning-based RL technique that provides a sequential policy that maximizes the long-term expected discounted reward ¯R(k) of a given MDP. The actor-critic framework maximizes the discounted reward using two neural networks: actor and critic networks. The actor learns a stochastic policy that maps the state of the MDP to a probability vector on the set of actions. The critic learns a function that evaluates the policy followed by the actor and gives feedback to the actor. Therefore, the two neural networks interact and adapt to each other.

The components of the actor-critic algorithm are as follows:

  • [leftmargin=0cm]

  • Actor Network: The actor takes the state of the MDP σ(k1)[0,1]N as its input. Its output is the probability vector μ(σ(k1);α)[0,1]N over the set of processes where α denotes the set of parameters of the actor neural network. The decision maker selects a process a(k)μ(σ(k1);α), i.e., the ith process is selected at time k with probability equal to the ith entry μi(σ(k1);α) of the actor output.

  • Reward Computation: Once the process a(k) is selected, the decision maker receives the corresponding observation ya(k), and the MDP state σ(k1) is updated to σ(k) as given by (5). The decision maker also calculates the instantaneous reward r(k) using (10), and the reward value is fed to the critic along with the current and previous states of the MDP.

  • Critic Network: The input to the critic at time k is given by

    θ(k)=(σ(k),σ(k1),r(k))[0,1]N×[0,1]N×R.

    The output of the critic is a scalar critique δ(θ(k);β) where β denotes the set of parameters of the critic neural network. This critique is computed based on the value function V(σ(k)) of the current MDP state as defined below:

    Vμ(σ)=Ea(k)μ{¯R(k)σ(k)=σ}.

    We note that Vμ(σ) is the expected average future reward when the MDP starts at state σ and follows the policy μ(;θ) thereafter. In other words, Vμ(σ) indicates the long term desirability of the MDP being in state σ. The scalar critique takes the form of a temporal difference (TD) error δ(θ(k);β)

    δ(θ(k);β)=r(k)+γ^V(σ(k))^V(σ(k1)),(11)

    where ^V is the value function estimate learned by the critic. A positive TD error indicates that the probability of choosing the current action should be increased for the future, and a negative TD error suggests that the probability of choosing a(k) should be decreased.

  • Learning Actor Parameters: The goal of the actor is to choose a policy such that the value function is maximized which in turn maximizes the expected average future reward. Therefore, the actor updates its parameter set α using the gradient descent step by moving in the direction in which the value function is maximized. The update equation for the actor parameters is given by

    α=α+δ(θ(k);β)α[logμa(k)(σ(k1);α)],(12)

    where α is the estimate of the network obtained in the previous time instant[sutton2018reinforcement, Chapter 13].

  • Learning Critic Parameters: The critic chooses its parameters such that it learns the estimate ^V() of the state value function V() accurately. Therefore, the critic updates its parameter set β by minimizing the square of the TD error δ2(θ(k);β).

  • Termination criterion: The actor-critic algorithm continues to collect observations until the confidence level on the decision exceeds the desired level πupper. We define the confidence level on ^si as max{σi(k),1σi(k)}. Therefore, the stopping criterion is as follows:

    mini=1,2,,Nmax{σi(k),1σi(k)}>πupper.(13)
    (Video) Lecture 15.1 — Anomaly Detection Problem | Motivation — [ Machine Learning | Andrew Ng ]

The above components completely describe the actor-critic algorithm, and we next summarize the overall algorithm and discuss its complexity.

V Overall Algorithm

Combing the estimation algorithm in Sec. III and the deep actor-critic method in Sec. IV, we obtain our anomaly detection algorithm. The decision maker collects observations using the selection policy obtained using the actor-critic algorithm until the stopping criterion given in (13) is satisfied. After the actor-critic algorithm terminates, the decision maker computes ^s using (9). We present the pseudo-code of the overall procedure in Algorithm 1.

0:Discount rate γ(0,1), Upper threshold on confidence πupper(0.5,1)

0:α,β with random weights, σ(0) with the prior on each process (can be learned from the training data)

1:forEpisode index =1,2,do

2:Time index k=1

3:repeat

4:Choose a process a(k)μ(σ(k1),α)

5:Receive observation ya(k)(k)

6:Compute σ(k) using (5) - (7)

7:Compute instantaneous reward r(k) using (10)

8:Update the actor neural network using (12)

9:Update the critic neural network by minimizing the temporal error δ in (11) with respect to β

10:Increase time index k=k+1

11:until(13) is satisfied

12:Declare the estimate ^s using (9)

13:endfor

The computational complexity of our algorithm is determined by the size of the neural networks, the update of the posterior belief vector given by (5)-(7), and the reward computation given by (10). Since all of them have linear complexity in the number of processes N, the overall computational complexity of our algorithm is polynomial in N. Also, the sizes of all the variables involved in the algorithm are linear in N except for the pairwise conditional probability P[sisj] for i,j=1,2,,N. Therefore, the memory requirement of the algorithm is O(N2). Hence, our algorithm possesses polynomial complexity, unlike the anomaly detection algorithms in [joseph2020anomaly, joseph2020anomaly2] that have exponential complexity in N. Consequently, our algorithm is more applicable in practical settings.

It is straightforward to extend our algorithm to the case in which the decision maker chooses n processes at a time. In that case, the output layer of the actor has (Nn)neurons, and we need to update σ(k)[0,1]N using the conditional probabilities of the form P[si1,si2,,sinsj], for 1<i1<i2<i3<<N and j=1,2,,N. Therefore, the overall computational complexity of the resulting algorithm is polynomial in N and the memory requirement is O(Nn+1).

A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (1)A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (2)
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (3)A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (4)
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (5)A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (6)
A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (7)A Scalable Algorithm for Anomaly Detection via Learning-Based Controlled Sensing (8)

Vi Simulation Results

In this section, we empirically study the detection performance of our algorithm. We use two metrics for the performance evaluation: accuracy (the fraction of times the algorithm correctly identifies all the anomalous processes) and stopping time.

Vi-a Simulation Setup

Our simulation setup is as described below:

  • [leftmargin=0cm]

  • Processes and Their Statistical Dependence: We consider five processes N=5 and assume that the probability of each process being normal is q=0.8. Here, the first and second processes (s1 and s2) are statistically dependent, and the third and fourth processes (s3 and s4) are also statistically dependent. These pairs of processes are independent of each other and independent of the fifth process (s5). The dependence is captured using the correlation coefficient ρ[0,1] that is common to both process pairs:

    P[s1=s2=0]=P[s3=s4=0]=q2+ρq(1q)
    P[s1s2]=P[s3s4]=(1ρ)q(1q)

    Also, we assume that the flipping probability p=0.2.

  • Implementation of Our Algorithm:

    We implement the actor and critic neural networks with three layers and the ReLU activation function between consecutive layers. The output layer of the actor layer is normalized to ensure that

    μ() is a probability vector over the set of processes. The parameters of the neural networks are updated using the Adam Optimizer, and we set the learning rates of the actor and the critic as 5×104, and 5×103, respectively. Also, we set the discount factor γ=0.9.

  • Competing Algorithms: We compare the performance of our algorithm with two other deep actor-critic-based algorithms:

    1. [leftmargin=0.3cm]

    2. Joint probability mass function (pmf)-based algorithm: This algorithm refers to the state-of-the-art method for anomaly detection problem presented in [joseph2020anomaly]. The algorithm is based on the joint posterior probabilities of all the entries of s[0,1]N. Since s can take 2N possible values, the complexity of this algorithm is 2N. However, the joint probabilities help the algorithm to learn all possible statistical dependencies among the process.

    3. Naive marginal pmf-based algorithm: We also compare our algorithm with a naive method that also relies on the marginal probabilities σ[0,1]N. This algorithm is identical to our algorithm except that at every time instant, this method only updates the entry of σa(k)(k) of σ(k) corresponding to the selected process a(k). In other words, this method ignores the possible statistical dependence of the observation ya(k)(k) on the processes other than a(k). Hence, the computational complexity of this algorithm is also O(N). We note that unlike our algorithm, this algorithm does not use any approximation, and therefore, its updates are always exact.

    Our algorithm is a compromise between the above two algorithms and relies on marginal probabilities σ while accounting for the possible statistical dependence among the processes.

(Video) "Scalable Anomaly Detection (with Zero Machine Learning)" by Arthur Gonigberg

Vi-B Discussion of Results

Our results are summarized in Figs. 2 and1 and the key inferences from them are as follows:

  • [leftmargin=0.3cm]

  • The accuracy and the stopping time of all the algorithms increase with πupper. This trend is expected due to the fact that as πupper increases, the decision maker requires more observations to satisfy the higher desired confidence level.

  • The accuracy of our algorithm is comparable to the other two algorithms when ρ=0 and ρ=1. The accuracy degrades as ρ is close to 0.5. This behavior is because our algorithm uses approximate marginal probabilities to compute the confidence level whereas the other two algorithms use exact values. This approximation in (4) is exact when ρ=0 and ρ=1. As ρ approaches 0.5, the approximation error increases, and the accuracy decreases.

  • The stopping times of the three algorithms are similar when ρ=0. This is because when ρ=0, all the processes are independent. Therefore, the updates of our algorithm are exact. The naive marginal pmf-based algorithm also offers good performance as there is no underlying statistical structure among the processes.

  • The stopping times of our algorithm and the joint pmf-based algorithm improve with ρ. As ρ increases, the processes become more correlated, and therefore, an observation corresponding to one process has more information about the other correlated processes. However, the naive marginal pmf-based algorithm ignores this correlation and handles the observations corresponding to the different processes independently. Therefore, the stopping time is insensitive to ρ. Consequently, the difference between the stopping times of the naive marginal pmf-based algorithm and the other two algorithms increases as ρ increases.

Further, from our experiments, we notice that the average runtime per per process selection decision for the joint pmf-based algorithm, our algorithm, and the naive marginal pmf-based algorithm are 3.2ms, 2.88ms, 2.89ms, respectively. This observation is in agreement with our complexity analysis in Sec. V which implies that the joint pmf-based algorithm is computationally heavier compared to the other two algorithms. We also recall that the difference between the runtimes of the joint pmf based algorithm and our algorithm grows with N.

Thus, we conclude that our algorithm combines the best of two worlds by benefiting from the statistical dependence among the processes (similar to the joint pmf-based algorithm) and offering low-complexity (similar to the naive marginal pmf-based algorithm).

Vii Conclusion

We presented a low-complexity algorithm to detect the anomalous processes among a set of binary processes by observing a single process at a time. The sequential process selection problem was formulated using a Markov decision process whose reward is defined using the entropy of the marginal probabilities of the processes. The optimal process selection policy was obtained via the deep actor-critic algorithm that maximizes the long-term average reward of the MDP. Using numerical results, we established that our algorithm learns and adapts to the underlying statistical dependence among the processes while operating with low complexity. This algorithm relies on approximate marginal probabilities which can lead to performance deterioration when the approximation error is large. A theoretical analysis that quantifies the approximation error is an interesting direction for future work.

(Video) Anomaly detection using Isolation Forest - Contextual Anomalies

References

    FAQs

    What are the three 3 basic approaches to anomaly detection? ›

    There are three main classes of anomaly detection techniques: unsupervised, semi-supervised, and supervised. Essentially, the correct anomaly detection method depends on the available labels in the dataset.

    What is a suggested algorithm that would be appropriate for anomaly detection related to identifying unusual activities in network activities or data? ›

    If you are specifically interested in Network/Graph analytics, the two main methods used for identifying anomalies in network graphs are the Direct Neighbour Outlier Detection Algorithm (DNODA) and Community Neighbour Algorithm (CNA).

    What happens in anomaly detection answer? ›

    Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset's normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance, a change in consumer behavior.

    What happens in anomaly detection Mcq? ›

    What happens in anomaly detection? - ' b)Build Machine Learning algorithms ' is the correct answer. 'Anomaly detection' is the 'identification of rare events ', 'items', or 'observations' that are 'suspicious' as a result of they take issue considerably from customary behaviors or patterns.

    Which algorithm is best for anomaly detection? ›

    Local outlier factor is probably the most common technique for anomaly detection. This algorithm is based on the concept of the local density. It compares the local density of an object with that of its neighbouring data points. If a data point has a lower density than its neighbours, then it is considered an outlier.

    What are the characteristics of anomaly detection? ›

    Characteristics of Anomaly Detection Problem
    • Processing type: There are off-line and on-line processing types. ...
    • Data: Although the data is often classified into structured, semi-structured, and unstructured types (details here), it is more convenient to consider data being pre-processed and transformed into ready-for-ML.
    Sep 21, 2021

    Which of these is not the type of anomaly check update delete select Insert? ›

    The correct answer is 'False'.

    Which of the following machine learning techniques helps in detecting the outliers in data? ›

    12. Which of the following machine learning techniques helps in detecting the outliers in data? Answer - C) The machine learning algorithm which helps in detecting the outliers is known as anomaly detection.

    What is the purpose of anomaly detection? ›

    Anomaly detection aims at finding unexpected or rare events in data streams, commonly referred to as anomalous events. Detecting anomalies could be useful directly or as a first insight to find new knowledge in the data.

    What is an example of an anomaly? ›

    An anomaly is an abnormality, a blip on the screen of life that doesn't fit with the rest of the pattern. If you are a breeder of black dogs and one puppy comes out pink, that puppy is an anomaly.

    What is an advantage of the anomaly detection method? ›

    The benefits of anomaly detection include the ability to: Monitor any data source, including user logs, devices, networks, and servers. Rapidly identify zero-day attacks as well as unknown security threats. Find unusual behaviors across data sources that are not identified when using traditional security methods.

    Which of the following is an advantage of anomaly detection Mcq? ›

    Which of the following is an advantage of anomaly detection? Explanation: Once a protocol has been built and a behavior defined, the engine can scale more quickly and easily than the signature-based model because a new signature does not have to be created for every attack and potential variant.

    What is the change detection problem in anomaly detection? ›

    Abstract. For the anomalous change detection problem, you have a pair of images, taken of the same scene, but at differ- ent times and typically under different viewing conditions. You are looking for interesting differences between the two images.

    Which method is used for encoding the categorical variables Mcq? ›

    Binary Encoding

    In this encoding scheme, the categorical feature is first converted into numerical using an ordinal encoder. Then the numbers are transformed in the binary number. After that binary value is split into different columns.

    Which of the following are the examples of anomaly detection? ›

    Applications of Anomaly Detection in the Business world. Intrusion detection, example identifies strange patterns in the network traffic (that could signal a hack). Health monitoring system in the hospital. Fraud detection in credit card transactions in Banks.

    Which algorithms can be used for Misuse Detection and anomaly detection? ›

    Machine learning algorithms can be very effective in building normal profiles and then in designing intrusion detection systems based on anomaly detection approach.

    What is anomaly detection in AI? ›

    Anomaly detection is a technique that uses AI to identify abnormal behavior as compared to an established pattern. Anything that deviates from an established baseline pattern is considered an anomaly. Dynatrace's AI autogenerates baseline, detects anomalies, remediates root cause, and sends alerts.

    What are the different types of anomalies? ›

    Anomalies can be classified into the following three categories:
    • Point Anomalies. If one object can be observed against other objects as anomaly, it is a point anomaly. ...
    • Contextual Anomalies. If object is anomalous in some defined context. ...
    • Collective Anomalies.
    Apr 10, 2018

    Is anomaly detection a classification problem? ›

    Using anomaly detection, no region in data space with a good number of observations from the reference class will be classified as anomaly, so this means that in your two class problem the minority class cannot be found anywhere where the majority class is present strongly enough.

    How can we prevent anomaly? ›

    The simplest way to avoid update anomalies is to sharpen the concepts of the entities represented by the data sets. In the preceding example, the anomalies are caused by a blending of the concepts of orders and products. The single data set should be split into two data sets, one for orders and one for products.

    Which of the following is a difficulty in anomaly detection? ›

    Challenges in anomaly detection include appropriate feature extraction, defining normal behaviors, handling imbalanced distribution of normal and abnormal data, addressing the variations in abnormal behavior, sparse occurrence of abnormal events, environmental variations, camera movements, etc.

    Which is the following is a positive outcome in anomaly detection? ›

    Perhaps the most significant benefit of anomaly detection is the automation of KPI analysis. For most businesses, KPI analysis is still a manual task of sorting through all of their digital channel's data across different dashboards.

    What are the three data anomalies that are likely to occur as a result of data redundancy? ›

    There are three types of anomalies: update, deletion, and insertion anomalies. An update anomaly is a data inconsistency that results from data redundancy and a partial update.

    What are 3 anomalies resolved by normalization? ›

    Normalization ensures that all three challenges (update, insert, and delete anomalies), as well as any others that may arise, are addressed during the design process.

    Which of the following anomaly may occur while inserting a new data record? ›

    An insertion anomaly occurs when you are inserting inconsistent information into a table. When we insert a new record, such as account no.

    Which of the following are the purpose of testing in machine learning Mcq? ›

    This is Expert Verified Answer

    Explanation: In Machine Learning testing, the programmer enters input and observes the behavior and logic of the machine. hence, the purpose of testing machine learning is to elaborate that the logic learned by machine remain consistent.

    What is supervised learning * Mcq? ›

    Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output.

    What is true about machine learning Mcq? ›

    ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. C. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention. Explanation: All statement are true about Machine Learning.

    Which machine learning technique can be used for anomaly detection ai900? ›

    Computer vision. Machine Learning (Regression)

    What exactly is an anomaly? ›

    noun, plural a·nom·a·lies. a deviation from the common rule, type, arrangement, or form. an anomalous person or thing; one that is abnormal or does not fit in: With his quiet nature, he was an anomaly in his exuberant family. an odd, peculiar, or strange condition, situation, quality, etc.

    How do you explain an anomaly? ›

    anomaly
    1. 1 : something different, abnormal, peculiar, or not easily classified : something anomalous They regarded the test results as an anomaly.
    2. 2 : deviation from the common rule : irregularity.
    3. 3 : the angular distance of a planet from its perihelion as seen from the sun.

    What is the meaning of anomaly in science? ›

    In the natural sciences, especially in atmospheric and Earth sciences involving applied statistics, an anomaly is a persisting deviation in a physical quantity from its expected value, e.g., the systematic difference between a measurement and a trend or a model prediction.

    What are the three 3 basic approaches to anomaly detection? ›

    There are three main classes of anomaly detection techniques: unsupervised, semi-supervised, and supervised. Essentially, the correct anomaly detection method depends on the available labels in the dataset.

    Which of the following algorithm can be used best in the case of detecting anomalous events in a trading system? ›

    Isolation Forest is one of the ML algorithms used for unsupervised anomaly detection using anomaly scoring.

    What is the key limitation of the machine learning based anomaly detection? ›

    One of the most important limitations of ID algorithms is real-time traffic analysis. The information system is potentially exposed to an intrusion risk if real-time traffic detection is inaccurate. Efficiency and speed are the main issues in anomaly detection systems.

    What happens in anomaly detection Mcq? ›

    What happens in anomaly detection? - ' b)Build Machine Learning algorithms ' is the correct answer. 'Anomaly detection' is the 'identification of rare events ', 'items', or 'observations' that are 'suspicious' as a result of they take issue considerably from customary behaviors or patterns.

    What is a suggested algorithm that would be appropriate for anomaly detection related to identifying unusual activities in network activities or data? ›

    If you are specifically interested in Network/Graph analytics, the two main methods used for identifying anomalies in network graphs are the Direct Neighbour Outlier Detection Algorithm (DNODA) and Community Neighbour Algorithm (CNA).

    How does network intrusion detection system works Mcq? ›

    Explanation: They are constantly updated with attack-definition files (signatures) that describe each type of known malicious activity. They then scan network traffic for packets that match the signatures, and then raise alerts to security administrators.

    What is the best anomaly detection algorithm? ›

    Local outlier factor is probably the most common technique for anomaly detection. This algorithm is based on the concept of the local density. It compares the local density of an object with that of its neighbouring data points. If a data point has a lower density than its neighbours, then it is considered an outlier.

    Which technique is used for anomaly detection? ›

    Some of the popular techniques are: Statistical (Z-score, Tukey's range test and Grubbs's test) Density-based techniques (k-nearest neighbor, local outlier factor, isolation forests, and many more variations of this concept) Subspace-, correlation-based and tensor-based outlier detection for high-dimensional data.

    How do you detect anomaly detection? ›

    How to detect Anomalies? Simple statistical techniques such as mean, median, quantiles can be used to detect univariate anomalies feature values in the dataset. Various data visualization and exploratory data analysis techniques can be also be used to detect anomalies.

    Which of the following are techniques to accelerate learning Mcq? ›

    Q: Which of the following are techniques to accelerate learning ?
    • Pilots and PoCs.
    • Job rotation.
    • monitoring and feedback.
    • All of the above.

    What is the use of hidden Markov model Mcq? ›

    Explanation: Hidden Markov model is used for solving temporal probabilistic reasoning that was independent of transition and sensor model.

    Which of the following clustering algorithm follows a top to bottom approach Mcq? ›

    a) Partitionalb) Hierarchicalc) Naive bayesd) None of the mentionedView AnswerAnswer: bExplanation: Hierarchical clustering groups data over a variety of scales by creating acluster tree or dendrogram. 2.

    What are different types of anomalies in database? ›

    There are three types of anomalies: update, deletion, and insertion anomalies.

    What are the characteristics of anomaly detection? ›

    Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers, discordant observations, exceptions, aberrations, surprises, peculiarities, or contaminants in different application domains [2].

    What is anomaly detection example? ›

    A single instance of data is anomalous if it deviates largely from the rest of the data points. An example is Detecting credit card fraud based on “amount spent.”

    What is meant by anomaly detection? ›

    Anomaly detection is the process of finding outliers in a given dataset. Outliers are the data objects that stand out amongst other objects in the dataset and do not conform to the normal behavior in a dataset.

    What are examples of anomalies? ›

    The definition of anomalies are people or things that are abnormal or stray from the usual method or arrangement. Proteus Syndrome, skin overgrowth and unusual bone development, and Hutchinson-Gilford Progeria Syndrome, the rapid appearance of aging in childhood, are both examples of medical anomalies.

    Which of these is not the type of anomaly check update delete select insert? ›

    The correct answer is 'False'.

    What are the 3 types of anomalies that could find in a not normalized database? ›

    There are three types of anomalies that occur when the database is not normalized. These are – Insertion, update and deletion anomaly.

    Why is anomaly detected? ›

    Anomaly detection can solve many business problems. In the world of finance, detecting anomalies can often lead to the prevention of fraudulent transactions. Fraud transactions can cause huge losses. Hence, noticing them as fast and as efficiently as possible becomes crucial.

    What is an advantage of the anomaly detection method? ›

    The benefits of anomaly detection include the ability to: Monitor any data source, including user logs, devices, networks, and servers. Rapidly identify zero-day attacks as well as unknown security threats. Find unusual behaviors across data sources that are not identified when using traditional security methods.

    What are 3 things that can be anomalies? ›

    Anomalies can be classified into the following three categories:
    • Point Anomalies. If one object can be observed against other objects as anomaly, it is a point anomaly. ...
    • Contextual Anomalies. If object is anomalous in some defined context. ...
    • Collective Anomalies.
    Apr 10, 2018

    Which type of analytics is used to detect anomalies? ›

    About Anomaly Detection

    Analytics Intelligence Anomaly Detection is a statistical technique to identify “outliers” in time-series data for a given dimension value or metric. First, Intelligence selects a period of historic data to train its forecasting model.

    What is anomaly detection in AI? ›

    Anomaly detection is a technique that uses AI to identify abnormal behavior as compared to an established pattern. Anything that deviates from an established baseline pattern is considered an anomaly. Dynatrace's AI autogenerates baseline, detects anomalies, remediates root cause, and sends alerts.

    Which of the following algorithm can be used best in the case of detecting anomalous events in a trading system? ›

    Isolation Forest is one of the ML algorithms used for unsupervised anomaly detection using anomaly scoring.

    What is the change detection problem in anomaly detection? ›

    Abstract. For the anomalous change detection problem, you have a pair of images, taken of the same scene, but at differ- ent times and typically under different viewing conditions. You are looking for interesting differences between the two images.

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