2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS)最新文献_第6页

AlkylVM: A Virtual Machine for Smart Contract Blockchain Connected Internet of Things AlkylVM:智能合约区块链连接物联网虚拟机

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328732

J. Ellul, Gordon J. Pace

引用次数: 35

Identifying Vulnerabilities in APT Attacks: A Simulated Approach 识别APT攻击中的漏洞:模拟方法

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328696

Mathew Nicho, Adelaiye Oluwasegun, F. Kamoun

引用次数: 6

Improving Flexibility in Wireless Sensor Networks via API. An Application in Environmental Monitoring 通过API提高无线传感器网络的灵活性。在环境监测中的应用

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328697

Manuel-Jesús Espinosa-Gavira, A. Jimenez-Pro, J. D. L. Rosa, A. A. Pérez, O. Florencias-Oliveros, J. C. P. Salas, José María Sierra Fernández

引用次数: 3

Drone Forensics: Challenges and New Insights 无人机取证:挑战和新见解

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328747

H. Bouafif, F. Kamoun, Farkhund Iqbal, A. Marrington

引用次数: 38

Slice Distance: An Insert-Only Levenshtein Distance with a Focus on Security Applications 切片距离:专注于安全应用的仅插入Levenshtein距离

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328718

Zeeshan Afzal, Johan Garcia, S. Lindskog, A. Brunström

引用次数: 3

Malware Classification with Deep Convolutional Neural Networks 基于深度卷积神经网络的恶意软件分类

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328749

Mahmoud Kalash, Mrigank Rochan, N. Mohammed, Neil D. B. Bruce, Yang Wang, Farkhund Iqbal

{"title":"Malware Classification with Deep Convolutional Neural Networks","authors":"Mahmoud Kalash, Mrigank Rochan, N. Mohammed, Neil D. B. Bruce, Yang Wang, Farkhund Iqbal","doi":"10.1109/NTMS.2018.8328749","DOIUrl":"https://doi.org/10.1109/NTMS.2018.8328749","url":null,"abstract":"In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.","PeriodicalId":140704,"journal":{"name":"2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130974882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 233

Pre-Computing Appropriate Parameters: How to Accelerate Somewhat Homomorphic Encryption for Cloud Auditing 预计算适当的参数:如何加速云审计的同态加密

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328713

Louis Tajan, Moritz Kaumanns, D. Westhoff

引用次数: 1

Modelling of S-MAC for Heterogeneous WSN 异构 WSN 的 S-MAC 建模

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328705

Canek Portillo, J. Martínez-Bauset, V. Pla

引用次数: 5

Lessons Learned from Implementing a Privacy-Preserving Smart Contract in Ethereum 在以太坊中实现隐私保护智能合约的经验教训

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328739

A. Unterweger, F. Knirsch, Christoph Leixnering, D. Engel

引用次数: 24

A Fragment Hashing Approach for Scalable and Cloud-Aware Network File Detection 面向可扩展和云感知网络文件检测的片段散列方法

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS) Pub Date : 2018-02-01 DOI: 10.1109/NTMS.2018.8328746

Johan Garcia

{"title":"A Fragment Hashing Approach for Scalable and Cloud-Aware Network File Detection","authors":"Johan Garcia","doi":"10.1109/NTMS.2018.8328746","DOIUrl":"https://doi.org/10.1109/NTMS.2018.8328746","url":null,"abstract":"Monitoring networks for the presence of some particular set of files can, for example, be important in order to avoid exfiltration of sensitive data, or combat the spread of Child Sexual Abuse (CSA) material. This work presents a scalable system for large-scale file detection in high-speed networks. A multi-level approach using packet sampling with rolling and block hashing is introduced. We show that such approach together with a well tuned implementation can perform detection of a large number of files on the network at 10 Gbps using standard hardware. The use of packet sampling enables easy distribution of the monitoring processing functionality, and allows for flexible scaling in a cloud environment. Performance experiments on the most run-time critical hashing parts shows a single-thread performance consistent with 10Gbps line rate monitoring. The file detectability is examined for three data sets over a range of packet sampling rates. A conservative sampling rate of 0.1 is demonstrated to perform well for all tested data sets. It is also shown that knowledge of the file size distribution can be exploited to allow lower sampling rates to be configured for two of the data sets, which in turn results in lower resource usage.","PeriodicalId":140704,"journal":{"name":"2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1