標(biāo)題: Titlebook: ; [打印本頁(yè)] 作者: Addendum 時(shí)間: 2025-3-21 19:05
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems影響因子(影響力)
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems影響因子(影響力)學(xué)科排名
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems被引頻次
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems被引頻次學(xué)科排名
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書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems年度引用學(xué)科排名
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems讀者反饋
書(shū)目名稱Guide to Vulnerability Analysis for Computer Networks and Systems讀者反饋學(xué)科排名
作者: FLEET 時(shí)間: 2025-3-21 22:53 作者: tattle 時(shí)間: 2025-3-22 02:20
Victor Zaslavsky,Robert J. Brymcular focusses on interaction with the underlying file system. This study identifies that ransomware instances have unique behavioural patterns, which are significantly different from those of normal user interaction.作者: 可轉(zhuǎn)變 時(shí)間: 2025-3-22 05:38 作者: 不可接觸 時(shí)間: 2025-3-22 09:18 作者: Increment 時(shí)間: 2025-3-22 16:33
Bibliographical note and bibliography,machine learning techniques are significantly degraded. This work provides evidence that machine learning is likely to perform better than ad hoc approaches, particularly when faced with intelligent attackers who can attempt to exploit the inherent weaknesses in a given detection strategy.作者: Increment 時(shí)間: 2025-3-22 20:16 作者: 煞費(fèi)苦心 時(shí)間: 2025-3-23 01:03
The Dawn of Soviet Influence in Egypthow accurately we can distinguish mobile users, based on various features. Here, our primary goal is to determine which techniques are most likely to be effective in a more comprehensive masquerade detection system.作者: 休閑 時(shí)間: 2025-3-23 01:23
https://doi.org/10.1007/978-94-010-3629-0cture. The process helps organisations to eliminate security issues before attackers can exploit them for monetary gains or other malicious purposes. The significant advancements in desktop, Web and mobile computing technologies have widened the range of security-related complications. It has become作者: famine 時(shí)間: 2025-3-23 06:17 作者: 晚間 時(shí)間: 2025-3-23 12:15
Soviet Social Scientists Talking. Trust is an essential element to develop confidence-based relationships amongst the various components in such a diverse environment. The current chapter presents the taxonomy of trust models and classification of information sources for trust assessment. Furthermore, it presents the taxonomy of r作者: prick-test 時(shí)間: 2025-3-23 14:12 作者: INCUR 時(shí)間: 2025-3-23 20:52
Memories of Space and Spaces of Memoryined, the challenges and opportunities of such a paradigm and how to design a system implementing zero trust starting with static vulnerability analysis. The role of artificial intelligence as a selfless mediating agent is examined to resolve some issues in implementing a trustless security system, 作者: heterogeneous 時(shí)間: 2025-3-23 22:44 作者: Mercurial 時(shí)間: 2025-3-24 05:15 作者: defuse 時(shí)間: 2025-3-24 06:44
,Teenage Gangs, ‘Afgantsy’ and Neofascists,bility to . in information systems such as online services with interfaces that accept user data inputs and return machine learning results such as labels. Two types of attacks are considered: . and .. In an ., the adversary collects labels of input data from an online classifier and applies . to tr作者: GEN 時(shí)間: 2025-3-24 14:20
Conclusion: Lessons for the West,owever, in recent years, the advent of modern and cheap telephony technologies and larger user base (more than six billion users) has attracted scammers to use telephony for distributing unwanted content via instant messaging and calls. Detection of unwanted caller in the telephony has become challe作者: 鳴叫 時(shí)間: 2025-3-24 16:21
Bibliographical note and bibliography,e this function call graph approach to elementary machine learning techniques that are trained on simpler features. We find that the machine learning techniques are generally more robust than the function call graphs, in the sense that the malware must be modified to a far greater extent before the 作者: SUGAR 時(shí)間: 2025-3-24 22:22
The United States and Revolutionary Iran,ence of encrypted code within an executable file could serve as a strong heuristic for malware detection. In this chapter, we consider the feasibility of detecting encrypted segments within an executable file using hidden Markov models.作者: hallow 時(shí)間: 2025-3-25 00:56 作者: 消散 時(shí)間: 2025-3-25 06:29
Victor Zaslavsky,Robert J. Brymrent malware types is often resulting from criminal opportunity. The monetisation of ransomware, coupled with the continuous growing importance of user data, is resulting in ransomware becoming one of the most prominent forms of malware. Detecting and stopping a ransomware attack is challenging due 作者: 滔滔不絕的人 時(shí)間: 2025-3-25 10:14
https://doi.org/10.1007/978-1-349-11383-5en to be overlooked due to the lack of expertise and technical approach to capture and model these requirements in an effective way. It is not helped by the fact that many companies, especially SMEs, tend to focus on the functionality of their business processes first, before considering security as作者: 格子架 時(shí)間: 2025-3-25 12:56 作者: 低位的人或事 時(shí)間: 2025-3-25 19:41 作者: 易改變 時(shí)間: 2025-3-25 21:34
A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attac. Trust is an essential element to develop confidence-based relationships amongst the various components in such a diverse environment. The current chapter presents the taxonomy of trust models and classification of information sources for trust assessment. Furthermore, it presents the taxonomy of r作者: CHART 時(shí)間: 2025-3-26 01:56
AI- and Metrics-Based Vulnerability-Centric Cyber Security Assessment and Countermeasure Selectionr is based on calculating a set of cyber security metrics suited for automatic- and human-based perception and analysis of cyber situation and suits for automated countermeasure response in a near real-time mode. To fulfil security assessments and make countermeasure decisions, artificial intelligen作者: 圣人 時(shí)間: 2025-3-26 06:59 作者: 有發(fā)明天才 時(shí)間: 2025-3-26 08:35 作者: Digitalis 時(shí)間: 2025-3-26 13:00 作者: Mast-Cell 時(shí)間: 2025-3-26 18:22
Vulnerability Detection and Analysis in Adversarial Deep Learningbility to . in information systems such as online services with interfaces that accept user data inputs and return machine learning results such as labels. Two types of attacks are considered: . and .. In an ., the adversary collects labels of input data from an online classifier and applies . to tr作者: Focus-Words 時(shí)間: 2025-3-26 23:37 作者: FLIP 時(shí)間: 2025-3-27 02:58 作者: Cantankerous 時(shí)間: 2025-3-27 06:32 作者: Melanoma 時(shí)間: 2025-3-27 10:14
Masquerade Detection on Mobile Devicesonsider the problem of masquerade detection on mobile devices. Specifically, we experiment with a variety of machine learning techniques to determine how accurately we can distinguish mobile users, based on various features. Here, our primary goal is to determine which techniques are most likely to 作者: 平項(xiàng)山 時(shí)間: 2025-3-27 17:01
Identifying File Interaction Patterns in Ransomware Behaviourrent malware types is often resulting from criminal opportunity. The monetisation of ransomware, coupled with the continuous growing importance of user data, is resulting in ransomware becoming one of the most prominent forms of malware. Detecting and stopping a ransomware attack is challenging due 作者: Postulate 時(shí)間: 2025-3-27 18:53
A Framework for the Visualisation of Cyber Security Requirements and Its Application in BPMNen to be overlooked due to the lack of expertise and technical approach to capture and model these requirements in an effective way. It is not helped by the fact that many companies, especially SMEs, tend to focus on the functionality of their business processes first, before considering security as作者: MAOIS 時(shí)間: 2025-3-28 01:26 作者: 弓箭 時(shí)間: 2025-3-28 02:55
A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attacve solution that could analyse each symptom, indicator, and vulnerability in order to detect the impact and likelihood of attacks directed at cloud computing environments. Therefore, possible implementation of the proposed framework might help to minimise false positive alarms, as well as to improve作者: Morsel 時(shí)間: 2025-3-28 06:18 作者: tattle 時(shí)間: 2025-3-28 12:21 作者: Contend 時(shí)間: 2025-3-28 15:20
Defending Against Chained Cyber-Attacks by Adversarial Agentsttention to the problem of determining the optimal/most effective strategy (with respect to this model) that the defender can use to block the attacker from gaining certain capabilities and find it to be an NP-complete problem. To address this complexity, we utilize an A*-based approach and develop 作者: 烤架 時(shí)間: 2025-3-28 22:48
Vulnerability Detection and Analysis in Adversarial Deep Learningematically selected based on the classification scores from the inferred classier. This attack is strengthened by . (GANs) and . producing . samples that are likely to be misclassified. The vulnerability is measured by the increase in misdetection rates. This quantitative understanding of the vulner作者: Cardioversion 時(shí)間: 2025-3-29 01:40
SOCIO-LENS: Spotting Unsolicited Caller Through Network Analysisr as a weighted call graph network and identify malicious users by analysing different network features of users. To this extent, we have identified a set of features that help represent malicious and non-malicious behaviour of users in a network. We have conducted rigorous experimentation of the pr作者: 保守 時(shí)間: 2025-3-29 06:15
A Framework for the Visualisation of Cyber Security Requirements and Its Application in BPMNan be used to extend, visualise and verify cyber security requirements in not only BPMN, but any other existing modelling language. We investigate a new approach to modelling security and propose a solution that overcomes current issues whilst still providing functionality to include all concepts po作者: 一起 時(shí)間: 2025-3-29 08:21 作者: 土產(chǎn) 時(shí)間: 2025-3-29 14:38
https://doi.org/10.1007/978-94-010-3629-0inding the security vulnerability. This method requires a large amount of time, effort and resources, and it is heavily reliant on expert knowledge, something that is widely attributed to being in short supply. The assistive vulnerability assessment is conducted with the help of scanning tools or fr作者: 熟練 時(shí)間: 2025-3-29 19:00 作者: CRASS 時(shí)間: 2025-3-29 21:34
https://doi.org/10.1007/978-1-349-02076-8pplied. To specify security objects and interrelationships among them, an ontological repository is realised. It joins data from various security databases and specifies techniques of logical inference to get answers on security-related requests. The suggested approach is demonstrated using several 作者: Lasting 時(shí)間: 2025-3-30 03:44 作者: Nonconformist 時(shí)間: 2025-3-30 04:46
Roger D. Markwick,Euridice Charon Cardonattention to the problem of determining the optimal/most effective strategy (with respect to this model) that the defender can use to block the attacker from gaining certain capabilities and find it to be an NP-complete problem. To address this complexity, we utilize an A*-based approach and develop 作者: OMIT 時(shí)間: 2025-3-30 10:22 作者: Outspoken 時(shí)間: 2025-3-30 12:37 作者: TRUST 時(shí)間: 2025-3-30 16:51
https://doi.org/10.1007/978-1-349-11383-5an be used to extend, visualise and verify cyber security requirements in not only BPMN, but any other existing modelling language. We investigate a new approach to modelling security and propose a solution that overcomes current issues whilst still providing functionality to include all concepts po作者: 躲債 時(shí)間: 2025-3-30 20:54
Guide to Vulnerability Analysis for Computer Networks and Systems978-3-319-92624-7Series ISSN 1617-7975 Series E-ISSN 2197-8433 作者: Adenocarcinoma 時(shí)間: 2025-3-31 04:48
Memories of Space and Spaces of Memoryined, the challenges and opportunities of such a paradigm and how to design a system implementing zero trust starting with static vulnerability analysis. The role of artificial intelligence as a selfless mediating agent is examined to resolve some issues in implementing a trustless security system, in addition to the challenges this presents.
