Exploiting Model Checking for Mobile Botnet Detection

Android malware is increasing from the point of view of the complexity and the harmful actions. As a matter fact, malware writers are developing sophisticated techniques to infect mobile devices very closed to their counterpart for personal computers. One of these threats is represented by the possibility to control the infected devices from the attacker i.e., the so-called botnet. In this paper a method able to identify botnet in Android environment through model checking is proposed. Starting from the malicious payload definition, the proposed method is able to detect and to localize the code related to the malicious botnet. We experiment real-world botnet based Android malware, obtaining encouraging results

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Multilayer framework for botnet detection using machine learning algorithms

The authors wish to thank Universiti Teknologi Malaysia (UTM) for its support under Research University Grant Vot- 20H04, Malaysia Research University Network (MRUN) Vot 4L876. The authors would like to acknowledge that this work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The work was also partially supported by the Specific Research project (SPEV) at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic, under Grant 2102-2021. The authors are grateful for the support of student Sebastien Mambou in consultations regarding application aspects. The authors also wish to thank the Ministry of Education Malaysia for the Hadiah Latihan Persekutuan (HLP) scholarship to complete the research.A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a ltering module and classi cation module to detect the botnet's command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through ow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.Universiti Teknologi Malaysia (UTM) through the Research University Vot-20H04Malaysia Research University Network (MRUN) Vot4L876Ministry of Higher Education through the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1Hadiah Latihan Persekutuan (HLP) Scholarship through the Ministry of Education MalaysiaSpecific Research Project (SPEV) by the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republi

Dynamic DNS Request Monitoring of Android Applications via networking

Smart devices are very popular and are becoming ubiquitous in the modern society, with Android OS as the most widespread operating system on current smartphones/tablets. However, malicious applications is one of the major concerns and fast growing security menaces facing the use of Internet in the Android platform, today. So, we need techniques and methods to address the massive malware attacks. One of the most relevant techniques to disclose sensitive behavior of Android applications during their runtime execution is Dynamic Analysis. Here we proposed a malware detection tool, termed as Network Sentinel, that it can be used for Dynamic DNS request Monitoring of Apps via networking. The main motivation for this work, it is extensively abuse of the DNS by malevolent communities in order to provide Internet connection within malicious networks and botnets. Finally, the experimental results obtained are promising by allowing us to capture the DNS queries requested by the smartphones to remote servers from the collected network traces at very low battery usage

Analysis of threats and security issues evaluation in mobile P2P networks

Technically, mobile P2P network system architecture can consider as a distributed architecture system (like a community), where the nodes or users can share all or some of their own software and hardware resources such as (applications store, processing time, storage, network bandwidth) with the other nodes (users) through Internet, and these resources can be accessible directly by the nodes in that system without the need of a central coordination node. The main structure of our proposed network architecture is that all the nodes are symmetric in their functions. In this work, the security issues of mobile P2P network system architecture such as (web threats, attacks and encryption) will be discussed deeply and then we propose different approaches and we analysis and evaluation of these mobile P2P network security issues and submit some proposal solutions to resolve the related problems with threats and other different attacks since these threats and attacks will be serious issue as networks are growing up especially with mobility attribute in current P2P networks