Ransomware Traffic Classification Using Deep Learning Models

Arivudainambi, D.; Kumar, K. A. Varun; Kumar, Ritesh; Visu, P.

doi:10.4018/ijwp.2020010101

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…In the database persistence layer, Hibernate automatically maps the database and JavaBeans through hbm.xml files so that programmers can manipulate Java beans to operate the database and return the results of the operation to the business layer. After the business layer finishes processing, it returns the results to the control layer and finally receives the response through JSP [13,14].…”

Section: System Architecture Implementation Processmentioning

confidence: 99%

Application of Computer Network Technology in Network Music Education System

Chao

2022

Mobile Information Systems

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In order to explore the application of network music education system, this paper proposes an application of computer network technology in the network music education system. This method explores the application of network music education systems through the key technical problems and solutions of information recommendation based on computer network technology. The research shows that the efficiency of network music education system based on computer network technology is about 30% higher than that of traditional methods. This paper implements the system according to the layered mode and tests the operation of the software system. The test results show that, through the use case test of each functional module of the foreground and background, the system can execute the issued commands smoothly and normally. Finally, it is concluded that the average risk occurrence rate is 2.02 hours/hour, and the probability of high risk is 17.4%. Conclusion. After testing, the operation of the software system has achieved the expected goal.

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Section: System Architecture Implementation Processmentioning

confidence: 99%

Application of Computer Network Technology in Network Music Education System

Chao

2022

Mobile Information Systems

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“…The solutions being used in the market for managing a UAV would not be feasible for a network of drones with rapid task distribution. As a part of this FSA approach, the drones in the network are treated as sensor node in a WSN (Arivudainambi et al , 2020). This ensures unbiased communication among the drones in the network.…”

Section: Proposed Methodologymentioning

confidence: 99%

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

K.A.

Priyadarshini

P.C.

et al. 2022

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Purpose Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination Design/methodology/approach Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE. Findings A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2. Research limitations/implications However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture. Practical implications The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level. Originality/value This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

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“…The script which contains the python language has the powers to connect the remote host of the IP list with the libraries which contains it by utilising the multi-threaded concepts. The messages transferring through this libraries have secure shell key for running the specific commands and get the access over the honeypot as well as monitoring the systems too [41]. The honey pots are controlled by honeynet systems which delivers honeypots to various physical machines which the setup is shown in Figs.…”

Section: Overall Architecture A) Data Servermentioning

confidence: 99%

A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

Pavendan¹,

Sankar²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Attacks on the cyber space is getting exponential in recent times. Illegal penetrations and breaches are real threats to the individuals and organizations. Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats (APTs) they fails. These APTs are targeted, more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses. Hence, there is a need for an effective defense system that can achieve a complete reliance of security. To address the above-mentioned issues, this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats. The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks. The proposed honeypot is self-realizing, strategic assisted which withholds the APTs actionable techniques and observes the behavior for analysis and modelling. The proposed graph theory based self learning honeypot using the results γ(C(n,1)),γ c (C(n,1)), γ sc (C(n,1)) outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.

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Ransomware Traffic Classification Using Deep Learning Models

Cited by 14 publications

References 9 publications

Application of Computer Network Technology in Network Music Education System

Application of Computer Network Technology in Network Music Education System

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

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