EAI Endorsed Trans. Security Safety最新文献_第6页

Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering 基于网络的恶意软件行为工件排序分析与分类

EAI Endorsed Trans. Security Safety Pub Date : 2018-12-11 DOI: 10.4108/eai.13-7-2018.156002

Aziz Mohaisen, Omar Alrawi, Jeman Park, Joongheon Kim, Daehun Nyang, Manar Mohaisen

{"title":"Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering","authors":"Aziz Mohaisen, Omar Alrawi, Jeman Park, Joongheon Kim, Daehun Nyang, Manar Mohaisen","doi":"10.4108/eai.13-7-2018.156002","DOIUrl":"https://doi.org/10.4108/eai.13-7-2018.156002","url":null,"abstract":"Using runtime execution artifacts to identify malware and its associated family is an established technique in the security domain. Many papers in the literature rely on explicit features derived from network, file system, or registry interaction. While effective, the use of these fine-granularity data points makes these techniques computationally expensive. Moreover, the signatures and heuristics are often circumvented by subsequent malware authors. In this work, we propose Chatter, a system that is concerned only with the order in which high-level system events take place. Individual events are mapped onto an alphabet and execution traces are captured via terse concatenations of those letters. Then, leveraging an analyst labeled corpus of malware, n-gram document classification techniques are applied to produce a classifier predicting malware family. This paper describes that technique and its proof-of-concept evaluation. In its prototype form, only network events are considered and eleven malware families are used. We show the technique achieves 83%-94% accuracy in isolation and makes non-trivial performance improvements when integrated with a baseline classifier of combined order features to reach an accuracy of up to 98.8%.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209236","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: 6

A secure and lightweight multicast communication system for Smart Grids 面向智能电网的安全轻量级多播通信系统

EAI Endorsed Trans. Security Safety Pub Date : 2018-12-11 DOI: 10.4108/eai.13-7-2018.156004

Tiago Antônio Rizzetti, B. Silva, A. Rodrigues, R. Milbradt, L. Canha

引用次数: 1

Formal Approach to Detect and Resolve Anomalies while Clustering ABAC Policies ABAC策略聚类时检测和解决异常的形式化方法

EAI Endorsed Trans. Security Safety Pub Date : 2018-12-03 DOI: 10.4108/eai.13-7-2018.156003

Maryem Ait El Hadj, A. Khoumsi, Yahya Benkaouz, M. Erradi

引用次数: 7

Threats, Countermeasures and Attribution of Cyber Attacks on Critical Infrastructures 关键基础设施网络攻击的威胁、对策和归因

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-17 DOI: 10.4108/eai.15-10-2018.155856

L. Maglaras, M. Ferrag, A. Derhab, M. Mukherjee, H. Janicke, Stylianos Rallis

引用次数: 28

How Stakeholders Perceived Security Risks? A New Predictive Functional Level Model and its Application to E-Learning 利益相关者如何感知安全风险?一种新的预测功能层模型及其在网络学习中的应用

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-15 DOI: 10.4108/eai.15-10-2018.155738

N. Rjaibi, Latifa Ben Arfa Rabai

{"title":"How Stakeholders Perceived Security Risks? A New Predictive Functional Level Model and its Application to E-Learning","authors":"N. Rjaibi, Latifa Ben Arfa Rabai","doi":"10.4108/eai.15-10-2018.155738","DOIUrl":"https://doi.org/10.4108/eai.15-10-2018.155738","url":null,"abstract":"A new predictive functional level security risk management model is proposed in order to quantify the security level perception and the level of risk involved. It helps in defining the assets, measuring economically the risk, managing the risk toward decisions making. It is out of implementation and based on a functional level architecture. The paper defines a simple predictive model, it relies on a few number of inputs which form the system’s security specifications and provides one output which is the average loss per unit of time ($/H) incurred by a stakeholder as a result of security threats. The obtained values represent how stakeholders perceived economically security risks and predict how it will change over time to implement in advance the needed security strategies. Our model is useful in any security context. We report it in practice originally to the level of e-Learning systems for current architectures because they lack a common measurable value and evidence of cyber security. Our model assists security experts from the early phases of system’s development to implement future safe and secure platforms.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116091990","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: 0

FPGA Implementation of Elliptic Curve Cryptoprocessor for Perceptual Layer of the Internet of Things 物联网感知层椭圆曲线密码处理器的FPGA实现

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-15 DOI: 10.4108/eai.15-10-2018.155739

V. Kamalakannan, S. Tamilselvan

{"title":"FPGA Implementation of Elliptic Curve Cryptoprocessor for Perceptual Layer of the Internet of Things","authors":"V. Kamalakannan, S. Tamilselvan","doi":"10.4108/eai.15-10-2018.155739","DOIUrl":"https://doi.org/10.4108/eai.15-10-2018.155739","url":null,"abstract":"Today’s developing era data and information security plays an important role in unsecured communication between Internet of Things (IoT) elements. In IoT, data are transmitted in plaintext for many reasons. One of the most common reason is the availability of hardware. Many IoT products are inexpensive components with limited memory and computational resources. Such devices might be unable to support the computationally intense cryptographic functions of asymmetrical cryptography. If designers considered the privacy implications of unencrypted data, they have limited options for encryption because of the hardware platform. Therefore the designers have to create their own security protocols or implement stripped-down versions of existing security protocols. The second option has a better chances. Evidence recommends such a modified protocol would run efficiently on small devices. Elliptic Curve Cryptography (ECC) is used to ensure complete protection against the security risks such as confidentiality, integrity, privacy and authentication by implementing an Elliptic Curve Cryptoprocessor. The work focuses on high-performance Elliptic Curve Cryptoprocessor design, optimized for Field Programmable Gate Array (FPGA) implementation, using the concept of asymmetric and hash algorithms. A novel cryptographic algorithm consisting of matrix mapping methodology and hidden generator point theory is to be applied for encryption/decryption between the sender and receiver whereas Elliptic Curve Digital Signature Algorithm (ECDSA) designed using Keccak Secured Hash Algorithm (SHA) algorithm is applied for the validation of the encrypted data. The proposed Cryptoprocessor operates at a minimum period of 6.980 ns and maximum frequency of 143.276 MHz. This work focuses on the practicability of public key cryptography implementation for devices connected in the perceptual layer of IoT.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127851794","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

A Multi-connection Encryption Algorithm Applied in Secure Channel Service System 一种用于安全信道业务系统的多连接加密算法

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-15 DOI: 10.4108/eai.15-5-2018.155167

Fanhao Meng, Rongheng Lin, Zhuoran Wang, Hua Zou, Shiqi Zhou

{"title":"A Multi-connection Encryption Algorithm Applied in Secure Channel Service System","authors":"Fanhao Meng, Rongheng Lin, Zhuoran Wang, Hua Zou, Shiqi Zhou","doi":"10.4108/eai.15-5-2018.155167","DOIUrl":"https://doi.org/10.4108/eai.15-5-2018.155167","url":null,"abstract":"Encryption is the most important method to enhance security of network transmitting. SDN (Software Defined Networking) Security Transmission Service can provide multi-connection transmitting service, which scatters data to multiple network connections for transmission so that data on different connections is isolated from each other. Based on the service, encrypting the isolated data prevents overall data from intercepted and deciphered. In the above scenario, we propose an encryption algorithm that uses the data themselves as encryption keys, and use the data isolation effect of multi-connection transmission to distribute the encrypted ciphertext to different network transmission paths, which is equivalent to using a rather random sequence as an encryption key for each data fragment without sharp increase in transmitting data, so that data transmitted on every connection are ensured to be safe. After compared with other encryption algorithms such as DES, AES and RSA, it is proved that in the multi-connection transmitting scenario this algorithm has better encryption effect and operating efficiency, which provides an effective guarantee for network security.","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132735283","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: 4

Mouse Underlaying: Global Key and Mouse Listener Based on an Almost Invisible Window with Local Listeners and Sophisticated Focus 鼠标底层:基于几乎不可见窗口的全局键和鼠标监听器，具有本地监听器和复杂焦点

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-15 DOI: 10.4108/eai.15-10-2018.155740

Tim Niklas Witte

引用次数: 1

Kernel-Space Intrusion Detection Using Software-Defined Networking 基于软件定义网络的内核空间入侵检测

EAI Endorsed Trans. Security Safety Pub Date : 2018-10-09 DOI: 10.4108/EAI.13-7-2018.155168

Tommy Chin, Kaiqi Xiong, M. Rahouti

引用次数: 3

Leveraging attention-based deep neural networks for security vetting of Android applications 利用基于注意力的深度神经网络对Android应用程序进行安全审查

EAI Endorsed Trans. Security Safety Pub Date : 2018-07-13 DOI: 10.4108/eai.27-9-2021.171168

Prabesh Pathak, Prabesh Poudel, Sankardas Roy, Doina Caragea

{"title":"Leveraging attention-based deep neural networks for security vetting of Android applications","authors":"Prabesh Pathak, Prabesh Poudel, Sankardas Roy, Doina Caragea","doi":"10.4108/eai.27-9-2021.171168","DOIUrl":"https://doi.org/10.4108/eai.27-9-2021.171168","url":null,"abstract":"Many traditional machine learning and deep learning algorithms work as a black box and lack interpretability. Attention-based mechanisms can be used to address the interpretability of such models by providing insights into the features that a model uses to make its decisions. Recent success of attention-based mechanisms in natural language processing motivates us to apply the idea for security vetting of Android apps. An Android app’s code contains API-calls that can provide clues regarding the malicious or benign nature of an app. By observing the pattern of the API-calls being invoked, we can interpret the predictions of a model trained to separate benign apps from malicious apps. In this paper, using the attention mechanism, we aim to find the API-calls that are predictive with respect to the maliciousness of Android apps. More specifically, we target to identify a set of API-calls that malicious apps exploit, which might help the community discover new signatures of malware. In our experiment, we work with two attention-based models: Bi-LSTM Attention and Self-Attention. Our classification models achieve high accuracy in malware detection. Using the attention weights, we also extract the top 200 API-calls (that reflect the malicious behavior of the apps) from each of these two models, and we observe that there is significant overlap between the top 200 API-calls identified by the two models. This result increases our confidence that the top 200 API-calls can be used to improve the interpretability of the models. Received on 14 July 2021; accepted on 03 August 2021; published on 27 September 2021","PeriodicalId":335727,"journal":{"name":"EAI Endorsed Trans. Security Safety","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124878251","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