標題: Titlebook: Deployable Machine Learning for Security Defense; First International Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh Conference proceedings 20 [打印本頁] 作者: metabolism 時間: 2025-3-21 19:53
書目名稱Deployable Machine Learning for Security Defense影響因子(影響力)
書目名稱Deployable Machine Learning for Security Defense影響因子(影響力)學科排名
書目名稱Deployable Machine Learning for Security Defense網(wǎng)絡(luò)公開度
書目名稱Deployable Machine Learning for Security Defense網(wǎng)絡(luò)公開度學科排名
書目名稱Deployable Machine Learning for Security Defense被引頻次
書目名稱Deployable Machine Learning for Security Defense被引頻次學科排名
書目名稱Deployable Machine Learning for Security Defense年度引用
書目名稱Deployable Machine Learning for Security Defense年度引用學科排名
書目名稱Deployable Machine Learning for Security Defense讀者反饋
書目名稱Deployable Machine Learning for Security Defense讀者反饋學科排名
作者: 檔案 時間: 2025-3-21 21:24
: Adaptable Fraud Detection in the Real Worldally and continuously update our weights for each ‘oracle’. For the second problem, we show how to derive an optimal decision surface, and how to compute the Pareto optimal set, to allow what-if questions. An important consideration is adaptation: Fraudsters will change their behavior, according to 作者: 高射炮 時間: 2025-3-22 01:29
Domain Generation Algorithm Detection Utilizing Model Hardening Through GAN-Generated Adversarial Exentiate from real domains. The resulting set of domains have characteristics, such as character distribution, that more closely resemble real domains than sets produced in previous research. We then use these GAN-produced domains as additional examples of DGA domains and use them to augment the trai作者: Legion 時間: 2025-3-22 05:46
Toward Explainable and Adaptable Detection and Classification of Distributed Denial-of-Service Attac approaches, along with the detection results this method further generates risk profiles that provides users with interpretability for filtering DDoS traffic. Additionally, this method does not need to retrain the detection model in order to make it fit in a new network environment. Users can lever作者: 多節(jié) 時間: 2025-3-22 10:09
DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threatsintrusion datasets have three key limitations - (1) They capture attack traffic at the external endpoints, limiting their usefulness in the context of APTs which comprise of attack vectors within the internal network as well (2) The difference between normal and anomalous behavior is quiet distingui作者: Glossy 時間: 2025-3-22 16:31 作者: Glossy 時間: 2025-3-22 20:38 作者: intuition 時間: 2025-3-22 21:49 作者: notification 時間: 2025-3-23 02:09
Billy Tsouvalas,Nick Nikiforakis approaches, along with the detection results this method further generates risk profiles that provides users with interpretability for filtering DDoS traffic. Additionally, this method does not need to retrain the detection model in order to make it fit in a new network environment. Users can lever作者: Ingratiate 時間: 2025-3-23 08:52
Lecture Notes in Computer Scienceintrusion datasets have three key limitations - (1) They capture attack traffic at the external endpoints, limiting their usefulness in the context of APTs which comprise of attack vectors within the internal network as well (2) The difference between normal and anomalous behavior is quiet distingui作者: 富足女人 時間: 2025-3-23 10:47 作者: Multiple 時間: 2025-3-23 13:51 作者: cacophony 時間: 2025-3-23 20:22
Foundations of Holistic Organization Designing:.How suspicious is ‘Smith’, trying to buy $500 shoes, on Monday 3am? How to merge the risk scores, from a handful of risk-assessment modules (‘oracles’) in an adversarial environment? More importantly, given historical data (orders, prices, and what-happened afterwards), and business goals/restr作者: 釘牢 時間: 2025-3-23 23:48 作者: 貨物 時間: 2025-3-24 03:41
Foundations of Holistic Organization Designf hardcoding the address of the C&C domain in the malware, DGAs are used to frequently change the address of the C&C server, causing static detection methods, such as blacklists, to be ineffective. In response, DGA detection methods have been proposed which attempt to detect these DGA-produced domai作者: FLAX 時間: 2025-3-24 08:33
Billy Tsouvalas,Nick Nikiforakisotnet), distributed Denial-of-Service (DDoS) attacks disrupt the services of the victim and make it unavailable to its legitimate users. Albeit studied many years already, the detection of DDoS attacks remains a troubling problem. In this paper, we propose a new, learning-based DDoS detection and cl作者: 寄生蟲 時間: 2025-3-24 14:12 作者: Stress 時間: 2025-3-24 18:07 作者: 膽大 時間: 2025-3-24 20:42 作者: diathermy 時間: 2025-3-25 00:42 作者: Flu表流動 時間: 2025-3-25 04:58
Deployable Machine Learning for Security Defense978-3-030-59621-7Series ISSN 1865-0929 Series E-ISSN 1865-0937 作者: 錢財 時間: 2025-3-25 08:27 作者: 種植,培養(yǎng) 時間: 2025-3-25 15:11
978-3-030-59620-0Springer Nature Switzerland AG 2020作者: condescend 時間: 2025-3-25 19:40 作者: 儲備 時間: 2025-3-26 00:02
MALOnt: An Ontology for Malware Threat Intelligencen different platforms from scattered threat sources. This collective information can guide decision making in cyber defense applications utilized by security operation centers. In this paper, we introduce an open-source malware ontology, MALOnt that allows the structured extraction of information an作者: 可行 時間: 2025-3-26 03:54
: Adaptable Fraud Detection in the Real Worlding:.How suspicious is ‘Smith’, trying to buy $500 shoes, on Monday 3am? How to merge the risk scores, from a handful of risk-assessment modules (‘oracles’) in an adversarial environment? More importantly, given historical data (orders, prices, and what-happened afterwards), and business goals/restr作者: 兇兆 時間: 2025-3-26 06:35 作者: 多余 時間: 2025-3-26 08:50
Domain Generation Algorithm Detection Utilizing Model Hardening Through GAN-Generated Adversarial Exf hardcoding the address of the C&C domain in the malware, DGAs are used to frequently change the address of the C&C server, causing static detection methods, such as blacklists, to be ineffective. In response, DGA detection methods have been proposed which attempt to detect these DGA-produced domai作者: goodwill 時間: 2025-3-26 13:24 作者: 態(tài)度暖昧 時間: 2025-3-26 18:55 作者: 易于 時間: 2025-3-26 21:49
DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threatstion faces, specifically attacks that go undetected by traditional signature-based intrusion detection systems. Along with the ability to process large amounts of data, machine learning brings the potential to detect contextual and collective anomalies, an essential attribute of an ideal threat dete作者: 漸變 時間: 2025-3-27 02:50 作者: 襲擊 時間: 2025-3-27 07:02 作者: 或者發(fā)神韻 時間: 2025-3-27 13:00 作者: 雪崩 時間: 2025-3-27 15:23
Lecture Notes in Computer Sciencey malware. We also demonstrate the annotation process using MALOnt on exemplar threat intelligence reports. A work in progress, this research is part of a larger effort towards auto-generation of knowledge graphs for gathering malware threat intelligence from heterogeneous online resources.作者: Dissonance 時間: 2025-3-27 20:45
https://doi.org/10.1007/978-3-031-64483-2ned adversarial training and corresponding insights to efficiently make the model more robust on safety-critical scenarios. We hope this work can serve as a stepping stone to seek efficient defenses against adversarial examples in large-scale object detectors.作者: 小官 時間: 2025-3-27 23:13
https://doi.org/10.1007/978-3-031-64171-8) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods.作者: Sigmoidoscopy 時間: 2025-3-28 05:10 作者: Champion 時間: 2025-3-28 06:43
Towards Practical Robustness Improvement for Object Detection in Safety-Critical Scenariosned adversarial training and corresponding insights to efficiently make the model more robust on safety-critical scenarios. We hope this work can serve as a stepping stone to seek efficient defenses against adversarial examples in large-scale object detectors.作者: inculpate 時間: 2025-3-28 13:22
Forecasting Network Intrusions from Security Logs Using LSTMs) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods.
