The characteristics of anonymous communication, such as difficulty in node discovery, service positio-ning, communication relationship confirmation, and user monitoring, make the darknet built on it ...full of various illegal and criminal activities of anonymous abuse. To this end, the academic community has carried out a series of targeted research around anonymous communication and darknet. Accordingly, on the basis of systematically intro-ducing the development history of anonymous communication, anonymous mechanisms and typical systems, this paper focuses on combing, summarizing and inducing related research in this field by combining key technologies of anonymous communication, anonymity measurement, anonymous attack, anonymous enhancement, anonymous communication performance evaluation and improvement and darknet space comprehensive governance. Mean-while, this paper focuses and analyzes the development trend of anonymous communication research in the future and the challenges and countermeasures faced by
Ransomware is an epidemic that adversely affects the lives of both individuals and large companies, where criminals demand payments to release infected digital assets. In the wake of the ransomware ...success, Ransomware-as-a-Service (RaaS) has become a franchise offered through darknet marketplaces, allowing aspiring cybercriminals to take part in this dubious economy. We have studied contemporary darknet markets and forums over a period of two years using a netnographic research approach. Our findings show that RaaS currently seems like a modest threat relative to popular opinion. Compared to other types of illegal digital goods, there are rather few RaaS items offered for sale in darknet marketplaces, often with questionable authenticity. From our data we have created a value chain and descriptions of the actors involved in this economy.
Humans can recognize and classify shapes, names, and provide responses to object that are received by visually quickly and accurately. More importantly, it is expected that the system created is able ...to help provide response in all tasks and time, for example when driving, walking in the crowd even when patrolling as a member of the military on dangerous terrain.This has become a problem in the system used on the battlefield. In the proposed system, the object detection model must be able to sort out the objects of armed humans (militia) with unarmed human objects. To overcome the problem the author uses the YOLO transfer learning algorithm which currently has the third version. It is stated that YOLOv3 has very extreme speed and accuracy. In mean Average Precision (mAP) obtained by 0.5 IOU, YOLOv3 is equivalent to 4x faster than Focal Loss. Moreover, YOLOv3 also offers optimal speed and accuracy simply by changing the size of the model, without the need for retraining.
DOI: https://doi.org/10.26905/jtmi.v6i1.4025
•Automated classification of the pap-smear image using exemplar pyramid deep feature extraction technique.•A shallow classifier(Cubic SVM) with high classification ability.•Two cervical cancer ...datasets (SIPaKMeD and Mendeley LBC) are used for pap-smear image classification.•Obtained 98.26% and 99.47% accuracies for SIPaKMeD and Mendeley LBC datasets respectively.
Cervical cancer is a common type of cancer in women worldwide. Detection of this type of cancer in the early stages is very important for the treatment process. Early diagnosis/detection is very important for the treatment of cervical cancer. The golden standard of diagnosing cervical cancer is the pap-smear test. To automatically diagnose cervical cancer, machine learning is a good solution and many computer vision/deep learning-based models have been presented in the literature.
In this study, an exemplar pyramid deep feature extraction-based method has been proposed for the detection of cervical cancer. The prime purpose of our proposal is to classify cervical cells in pap-smear images for the detection of cancer. SIPaKMeD and Mendeley Liquid Based Cytology (LBC) datasets have been used to develop our exemplar pyramid deep feature generator. The phases/steps of the proposed exemplar pyramid structure-based model are; (i) transfer learning-based feature extraction using DarkNet19 or DarkNet53 networks in an exemplar pyramid structure and the proposed feature generator creates 21,000 features. By deploying Neighborhood Component Analysis (NCA), the most informative/weighted 1000 features from the generated 21,000 features. The selected 1000 features by NCA are classified with the Support Vector Machine (SVM) algorithm. Both 5-fold cross-validation and hold-out validation (80:20) have been utilized as validation techniques. The best accuracies for the SIPaKMeD and Mendeley LBC datasets have been computed as 98.26% and 99.47%, respectively. The obtained results illustrate that the proposed exemplar pyramid model is successful to diagnose cervical cancer using pap-smear images.
The malicious activities in the darknet are an emerging threat to the cyber space. Darknet sites operate using TOR(The Onion Router) hidden services which provides the feature of disguising the users ...of the transaction in the darknet market place. Hence identifying and monitoring such illegal activities in the marketplace has become a tedious task for the cyber and law enforcement officials. This paper presents a prototype for a framework which analyse the traffic flow in the darknet as finding the exact sender and receiver is almost impossible as the TOR is increasing the layers of security to the maximum extent making it impossible to track the users in the transactions. Here we give a methodology using webcrawlers and extract the data from the darknet sites to find the domain of the traffic flow through which the broad area of traffic can be sorted out which would be beneficial for the cyber and law enforcement agencies to find the illicit trade in the darknet market places
With the development of the Internet, people have paid more attention to privacy protection, and privacy protection technology is widely used. However, it also breeds the darknet, which has become a ...tool that criminals can exploit, especially in the fields of economic crime and military intelligence. The darknet detection is becoming increasingly important; however, the darknet traffic is seriously unbalanced. The detection is difficult and the accuracy of the detection methods needs to be improved. To overcome these problems, we first propose a novel learning method. The method is the Chebyshev distance based Between-class learning (CDBC), which can learn the spatial distribution of the darknet dataset, and generate "gap data". The gap data can be adopted to optimize the distribution boundaries of the dataset. Second, a novel darknet traffic detection method is proposed. We test the proposed method on the ISCXTor 2016 dataset and the CIC-Darknet 2020 dataset, and the results show that CDBC can help more than 10 existing methods improve accuracy, even up to 99.99%. Compared with other sampling methods, CDBC can also help the classifiers achieve higher recall.
•An unsupervised text summarization framework based on deep neural networks.•Vector representation of sentences using recurrent neural networks.•Summary generated using three sentence features ...relevance, novelty and position.•Deep auto-encoders are exploited for computing sentence content relevance.•A new text summarization dataset is introduced from darknet domains.
In this paper, we propose SummCoder, a novel methodology for generic extractive text summarization of single documents. The approach generates a summary according to three sentence selection metrics formulated by us: sentence content relevance, sentence novelty, and sentence position relevance. The sentence content relevance is measured using a deep auto-encoder network, and the novelty metric is derived by exploiting the similarity among sentences represented as embeddings in a distributed semantic space. The sentence position relevance metric is a hand-designed feature, which assigns more weight to the first few sentences through a dynamic weight calculation function regulated by the document length. Furthermore, a sentence ranking and a selection technique are developed to generate the document summary by ranking the sentences according to the final score obtained through the fusion of the three sentences selection metrics. We also introduce a new summarization benchmark, Tor Illegal Documents Summarization (TIDSumm) dataset, especially to assist Law Enforcement Agencies (LEAs), that contains two sets of ground truth summaries, manually created, for 100 web documents extracted from onion websites in Tor (The Onion Router) network. Empirical results show that, on DUC 2002, on Blog Summarization, and on TIDSumm datasets, our text summarization approach obtains comparable or better performance than the state-of-the-art methods for different ROUGE metrics.
The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet ...traffic in an attempt to block these criminal activities. This research aims to improve darknet traffic detection by assessing a wide variety of machine learning and deep learning techniques for the classification of such traffic and for classification of the underlying application types. We find that a Random Forest model outperforms other state-of-the-art machine learning techniques used in prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our Random Forest classifier, we obfuscate select application type classes to simulate realistic adversarial attack scenarios. We demonstrate that our best-performing classifier can be degraded by such attacks, and we consider ways to effectively deal with such adversarial attacks.
Functional classification of bitcoin addresses Febrero-Bande, Manuel; González-Manteiga, Wenceslao; Prallon, Brenda ...
Computational statistics & data analysis,
20/May , Letnik:
181
Journal Article
Recenzirano
Odprti dostop
A classification model for predicting the main activity of bitcoin addresses based on their balances is proposed. Since the balances are functions of time, methods from functional data analysis are ...applied; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. The proposed approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
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