Recommended System Optimization in Social Networks based on Cooperative Filter with Deep MVR Algorithm

Gao, Qiang

doi:10.21203/rs.3.rs-1615797/v2

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To prove the strong efficiency of the presented approach, the proposed approach of this paper is compared with advanced models. These methods are: SVM in [41], RF in [42], AdaBoost in [43], HAST-IDS in [44], CNN-BiLSTM in [45], LuNet in [46] and Pelican in [47]. These methods are trained and tested with a similar data partition and, with the strategy of the cross-validation with equal to .…”

Section: B the Acquired Resultsmentioning

confidence: 99%

Presentation of a New Method for Intrusion Detection by using Deep Learning in Network

2023

IJACSA

View full text Add to dashboard Cite

Intrusion detection in cyberspace is an important field for today's research on the scope of the security of computer networks. The purpose of designing and implementing the systems of intrusion detection is to accurately categorize the virtual users, the hackers and the network intruders based on their normal or abnormal behavior. Due to the significant increase in the volume of the exchanged data in cyberspace, the identification and the reduction of inappropriate data characteristics will play a significant role in the increment of accuracy and speed of intrusion detection systems. The most advanced systems for intrusion detection are designed for the detection of an attack with the inspection of the full data of an attack. It means that a system of detection will be able to recognize the attack only after the execution of the attack on the attacked computer. In this paper, a system for end-to-end early intrusion detection is presented for the prevention of attacks on the network before these attacks cause further detriment to the system. The proposed method uses a classifier based on the network of the deep neural for the detection of an attack. The proposed network on a supervised method is trained for the exploitation of the related features by the raw data of the traffic of the network. Experimentally, the proposed approach has been evaluated on the dataset of NSL-KDD. The extensive experiments show that the presented approach performs better than the advanced approaches based on the accuracy, the rate of detection and the rate of the false positive, and also, the proposed system betters the rate of detection for the classes of the minority.

show abstract

Section: B the Acquired Resultsmentioning

confidence: 99%

Presentation of a New Method for Intrusion Detection by using Deep Learning in Network

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…This section compares the effectiveness of the proposed strategy with four different ways of preventing denial-ofservice attacks. The four options are as follows: Ryu controller [40] without any protection method, Ryu controller [41] with MLFQ mechanism, Ryu controller [18] with Flood Defender mechanism, and Ryu controller [40] with FMD-ARA system. The method used by the researchers in [19] provides the basis for this comparison.…”

Section: Comparing the Effectiveness Of The Proposed Solution To Thos...mentioning

confidence: 99%

Security in Software-Defined Networks Against Denial-of-Service Attacks Based on Increased Load Balancing Efficiency

ZHANG,

DING

2023

IJACSA

View full text Add to dashboard Cite

The goal of software-oriented networks (SDNs), which enable centralized control by separating the control layer from the data layer, is to increase manageability and network compatibility. However, this form of network is vulnerable to the control layer going down in the face of a denial-of-service assault because of the centralized control policy. The considerable increase in events brought on by the introduction of fresh currents into the network puts a lot of strain on the control surface when the system is in reaction mode. Additionally, the existence of recurring events that seriously impair the control surface's ability to function, such as the gathering of statistical data from the entire network, might have a negative impact. This article introduces a new approach that uses a control box comprising a coordinating controller, a main controller that establishes the flow rules, and one or more sub-controllers that establish the rules to fend off the attack and avoid network paralysis. It makes use of current (when needed). The controllers who currently set the regulations are relieved of some work by giving the coordinating controller management and supervision responsibilities. Additionally, the coordinator controller distributes the load at the control level by splitting up incoming traffic among the controllers of the flow rules. Thus, a proposed method can avoid performance disruption of the flow rule setter's main controller and withstand denial-of-service attacks by distributing the traffic load brought on by the denial-ofservice attack to one or more sub-controllers of the flow rule setter. The results of the experiments conducted indicate that, when compared to the existing solutions, the proposed solution performs better in the face of a denial-of-service assault.

show abstract

Recommended System Optimization in Social Networks based on Cooperative Filter with Deep MVR Algorithm

Cited by 2 publications

References 26 publications

Presentation of a New Method for Intrusion Detection by using Deep Learning in Network

Presentation of a New Method for Intrusion Detection by using Deep Learning in Network

Security in Software-Defined Networks Against Denial-of-Service Attacks Based on Increased Load Balancing Efficiency

Contact Info

Product

Resources

About