scholarly journals Experimental Cyber Attack Detection Framework

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1682
Author(s):  
Cătălin Mironeanu ◽  
Alexandru Archip ◽  
Cristian-Mihai Amarandei ◽  
Mitică Craus
Keyword(s):  
Machine Learning ◽  
Attack Detection ◽  
Learning Tools ◽  
Cyber Attack ◽  
Network Access ◽  
Public And Private ◽  
Real Time Traffic ◽  
Attack Pattern ◽  
Attack Patterns ◽  
Novel Concept

Digital security plays an ever-increasing, crucial role in today’s information-based society. The variety of threats and attack patterns has dramatically increased with the advent of digital transformation in our lives. Researchers in both public and private sectors have tried to identify new means to counteract these threats, seeking out-of-the-box ideas and novel approaches. Amongst these, data analytics and artificial intelligence/machine learning tools seem to gain new ground in digital defence. However, such instruments are used mainly offline with the purpose of auditing existing IDS/IDPS solutions. We submit a novel concept for integrating machine learning and analytical tools into a live intrusion detection and prevention solution. This approach is named the Experimental Cyber Attack Detection Framework (ECAD). The purpose of this framework is to facilitate research of on-the-fly security applications. By integrating offline results in real-time traffic analysis, we could determine the type of network access as a legitimate or attack pattern, and discard/drop the latter. The results are promising and show the benefits of such a tool in the early prevention stages of both known and unknown cyber-attack patterns.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals An Efficient Classifier for U2R, R2L, DoS Attack

2020 ◽  
Vol 9 (1) ◽  
pp. 644-647
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Learning Algorithms ◽  
Research Area ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Detection Systems

The internet has become an irreplaceable communicating and informative tool in the current world. With the ever-growing importance and massive use of the internet today, there has been interesting from researchers to find the perfect Cyber Attack Detection Systems (CADSs) or rather referred to as Intrusion Detection Systems (IDSs) to protect against the vulnerabilities of network security. CADS presently exist in various variants but can be largely categorized into two broad classifications; signature-based detection and anomaly detection CADSs, based on their approaches to recognize attack packets.The signature-based CADS use the well-known signatures or fingerprints of the attack packets to signal the entry across the gateways of secured networks. Signature-based CADS can only recognize threats that use the known signature, new attacks with unknown signatures can, therefore, strike without notice. Alternatively, anomaly-based CADS are enabled to detect any abnormal traffic within the network and report. There are so many ways of identifying anomalies and different machine learning algorithms are introduced to counter such threats. Most systems, however, fall short of complete attack prevention in the real world due system administration and configuration, system complexity and abuse of authorized access. Several scholars and researchers have achieved a significant milestone in the development of CADS owing to the importance of computer and network security. This paper reviews the current trends of CADS analyzing the efficiency or level of detection accuracy of the machine learning algorithms for cyber-attack detection with an aim to point out to the best. CADS is a developing research area that continues to attract several researchers due to its critical objective.

scholarly journals Detecting Phishing Website with Machine Learning

2019 ◽  
Vol 8 (3) ◽  
pp. 5626-5629
Keyword(s):  
Machine Learning ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Svm Classifier ◽  
Cyber Attack ◽  
Phishing Attacks ◽  
Two Parameters ◽  
Phishing Detection

Attacks are many types to disturb the network or any other websites. Phishing attacks (PA) are a type of attacks which attack the website and damage the website and may lose the data. Many types of research have been done to prevent the attacks. To overcome this, in this paper, the integrated phishing attack detection system which is adopted with SVM classifier is implemented to detect phishing websites. Phishing is the cyber attack that will destroy the website and may attack with the virus. There are two parameters that can detect the final phishing detection rate such as Identity, and security. Phishing attacks also occur in various banking and e-commerce websites. This paper deals with the UCL machine learning phishing dataset which consists of 32 attributes. The proposed algorithm implements on this dataset and shows the performance.

scholarly journals DDoS Attack Detection and Classification using Machine Learning Models with Real-Time Dataset Created

2021 ◽  
Vol 9 (5) ◽  
pp. 145-153
Author(s):  
Harrsheeta Sasikumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Ddos Attack ◽  
Real Time Traffic ◽  
Server Breakdown ◽  
Networked Devices

Distributed Denial of Service (DDoS) attack is one of the common attack that is predominant in the cyber world. DDoS attack poses a serious threat to the internet users and affects the availability of services to legitimate users. DDOS attack is characterized by the blocking a particular service by paralyzing the victim’s resources so that they cannot be used to legitimate purpose leading to server breakdown. DDoS uses networked devices into remotely controlled bots and generates attack. The proposed system detects the DDoS attack and malware with high detection accuracy using machine learning algorithms. The real time traffic is generated using virtual instances running in a private cloud. The DDoS attack is detected by considering the various SNMP parameters and classifying using machine learning technique like bagging, boosting and ensemble models. Also, the various types of malware on the networked devices are prevent from being used as a bot for DDOS attack generation.

Sign in / Sign up

Export Citation Format

Share Document