With the advent of 5G, cyber–physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface wider. Hence, guarding the network against cyberattacks is critical not only for its primary subscribers but to prevent it from being exploited as a proxy to attack CPSs. In this article, we propose a consolidated framework, by utilizing deep convolutional neural networks (CNNs) and real network data, to provide early detection for distributed denial-of-service (DDoS) attacks orchestrated by a botnet that controls malicious devices. These puppet devices individually perform silent call, signaling, SMS spamming, or a blend of these attacks targeting call, Internet, SMS, or a blend of these services, respectively, to cause a collective DDoS attack in a cell that can disrupt CPSs’ operations. Our results demonstrate that our framework can achieve higher than $91\%$ normal and underattack cell detection accuracy.

Deep Learning-Based DDoS-Attack Detection for Cyber–Physical System Over 5G Network

Wireless sensor network (WSN) is an indispensible part of Internet of Things that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology, and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly toward security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this paper field based on our thorough survey and conclude this paper with possible future research directions.

/pdf/data-collection-for-security-measurement-in-wireless-sensor-3dv4yzzd2n.pdf

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Network traffic classification for data fusion: A survey

The Internet of Things (IoT) is shaping the current and next generation of the Internet The vision of IoT is to embed communication capabilities with a highly distributed, ubiquitous and dense heterogeneous devices network This vision includes the adaptation of secure mobile networks, anytime, anywhere, by anyone or anything with new intelligent applications and services Many efforts have been made to review the literature related to the IoT for the benefit of IoT development However, many issues need to be addressed to overtake the full potential of the IoT Therefore, this paper aims to classify and standardize IoT research areas by considering review papers that were published between 2010 and 2019 This paper analyzes a total of 95 related reviews, which were manually selected from databases based on 6 chosen areas This paper presents the trends and classification of IoT reviews based on 6 research areas, namely, application, architecture, communication, challenges, technology, and security IoT communication research has been dominating the trends with 21% of total reviews and more than 100% research growth in the last 10 years Hence, this paper can provide useful insights into specific emerging areas of IoT to assist future research

/pdf/a-survey-on-trend-and-classification-of-internet-of-things-20ld315mn9.pdf

A Survey on Trend and Classification of Internet of Things Reviews

A survey on network data collection

The long-term evolution (LTE)/LTE-advanced (LTE-A) network provides advanced services for billions of users with its higher bandwidths, better spectrum efficiency, and lower latency than legacy cellular networks. But it still suffers from new security threats due to its all IP-based heterogeneous architecture. Therefore, there is a critical need to perform a rapid and accurate network security measurement in the LTE/LTE-A network. To achieve LTE/LTE-A network security measurement, security-relevant data (in short security data) collection and data analysis for attack detection are required as prerequisites. However, most of the existing work only focuses on data collection and analysis for a certain type of LTE/LTE-A attacks. Little work has been done to comprehensively perform data collection and analysis for detecting various attacks on the LTE/LTE-A network. Different from previous work, in this paper, we review the security data collection and data analysis methods in terms of various attacks in order to provide the basis of security measurement in the LTE/LTE-A network. We first present a comprehensive taxonomy of attacks according to the LTE/LTE-A network structure. Then, we propose a number of criteria for evaluating the performance of data collection and analysis methods. And we lay our emphasis on the survey of data collection and analysis methods for significant active attack detection in the LTE/LTE-A network. All the reviewed methods are analyzed and discussed based on the proposed evaluation criteria. Furthermore, current open issues and future research challenges are presented with a view to stimulating future research. Finally, an adaptive data collection and data analysis model for security measurement in the LTE/LTE-A network is proposed.

/pdf/lte-lte-a-network-security-data-collection-and-analysis-for-4ydoplnsh8.pdf

LTE/LTE-A Network Security Data Collection and Analysis for Security Measurement: A Survey

Pervasive social networking (PSN) is facilitating and enriching people’s life at any time and in any place. One of the most important issues in PSN is its security and privacy, since users hope their privacy not to be disclosed in social activities. Trust relationship plays a crucial role in the PSN system, and can be utilized to support trustworthy PSN system with anonymous authentication. Thus, this paper proposes an anonymous trust authentication (AnonyTrust) scheme that can simultaneously authenticate identities and trust levels of users with privacy preservation. It also can achieve conditional traceability with a trusted server (TS), and can switch online and offline states with multiple authorized access points (APs). The security analysis and performance evaluation show the scheme is secure and efficient regarding security, anonymity, computational complexity, and communication cost. To verify the feasibility of the proposed scheme, a lightweight secret chat application called AnonyChat is developed in practice. The results show AnonyChat performs well and efficiently in Android devices.

/pdf/anonytrust-an-anonymous-trust-authentication-system-for-1gv0h0r5q0.pdf

AnonyTrust: An Anonymous Trust Authentication System for Pervasive Social Networking

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LTE/LTE-A Network Security Data Collection and Analysis for Security Measurement: A Survey

AnonyTrust: An Anonymous Trust Authentication System for Pervasive Social Networking