Advanced Machine Language Approach to Detect DDoS Attack Using DBSCAN Clustering Technology with Entropy

Girma, Anteneh; Garuba, Mosses; Goel, Rajini

doi:10.1007/978-3-319-54978-1_17

Cited by 10 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed data sanitization scheme is analyzed using the vehicular parameters based on various characteristics, rather than trying to match the characteristics to a specific distribution shape [25]. Research in [13] uses DBSCAN to label the train data, whereas this paper uses it to pre-process the test data and remove data points that can reduce the accracy of the trained classifier in advance.…”

Section: Adversarial Sanitation Defensementioning

confidence: 99%

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

Kim¹,

Chung²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The development of Vehicular Ad-hoc Network (VANET) technology is helping Intelligent Transportation System (ITS) services to become a reality. Vehicles can use VANETs to communicate safety messages on the road (while driving) and can inform their location and share road condition information in real-time. However, intentional and unintentional (e.g., packet/frame collision) wireless signal jamming can occur, which will degrade the quality of communication over the channel, preventing the reception of safety messages, and thereby posing a safety hazard to the vehicle's passengers. In this paper, VANET jamming detection applying Support Vector Machine (SVM) machine learning technology is used to classify jamming and non-jamming situations. The analysis is based on two cases which include normal traffic and heavy traffic conditions, where the results show that the probability of packet dropping will increase when many vehicles are using the wireless channel simultaneously. When using SVM classification, the most appropriate feature set applied in determining a jamming situation shows an accuracy of 98% or higher. Furthermore, more advanced jamming attacks need to be considered for preparation of more reliable and safer autonomous ITS services. Such research can use vehicular communication transmission and reception data based on selected published datasets. In this paper, an additional adversarial defense algorithm using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is proposed, which assumes that evolutionary attacks of the jammer will attempt to confuse the trained classifier. The simulation results show that applying DBSCAN can improve the accuracy by elimination of outliers before conducting classification testing.

show abstract

Section: Adversarial Sanitation Defensementioning

confidence: 99%

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

Kim¹,

Chung²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A method for detecting DDoS attacks using entropy DBSCAN clustering technique was proposed by Anteneh Girma et al . [ 25 ] . SUN Jian et al proposed a multi‐exemplar affinity propagation (MEAP) clustering algorithm for user access behavior.…”

Section: Related Workmentioning

confidence: 99%

“…To our utmost knowledge, very little literature has studied on attack gang mining issues, while there is a lot of researchon DDoS attack discovery and defense. Jian Kang et al proposed a classification method based on similarity and hierarchical clustering to classify tools for launching DDoS attacks [24] . A method for detecting DDoS attacks using entropy DBSCAN clustering technique was proposed by Anteneh Girma et al [25] .…”

Section: Related Workmentioning

confidence: 99%

HiAtGang: How to Mine the Gangs Hidden Behind DDoS Attacks

Zhu

QIU

Rao

et al. 2022

Chinese J of Electronics

View full text Add to dashboard Cite

Identifying and determining behaviors of attack gangs is not only an advanced stage of the network security event tracing and analysis, but also a core step of large‐scale combat and punishment of cyber attacks. Most of the work in the field of distributed denial of service (DDoS) attack analysis has focused on DDoS attack detection, and a part of the work involves the research of DDoS attack sourcing. We find that very little work has been done on the mining and analysis of DDoS attack gangs. DDoS attack gangs naturally have the attributes of human community relations. We propose a framework named HiAtGang, in which we define the concept of the gang detection in DDoS attacks and introduce the community analysis technology into DDoS attack gang analysis. Different attacker clustering algorithms are compared and analyzed. Based on analysis results of massive DDoS attack events that recorded by CNCERT/CC (The National Computer Network Emergency Response Technical Team/Coordination Center of China), the effective gang mining and attribute calibration have been achieved. More than 250 DDoS attack gangs have been successfully tracked. Our research fills the gaps in the field of the DDoS attack gang detection and has supported CNCERT/CC in publishing “Analysis Report on DDoS Attack Resources” for three consecutive years and achieved a good practical effect on combating DDoS attack crimes.

show abstract

“…Clustering task is used to compare industry and business processes [40] and detect outliers. Girma et al used clustering to detect DDOS attacks [41]. Also, the clustering task is used in forensic analysis.…”

Section: Clustering Task In Network Securitymentioning

confidence: 99%

The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

Ibitoye¹,

Rana²,

Shehaby³

et al. 2019

Preprint

View full text Add to dashboard Cite

Machine learning models have made many decision support systems to be faster, more accurate and more efficient. However, applications of machine learning in network security face more disproportionate threat of active adversarial attacks compared to other domains. This is because machine learning applications in network security such as malware detection, intrusion detection, and spam filtering are by themselves adversarial in nature. In what could be considered an arms race between attackers and defenders, adversaries constantly probe machine learning systems with inputs which are explicitly designed to bypass the system and induce a wrong prediction. In this survey, we first provide a taxonomy of machine learning techniques, styles, and algorithms. We then introduce a classification of machine learning in network security applications. Next, we examine various adversarial attacks against machine learning in network security and introduce two classification approaches for adversarial attacks in network security. First, we classify adversarial attacks in network security based on a taxonomy of network security applications. Secondly, we categorize adversarial attacks in network security into a problem space vs. feature space dimensional classification model. We then analyze the various defenses against adversarial attacks on machine learning-based network security applications. We conclude by introducing an adversarial risk model and evaluate several existing adversarial attacks against machine learning in network security using the risk model. We also identify where each attack classification resides within the adversarial risk model.

show abstract

Advanced Machine Language Approach to Detect DDoS Attack Using DBSCAN Clustering Technology with Entropy

Cited by 10 publications

References 12 publications

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

HiAtGang: How to Mine the Gangs Hidden Behind DDoS Attacks

The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey

Contact Info

Product

Resources

About