Internet of Drones (IoD): Threats, Vulnerability, and Security Perspectives

The development of the Internet of Drones (IoD) becomes vital because of a proliferation of drone-based civilian or military applications. The IoD based technological revolution upgrades the current Internet environment into a more pervasive and ubiquitous world. IoD is capable of enhancing the state-of-the-art for drones while leveraging services from the existing cellular networks. Irrespective to a vast domain and range of applications, IoD is vulnerable to malicious attacks over open-air radio space. Due to increasing threats and attacks, there has been a lot of attention on deploying security measures for IoD networks. In this paper, critical threats and vulnerabilities of IoD are presented. Moreover, taxonomy is created to classify attacks based on the threats and vulnerabilities associated with the networking of drone and their incorporation in the existing cellular setups. In addition, this article summarizes the challenges and research directions to be followed for the security of IoD.Comment: 13 pages, 3 Figures, 1 Table, The 3rd International Symposium on Mobile Internet Security (MobiSec'18), Auguest 29-September 1, 2018, Cebu, Philippines, Article No. 37, pp. 1-1

NetFlow Monitoring and Cyberattack Detection Using Deep Learning With Ceph

Figuring the network's hidden abnormal behavior can reduce network vulnerability. This paper presents a detailed architecture in which the collected log data of the network can be processed and analyzed. We process and integrate on-campus network information from every router and store the integrated NetFlow log data. Ceph is used as an open-source distributed storage platform that offers high efficiency, high reliability, scalability, and preliminary preprocessing of raw data with Python, removing redundant areas and unification. In the subanalysis, we discover the anomaly event and absolute flow by three times of standard deviation rule. Keras has been used to classify in-time data collected via a cyber-attack and to construct an automatic identifier template through the Recurring Neural Network (RNN) test. The identification accuracy of the optimization model is around 98% in attack detection. Finally, in the MySQL server, the results of the real-time evaluation can be obtained, and the results of the assessment can be displayed via ECharts

Reachability-based impact as a measure for insiderness

Insider threats pose a difficult problem for many organisations. While organisations in principle would like to judge the risk posed by a specific insider threat, this is in general not possible. This limitation is caused partly by the lack of models for human behaviour, partly by restrictions on how much and what may be monitored, and by our inability to identify relevant features in large amounts of logged data. To overcome this, the notion of insiderness has been proposed, which measures the degree of access an actor has to a certain resource. We extend this notion with the concept of impact of an insider, and present different realisations of impact. The suggested approach results in readily usable techniques that allow to get a quick overview of potential insider threats based on locations and assets reachable by employees. We present several variations ranging from pure reachability to potential damage to assets causable by an insider

Development of an enhanced scheme for NEMO environment

The frequent change of Mobile Node (MN) location is going to increase rapidly as everything is a mobile presently. In order to achieve seamless mobility, mobility managements are considered as very important. The mobile IPv6 MIPv6 is proposed and standardized by IETF. It is introduced to improve the host mobility management. However, it faces different problems when mobile nodes move between different network infrastructures. To overcome these issues, proxy mobile IPv6 PMIPv6 is introduced. PMIPv6 is a network based mobility management intended to improve handover delay by functioning mobility managements on behalf of mobile node. However, PMIPv6 added additional cost to the network by implementing mobile access gateway and bi-directional tunnel. In addition, network mobility, NEMO standardized as extension to MIPV6 to support session continuity to the internet services on behalf of mobile network nodes. Nevertheless, they still issues of packet loss and handover delay during the registration of MNNs and handoff of NEMO. The research within this area is very active, trying to solve these problems by integration of different mobility management’s schemes. In this paper, we have focused on evaluating different integrations of mobility managements with NEMO. Then we proposed a BUNSD-LMA schema, to solve the problem of packet loss and handover delay, by integration of PMIPv6 with NEMO BS, using pre-registration of MNP (HNP) in advance with short time in binding update extensions message option format

Backdoor attack detection based on stepping stone detection approach

Network intruders usually use a series of hosts (stepping stones) to conceal the tracks of their intrusion in the network. This type of intrusion can be detected through an approach called Stepping Stone Detection (SSD). In the past years, SSD was confined to the detection of only this type of intrusion. In this dissertation, we consider the use of SSD concepts in the field of backdoor attack detection. The application of SSD in this field results in many advantages. First, the use of SSD makes the backdoor attack detection and the scan process time faster. Second, this technique detects all types of backdoor attack, both known and unknown, even if the backdoor attack is encrypted. Third, this technique reduces the large storage resources used by traditional antivirus tools in detecting backdoor attacks. This study contributes to the field by extending the application of SSD-based techniques, which are usually used in SSD-based environments only, into backdoor attack detection environments. Through an experiment, the accuracy of SSD-based backdoor attack detection is shown as very high

An Ensemble-based Machine Learning Model for Investigating Children Interaction with Robots in Childhood Education

"Investing in children's well-being and supporting high-quality pre-school education is a significant component of its promotion (ECE). All children have the right to participate. ECE teachers' thoughts about children's participation were examined to see if they were linked to children's perceptions of their participation. On the other hand, current studies focus on a single categorization method with lower overall accuracy. The findings of this study provided the basis for the development of an ensemble machine learning (ML) approach for measuring the participation of children with learning disabilities in educational situations that were specifically developed for them. Visual and auditory data are collected and analyzed to determine whether or not the youngster is engaged during the robot-child interaction in this manner. It is proposed that an ensemble ML technique (Enhanced Deep Neural Network (EDNN), Modified Extreme Gradient Boost Classifier, and Logistic Regression) be used to judge whether or not a youngster is actively engaged in the learning process. Children's participation in ECE courses depends on both the quantitative and qualitative characteristics of the classroom, according to this research.