The Capture-Recapture approach for population estimation in computer networks

Accettura, Nicola; Neglia, Giovanni; Grieco, Luigi Alfredo

doi:10.1016/j.comnet.2015.07.013

Cited by 7 publications

(9 citation statements)

References 50 publications

(37 reference statements)

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“…In 2016, Hu et al [12] have introduced a strategy with security protection to transmit adaption mechanism. Mainly, a CJ scheme replaces cooperative transmission, when there was no satisfaction in security or QoS constraint.…”

Section: Trusted Detection Of Ransom Ware Using Machine Learning Algomentioning

confidence: 99%

Trusted Detection of Ransom ware using Machine Learning Algorithms

2019

IJITEE

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Nowadays, the Computer Networks and the internet are increased. Lots of information is accessed and allowed to the users to share the information to the Internet. One of the major issues with internet was different types of attack. Ransomware is a one kind of attack or it is malicious software that threatens to publish the victim's data. A variety of threats is the main target for the effective network security and avoids them from spreading or entering to the networks the network security on computer essential for computer networks. Ransom ware is a critical threat in network security since each day the raising of ransomware gets abundant. The major problem by the researchers is the prediction of ransomware. This paper planned to carry out a review on the different method to detect ransomware. Ransomware detection is very much helpful on minimizing the workload of analyst and for determining the variation in hidden Ransomware samples. Using machine learning algorithms Ransomware detected efficiently and trustfully.

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Section: Trusted Detection Of Ransom Ware Using Machine Learning Algomentioning

confidence: 99%

Trusted Detection of Ransom ware using Machine Learning Algorithms

2019

IJITEE

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“…We assume that the destination d is drawn from D according to a distribution δ that depends on either the principal or the client, depending on which type is the ultimate target of a ack. 1 For each destination, we assume that the client selects an existing circuit or builds a new one; we assume that the circuit-selection and -construction decisions are made in an online way, before the next destination is selected/received. Once the circuit (which we denote ci) is chosen/constructed, the client and the destination exchange tra c over the circuit.…”

Section: A2 Dynamics History and Activitymentioning

confidence: 99%

“…Note that the more relays the adversary introduces the more gradually he must introduce them and the more the relays should be in multiple locations if he is to avoid suspicion. Again using (1), if the number of cabal members where B ∈ (0, 1) and m is a positive integer, then the adversary will learn at least one guard of each of the c cabal members. e right column of Fig.…”

Section: 31mentioning

confidence: 99%

“…ese were originally used for species population estimates in biology where it would be impossible to observe the entire population, e.g., estimating the number of sh in a given lake. ey are now used in many se ings, including computer networks [1].…”

Section: Recounting Onions From Our Pastmentioning

confidence: 99%

“…Result Highlights: MTor is a published protocol for multicast over Tor [20]. Its security goals are to "prevent an adversary from (1) discerning the sender of the message and (2) enumerating group members. " We show in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Onions in the Crosshairs

Jaggard

Syverson

2017

Proceedings of the 2017 on Workshop on Privacy in the Electronic Society

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We introduce and investigate targeting adversaries who selectively a ack users of Tor or other secure-communication networks. We argue that a acks by such adversaries are more realistic and more signi cant threats to those most relying on Tor's protection than are a acks in prior analyses of Tor security. Previous research and Tor design decisions have focused on protecting against adversaries who are equally interested in any user of the network. Our adversaries selectively target users-e.g., those who visit a particular website or chat on a particular private channel-and essentially disregard Tor users other than these. We present a model of such adversaries and investigate three example cases where particular users might be targeted: a cabal conducting meetings using MTor, a published Tor multicast protocol; a cabal meeting on a private IRC channel; and users visiting a particular .onion website. In general for our adversaries, compromise is much faster and provides more feedback and possibilities for adaptation than do a acks examined in prior work. We also discuss selection of websites for targeting of their users based on the distribution across users of site activity. We describe adversaries both a empting to learn the size of a cabal meeting online or of a set of su ciently active visitors to a targeted site and a empting to identify guards of each targeted user. We compare the threat of targeting adversaries versus previously considered adversaries, and we brie y sketch possible countermeasures for resisting targeting adversaries.

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Comparison of Confidence Intervals for the TG Estimator in Capture-recapture Data

Anan

Kanjanasamranwong

Chantarangsri

et al. 2019

J. Phys.: Conf. Ser.

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Capture-recapture techniques are very powerful tool and widely used for estimating an elusive target population size. Capture-recapture count data is presented in form of frequencies of frequencies data. They consist of the frequency of unites detected exactly once, twice, and so on, and the frequency of undetected unites is unknown. As consequence, the resulting distribution is a zero-truncated count distribution. The binomial distribution is selected as a simple model if the maximum number of counting occasions is known. It counting occasions are not known in advance, the series of frequencies assumed to be the Poisson distribution. In fact, the target population might be heterogeneous because it has different characteristics, resulting in over or under dispersion based on the basic models. The mixed Poisson, which is the exponential-Poisson mixture model, have been widely used to construct population size estimator for capture-recapture data. The original Turing estimator provides a good performance under the Poisson distribution. Additionally, an extension of Turing estimator, called the Turing-based geometric distribution with non-parametric approach was proposed (TG) for the heterogeneous population. It gives an easy way to estimate the target population size. In this work, we derived uncertainty measures for the TG estimator by considering two sources of variance (M1), and the second way is using only one source of variance (M2). It is emphasised that although the analytic approaches to compute uncertainty measures can be easily used in practice, there are valid asymptotically and requires a large sample size. Therefore, re-sampling approaches, true bootstraps (M3), imputed bootstrap (M4) and reduced bootstrap (M5), are proposed as alternative methods to get uncertainty measures. The study compares performance of variance and confidence interval of paralytics and re-sampling methods by using a simulation study. Overall, the imputed bootstrap is the best choice for estimating variance and constructing confidence interval for the TG estimator. The analytic approach with two sources of variance remains successful to estimate variance and calculate confidence interval in the case of large. It is very clear that the reduced bootstrap and the analytic approach with one source of variance are not appropriate in all situations. For the true bootstrap, the true value of population size is often unknown in nature; therefore, it quite useless for capture-recapture study.

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The Capture-Recapture approach for population estimation in computer networks

Cited by 7 publications

References 50 publications

Trusted Detection of Ransom ware using Machine Learning Algorithms

Trusted Detection of Ransom ware using Machine Learning Algorithms

Onions in the Crosshairs

Comparison of Confidence Intervals for the TG Estimator in Capture-recapture Data

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