scholarly journals Application of Bioinformatics Algorithms for 3RO\PRUSKLF Cyberattacks Detection

Author(s):  
Maxim Kalinin ◽  
Dmitry Zegzhda ◽  
Vasiliy Krundyshev ◽  
Daria Lavrova ◽  
Dmitry Moskvin ◽  
...  

The functionality of any system can be represented as a set of commands that lead to a change in the state of the system. The intrusion detection problem for signature-based intrusion detection systems is equivalent to matching the sequences of operational commands executed by the protected system to known attack signatures. Various mutations in attack vectors (including replacing commands with equivalent ones, rearranging the commands and their blocks, adding garbage and empty commands into the sequence) reduce the effectiveness and accuracy of the intrusion detection. The article analyzes the existing solutions in the field of bioinformatics and considers their applicability for solving the problem of identifying polymorphic attacks by signature-based intrusion detection systems. A new approach to the detection of polymorphic attacks based on the suffix tree technology applied in the assembly and verification of the similarity of genomic sequences is discussed. The use of bioinformatics technology allows us to achieve high accuracy of intrusion detection at the level of modern intrusion detection systems (more than 0.90), while surpassing them in terms of cost-effectiveness of storage resources, speed and readiness to changes in attack vectors. To improve the accuracy indicators, a number of modifications of the developed algorithm have been carried out, as a result of which the accuracy of detecting attacks increased by up to 0.95 with the level of mutations in the sequence up to 10%. The developed approach can be used for intrusion detection both in conventional computer networks and in modern reconfigurable network infrastructures with limited resources (Internet of Things, networks of cyber-physical objects, wireless sensor networks).

2014 ◽  
Vol 10 (12) ◽  
pp. 608162 ◽  
Author(s):  
Abdelouahid Derhab ◽  
Abdelghani Bouras ◽  
Mustapha Reda Senouci ◽  
Muhammad Imran

2017 ◽  
Vol 10 (1) ◽  
pp. 122-147 ◽  
Author(s):  
Cláudio Toshio Kawakani ◽  
Sylvio Barbon ◽  
Rodrigo Sanches Miani ◽  
Michel Cukier ◽  
Bruno Bogaz Zarpelão

To support information security, organizations deploy Intrusion Detection Systems (IDS) that monitor information systems and networks, generating alerts for every suspicious behavior. However, the huge amount of alerts that an IDS triggers and their low-level representation make the alerts analysis a challenging task. In this paper, we propose a new approach based on hierarchical clustering that supports intrusion alert analysis in two main steps. First, it correlates historical alerts to identify the most common strategies attackers have used. Then, it associates upcoming alerts in real time according to the strategies discovered in the first step. The experiments were performed using a real dataset from the University of Maryland. The results showed that the proposed approach could properly identify the attack strategy patterns from historical alerts, and organize the upcoming alerts into a smaller amount of meaningful hyper-alerts.


2019 ◽  
Vol 8 (2) ◽  
pp. 2612-2616

Intrusion detection is the one of the challenging task in wireless sensor network and prevents the system and network resources from being intrude or compromised. One of the ongoing strategies for recognizing any anomalous activities presented in a network is done by intrusion detection systems (IDS) and it becomes an essential part of defense system against attacker problems. The primary goal of our work is to study and analyze intrusion detection technique meant for improving the performance of Intrusion Detection using hybrid ANN based Clustering technique. To estimate the effectiveness of the proposed strategy, KDD CUP 99 dataset is utilized for testing and assessment. Based on the analysis, it is noticed that the proposed ANN clustering performs much better than other methods with respect to accuracy which attains an average high accuracy of 93.91%when compared with other methods.


2013 ◽  
Vol 15 (3) ◽  
pp. 1223-1237 ◽  
Author(s):  
Abror Abduvaliyev ◽  
Al-Sakib Khan Pathan ◽  
Jianying Zhou ◽  
Rodrigo Roman ◽  
Wai-Choong Wong

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