Convergence of detection probability, computational gains, and asymptotic analysis of an algorithm for physical-layer intrusion detection system

2018 ◽  
Vol 29 (8) ◽  
pp. e3430
Author(s):  
Awais Yousaf ◽  
Asim Loan ◽  
Radu F. Babiceanu ◽  
Leandros Maglaras ◽  
Onaiza Yousaf
Keyword(s):  
Intrusion Detection ◽  
Asymptotic Analysis ◽  
Intrusion Detection System ◽  
Detection Probability ◽  
Detection System ◽  
Physical Layer

Physical Layer Intrusion Detection System Based on Channel Prediction for Wireless Networks

2014 ◽  
Vol 556-562 ◽  
pp. 2711-2714
Author(s):  
Soo Young Shin ◽  
Isnan Arif Wicaksono
Keyword(s):  
Wireless Networks ◽  
Wireless Communication ◽  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Time Slot ◽  
Detection System ◽  
Physical Layer ◽  
False Alarm Ratio ◽  
Channel Prediction

Wireless Networks suffer from many constraints including wireless communication channel, internal and external attacks, security becomes the main concern to deal with such kind of networks. Therefore, an intrusion detection system (IDS) is required that monitors the network, detects misbehavior or anomalies and notifies other nodes in the network to avoid or punish the misbehaving nodes. This paper describes the simple method to detect the intruder in wireless communication system based on physical layer characteristics. Channel prediction method is used in receiver part to predict the transmission channel for the next time slot. Then, in the next time slot the result is compared with the actual value of channel from the channel estimation. Number of detection and false alarm ratio is measured as performance matrices of the simulation. Based on simulation result, the proposed intrusion detection system give high detection ratio and low false alarm ratio for given threshold.

scholarly journals HTBIDS: Hybrid Trust Based Intrusion Detection System for Physical Layer in WSN

2021 ◽  
Vol 10 (1) ◽  
pp. 49-53
Keyword(s):  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Physical Layer ◽  
Wireless Sensor ◽  
Network Communication ◽  
Detection Accuracy ◽  
Jamming Attack

In recent years, wireless sensor network (WSN) is the measure concern over network communication. A number of attacks are occurred at the time of network communication as result it hampers the smooth functionality, data flow and data transmission. In this article, we have proposed a trust-based intrusion detection system for physical layers attacks using DRI and Cross Check method. The HTBIDS is effective method to identify the abnormal nodes in wireless sensor network. The abnormal nodes are attacked by periodic jamming attack. We have considered the periodic jamming attack at physical layer for performance evaluation. Results show that HTBIDS performs better using detection accuracy (DA) and false alarm rate (FAR).

Physical-layer intrusion detection system for smart jamming attacks

2017 ◽  
Vol 28 (11) ◽  
pp. e3189 ◽  
Author(s):  
Awais Yousaf ◽  
Asim Loan ◽  
Radu F. Babiceanu ◽  
Onaiza Yousaf
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Physical Layer ◽  
Jamming Attacks

scholarly journals DRLDO A Novel DRL based De obfuscation System for Defence Against Metamorphic Malware

2021 ◽  
Vol 71 (1) ◽  
pp. 55-65
Author(s):  
Mohit Sewak ◽  
Sanjay K. Sahay ◽  
Hemant Rathore
Keyword(s):  
Reinforcement Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Probability ◽  
Detection System ◽  
Experimental Results ◽  
Multiple Generations ◽  
Defence System ◽  
Metamorphic Malware ◽  
Very High

In this paper, we propose a novel mechanism to normalise metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defence system. We name this system as DRLDO, for deep reinforcement learning based de-obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against ‘zero-day’ attack from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system till date that use advance DRL to intelligently and automatically normalise obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS’ classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardised dataset that contain multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise undetectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of ≈ 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.

PL-IDS: physical layer trust based intrusion detection system for wireless sensor networks

2018 ◽  
Vol 10 (4) ◽  
pp. 489-494 ◽  
Author(s):  
Umashankar Ghugar ◽  
Jayaram Pradhan ◽  
Sourav Kumar Bhoi ◽  
Rashmi Ranjan Sahoo ◽  
Sanjaya Kumar Panda
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Physical Layer ◽  
Wireless Sensor
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