• Journal of Industrial Technology
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• v.22 no.B
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• pp.147-153
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• 2002

It is very important to detect and remove original sources of DOS (Denial of Service) attacks or connection oriented/connectionless attacks. In this paper, we implement a traceback system that does not require the reaction of routers and administrators and does not need log data. We bring in a traceback server and traceback agents in each network and use sniffing and spoofing schemes. Finally, the traceback server detects attacking hosts using information transmitted from traceback agents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.29-37
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• 2020

Conventional firewalls cannot cope with attacks immediately. It is because security professionals or administrators need to analyze them and enter relevant policies to the firewalls. In addition, those policies may often block even normal accesses. Even though the packet themselves are normal, there exist many attacks that cause denial of service due to the inflow of a large amount of those packets. In this paper, we propose a method to block attacks such as Flooding, Spoofing and Scanning while allowing normal accesses based on whitelist policies which are automatedly generated by learning normal access patterns.

• Annual Conference of KIPS
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• 2021.11a
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• pp.651-654
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• 2021

Face anti-spoofing (FAS) techniques play a significant role in the defense of facial recognition systems against spoofing attacks. Existing FAS methods achieve the great performance depending on annotated additional modalities. However, labeling these high-cost modalities need a lot of manpower, device resources and time. In this work, we proposed to use self-transforming modalities instead the annotated modalities. Three different modalities based on frequency domain and temporal domain are applied and analyzed. Intuitive visualization analysis shows the advantages of each modality. Comprehensive experiments in both the CNN-based and transformer-based architecture with various modalities combination demonstrate that self-transforming modalities improve the vanilla network a lot. The codes are available at https://github.com/chenmou0410/FAS-Challenge2021.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.217-228
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• 2024

LiDAR(Light Detection And Ranging) sensors, which play a pivotal role among cameras, RADAR(RAdio Detection And Ranging), and ultrasonic sensors for the safe operation of autonomous vehicles, can recognize and detect objects in 360 degrees. However, since LiDAR sensors use lasers to measure distance, they are vulnerable to attackers and face various security threats. In this paper, we examine several security threats against LiDAR sensors: relay, spoofing, and replay attacks, analyze the possibility and impact of physical jamming attacks, and analyze the risk these attacks pose to the reliability of autonomous driving systems. Through experiments, we show that jamming attacks can cause errors in the ranging ability of LiDAR sensors. With vehicle-to-vehicle (V2V) communication, multi-sensor fusion under development and LiDAR anomaly data detection, this work aims to provide a basic direction for countermeasures against these threats enhancing the security of autonomous vehicles, and verify the practical applicability and effectiveness of the proposed countermeasures in future research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.168-182
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• 2020

Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems. Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability. In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel. Then a novel feature is proposed to detect the replay attacks. To further boost the detection performance, four post-processing methods using normalization techniques are investigated. We evaluate our proposed method on the ASVspoof 2017 dataset. The experimental results show that our approach outperforms the competing methods in terms of detection accuracy. More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.307-317
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• 2012

Rapid development of the IT technology and mobile communications has increasingly improved many kinds of digital devices arise, as well as the mobile technology. However, the attacks (virus, hacking and Ip spoofing etc) have also increasingly grown dogged on any region including the society security. As the visual data is prone to copy, delete and move etc, it is necessary that attesting to the integrity of forensics evidence is crucial, as well as data transmission security. This paper presents a framework model using digital forensics method and the results of its performance evaluation for mobile security. The results show that the integrity of the visual data can be obtain with high security and make a proposal refer to prevention of Mobile IP Spoofing attack using our hashing data.

• Journal of IKEEE
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• v.28 no.1
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• pp.6-13
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• 2024

This paper proposes an approach to detect abnormal data in automotive controller area network (CAN) using an unsupervised learning model, i.e. autoencoder and Gaussian kernel density estimation function. The proposed autoencoder model is trained with only message ID of CAN data frames. Afterwards, by employing the Gaussian kernel density estimation function, it effectively detects abnormal data based on the trained model characterized by the optimally determined number of frames and a loss threshold. It was verified and evaluated using four types of attack data, i.e. DoS attacks, gear spoofing attacks, RPM spoofing attacks, and fuzzy attacks. Compared with conventional unsupervised learning-based models, it has achieved over 99% detection performance across all evaluation metrics.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.1
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• pp.14-26
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• 2008

IP over Ethernet technology widely used as Internet access uses the ARP(Address Resolution Protocol) that translates an ip address to the corresponding MAC address. recently, there are ARP security attacks that intentionally modify the IP address and its corresponding MAC address, utilizing various tools like "snoopspy". Since ARP attacks can redirect packets to different MAC address other than destination, attackers can eavesdrop packets, change their contents, or hijack the connection. Because the ARP attack is performed at data link layer, it can not be protected by security mechanisms such as Secure Shell(SSH) or Secure Sockets Layer(SSL). Thus, in this paper, we classify the ARP attack into downstream ARP spoofing attack and upstream ARP redirection attack, and propose a new security mechanism using DHCP information for acquisition of IP address. We propose a "DHCP snoop mechanism" or "DHCP sniffing/inspection mechanism" for ARP spoofing attack, and a "static binding mechanism" for ARP redirection attack. The proposed security mechanisms for ARP attacks can be widely used to reinforce the security of the next generation internet access networks including BcN.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.449-460
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• 2021

ASVspoof 2017 deals with detection of replay attacks and aims to classify real human voices and fake voices. The spoofed voice refers to the voice that reproduces the original voice by different types of microphones and speakers. data augmentation research on image data has been actively conducted, and several studies have been conducted to attempt data augmentation on voice. However, there are not many attempts to augment data for voice replay attacks, so this paper explores how audio modification through data augmentation techniques affects the detection of replay attacks. A total of 7 data augmentation techniques were applied, and among them, dynamic value change (DVC) and pitch techniques helped improve performance. DVC and pitch showed an improvement of about 8% of the base model EER, and DVC in particular showed noticeable improvement in accuracy in some environments among 57 replay configurations. The greatest increase was achieved in RC53, and DVC led to an approximately 45% improvement in base model accuracy. The high-end recording and playback devices that were previously difficult to detect were well identified. Based on this study, we found that the DVC and pitch data augmentation techniques are helpful in improving performance in the voice spoofing detection problem.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.357-364
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• 2015

Tags and Readers is receiving and sending the data using the wireless communication in the RFID environment. Therefore, it could allow an attacker to participate in the network without the physical constraints, which can be easily exposed to a variety of attacks, such as taps and data forgery. Also, it is not easy to apply the security techniques to defend external attacks because the resource constraints of RFID tags is high. In this paper, new tag-reader mutual authentication protocol is proposed to protect the external cyber attacks such as spoofing attacks, replay attacks, traffic analysis attacks, location tracking attacks. The performance evaluation of the proposed mutual authentication protocol is performed and the simulation results are presented.