2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)最新文献

Smart Activities Monitoring System (SAMS) for security applications 用于安全应用的智能活动监控系统(SAMS)

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166138

K. Dahri, Shakeel Rajput, S. Memon, L. D. Dhomeja

{"title":"Smart Activities Monitoring System (SAMS) for security applications","authors":"K. Dahri, Shakeel Rajput, S. Memon, L. D. Dhomeja","doi":"10.1109/CyberSA.2015.7166138","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166138","url":null,"abstract":"In this paper, an android based SAMS (Smart Activities Monitoring System) application for smart phone is proposed. This application is developed with the aim of increasing the national security in Pakistan. In last decade, various incidents including militant attacks and ransom-demands have been reported in which cell phones played a central role in communication between the culprits. The tracking of these criminals is very important and the government needs to adopt technologies to track mobile phones if they are being used for dangerous activities. In this paper, an android based application is presented which is designed and tested to track a suspect without his/her attention. This application tracks a smartphone by obtaining its current location and monitors a suspect remotely by retrieving information such as call logs, message logs etc. It also detects the face of the suspect and covertly captures the picture using cell phone camera and then sends it via multiple messages. Moreover, the monitoring user can also make calls to the phone which the culprit is using in stealth mode to hear the conversation happening in surroundings of the user without the knowledge of suspect.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885835","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

An efficient remote mutual authentication scheme using smart mobile phone over insecure networks 利用智能手机在不安全网络上实现高效的远程互认证方案

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166114

Ruhul Amin, S. H. Islam, G. P. Biswas, M. Khan

引用次数: 8

Context informed intelligent information infrastructures for better situational awareness 为更好的态势感知提供上下文信息的智能信息基础设施

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166136

C. Laing, Paul Vickers

引用次数: 3

On demand for situational awareness for preventing attacks on the smart grid 根据需要的态势感知，以防止对智能电网的攻击

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166133

Yegor Shovgenya, Florian Skopik, K. Theuerkauf

引用次数: 4

Development of cyber situation awareness model 网络态势感知模型的开发

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166135

Dauda Adenusi, B. K. Alese, B. Kuboye, A. Thompson

{"title":"Development of cyber situation awareness model","authors":"Dauda Adenusi, B. K. Alese, B. Kuboye, A. Thompson","doi":"10.1109/CyberSA.2015.7166135","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166135","url":null,"abstract":"This study designed and simulated cyber situation awareness model for gaining experience of cyberspace condition. This was with a view to timely detecting anomalous activities and taking proactive decision safeguard the cyberspace. The situation awareness model was modelled using Artificial Intelligence (AI) technique. The cyber situation perception sub-model of the situation awareness model was modelled using Artificial Neural Networks (ANN). The comprehension and projection submodels of the situation awareness model were modelled using Rule-Based Reasoning (RBR) techniques. The cyber situation perception sub-model was simulated in MATLAB 7.0 using standard intrusion dataset of KDD'99. The cyber situation perception sub-model was evaluated for threats detection accuracy using precision, recall and overall accuracy metrics. The simulation result obtained for the performance metrics showed that the cyber-situation sub-model of the cybersituation model better with increase in number of training data records. The cyber situation model designed was able to meet its overall goal of assisting network administrators to gain experience of cyberspace condition. The model was capable of sensing the cyberspace condition, perform analysis based on the sensed condition and predicting the near future condition of the cyberspace.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606860","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: 2

Talent discovery for cyber security: An organisational perspective 网络安全人才发现:组织视角

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166122

Marcelo Paiva Fontenele, Lily Sun

引用次数: 1

Beyond gut instincts: Understanding, rating and comparing self-learning IDSs 超越直觉:理解、评价和比较自学的ids

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166117

Markus Wurzenberger, Florian Skopik, Giuseppe Settanni, Roman Fiedler

引用次数: 1

Analysis of Malware behavior: Type classification using machine learning 恶意软件行为分析:使用机器学习进行类型分类

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166115

Radu S. Pirscoveanu, Steven S. Hansen, Thor M. T. Larsen, Matija Stevanovic, J. Pedersen, A. Czech

{"title":"Analysis of Malware behavior: Type classification using machine learning","authors":"Radu S. Pirscoveanu, Steven S. Hansen, Thor M. T. Larsen, Matija Stevanovic, J. Pedersen, A. Czech","doi":"10.1109/CyberSA.2015.7166115","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166115","url":null,"abstract":"Malicious software has become a major threat to modern society, not only due to the increased complexity of the malware itself but also due to the exponential increase of new malware each day. This study tackles the problem of analyzing and classifying a high amount of malware in a scalable and automatized manner. We have developed a distributed malware testing environment by extending Cuckoo Sandbox that was used to test an extensive number of malware samples and trace their behavioral data. The extracted data was used for the development of a novel type classification approach based on supervised machine learning. The proposed classification approach employs a novel combination of features that achieves a high classification rate with a weighted average AUC value of 0.98 using Random Forests classifier. The approach has been extensively tested on a total of 42,000 malware samples. Based on the above results it is believed that the developed system can be used to pre-filter novel from known malware in a future malware analysis system.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129614130","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: 97

An evaluation of feature selection and reduction algorithms for network IDS data 网络IDS数据特征选择与约简算法的评价

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166129

Therese Bjerkestrand, D. Tsaptsinos, E. Pfluegel

{"title":"An evaluation of feature selection and reduction algorithms for network IDS data","authors":"Therese Bjerkestrand, D. Tsaptsinos, E. Pfluegel","doi":"10.1109/CyberSA.2015.7166129","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166129","url":null,"abstract":"Intrusion detection is concerned with monitoring and analysing events occurring in a computer system in order to discover potential malicious activity. Data mining, which is part of the procedure of knowledge discovery in databases, is the process of analysing the collected data to find patterns or correlations. As the amount of data collected, store and processed only increases, so does the significance and importance of intrusion detection and data mining. A dataset that has been particularly exposed to research is the dataset used for the Third International Knowledge Discovery and Data Mining Tools competition, KDD99. The KDD99 dataset has been used to identify what data mining techniques relate to certain attack and employed to demonstrate that decision trees are more efficient than the Naïve Bayes model when it comes to detecting new attacks. When it comes to detecting network intrusions, the C4.5 algorithm performs better than SVM. The aim of our research is to evaluate and compare the usage of various feature selection and reduction algorithms against publicly available datasets. In this contribution, the focus is on feature selection and reduction algorithms. Three feature selection algorithms, consisting of an attribute evaluator and a test method, have been used. Initial results indicate that the performance of the classifier is unaffected by reducing the number of attributes.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121270430","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: 9

Prospects and challenges for social media in Pakistan 巴基斯坦社交媒体的前景与挑战

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166124

S. Memon, Sorath Mahar, L. D. Dhomeja, Farman Pirzado

引用次数: 8