• Convergence Security Journal
• /
• v.20 no.5
• /
• pp.11-17
• /
• 2020

With the increase in cases of using Bluetooth devices used in the electric vehicle charging systems, security issues are also raised. Although various technical efforts have beed made to enhance security of bluetooth technology, various attack methods exist. In this paper, we propose an intelligent Bluetooth intrusion detection system based on a well-known machine learning method, Hidden Markov Model, for the purpose of detecting intelligently representative Bluetooth attack methods. The proposed approach combines packet types of H4, which is bluetooth transport layer protocol, and the transport directions of the packet firstly to represent the behavior of current traffic, and uses the temporal deployment of these combined types as the final input features for detecting attacks in real time as well as accurate detection. We construct the experimental environment for the data acquisition and analysis the performance of the proposed system against obtained data set.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.4
• /
• pp.2149-2170
• /
• 2017

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user's session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2002.05a
• /
• pp.252-255
• /
• 2002

As more critical services are provided in the internet, the risk to these services from malicious users increases. Several networks have experienced problems like Denial of Service(DoS) attacks recently. We analyse a network-based denial of service attack, which is called SYM flooding, to TCP-based networks. It occurs by an attacker who sends TCP connection requests with spoofed source address to a target system. Each request causes the targeted system to send instantly data packets out of a limited pool of resources. Then the target system's resources are exhausted and incoming TCP port connections can not be established. The paper is concerned with a detailed analysis of TCP SYN flooding denial of service attack. In this paper, we propose a Real Time Scan Detector(RTSD) mechanism and evaluate it\`s Performance.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.353-357
• /
• 2011

U.S. Obama government is submit a motion to consider cyber attacks on State as a war. 7.7DDoS attack in Korea in 2009 and 3.4 DDoS attacks 2011, the country can be considered about cyber attacks. China hackers access a third country, bypassing South Korea IP by hacking the e-commerce sites with fake account, that incident was damaging finance. In this paper, for WiBro service, DDoS attacks, hackers, security incidents and vulnerabilities to the analysis. From hacker's attack, WiBro service's prognostic relevance by analyzing symptoms and attacks, in real time, Divide Red, Orange, Yellow, Green belonging to the risk rating. For hackers to create a blacklist, to defend against attacks in real-time air-conditioning system is the study of security. WiBro networks for incident tracking and detection after the packets through the national incident response should contribute to the development of technology.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.11
• /
• pp.89-101
• /
• 2023

In order to prevent damages caused by cyber-attacks on nations, businesses, and other entities, anomaly detection techniques for early detection of attackers have been consistently researched. Real-time reduction and false positive reduction are essential to promptly prevent external or internal intrusion attacks. In this study, we hypothesized that the type and frequency of attack events would influence the improvement of anomaly detection true positive rates and reduction of false positive rates. To validate this hypothesis, we utilized the 2015 login log dataset from the Los Alamos National Laboratory. Applying the preprocessed data to representative anomaly detection algorithms, we confirmed that using characteristics that simultaneously consider the type and frequency of attack events is highly effective in reducing false positives and execution time for anomaly detection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4909-4926
• /
• 2020

Cyberattacks penetrate the server and perform various malicious acts such as stealing confidential information, destroying systems, and exposing personal information. To achieve this, attackers perform various malicious actions by infecting endpoints and accessing the internal network. However, the current countermeasures are only anti-viruses that operate in a signature or pattern manner, allowing initial unknown attacks. Endpoint Detection and Response (EDR) technology is focused on providing visibility, and strong countermeasures are lacking. If you fail to respond to the initial attack, it is difficult to respond additionally because malicious behavior like Advanced Persistent Threat (APT) attack does not occur immediately, but occurs over a long period of time. In this paper, we propose a technique that detects an unknown attack using an event log without prior knowledge, although the initial response failed with anti-virus. The proposed technology uses a combination of AutoEncoder and 1D CNN (1-Dimention Convolutional Neural Network) based on semi-supervised learning. The experiment trained a dataset collected over a month in a real-world commercial endpoint environment, and tested the data collected over the next month. As a result of the experiment, 37 unknown attacks were detected in the event log collected for one month in the actual commercial endpoint environment, and 26 of them were verified as malicious through VirusTotal (VT). In the future, it is expected that the proposed model will be applied to EDR technology to form a secure endpoint environment and reduce time and labor costs to effectively detect unknown attacks.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.1
• /
• pp.71-77
• /
• 2006

Several network attacks, such as distributed denial of service (DDoS) attack, present a very serious threat to the stability of the internet. The threat posed by network attacks on large networks, such as the internet, demands effective detection method. Therefore, a simple intrusion detection system on large-scale backbone network is needed for the sake of real-time detection, preemption and detection efficiency. In this paper, in order to discriminate attack traffic from legitimate traffic on backbone links, we suggest a relatively simple statistical measure, entropy, which can track value frequency. Den is conspicuous distinction of entropy values between attack traffic and legitimate traffic. Therefore, we can identify what kind of attack it is as well as detecting the attack traffic using entropy value.

• Journal of Internet Computing and Services
• /
• v.9 no.4
• /
• pp.85-96
• /
• 2008

An authentication procedure on wired and wireless network will be done based on the registration and management process storing both the user's IP address and client device's MAC address information. However, existent MAC address registration/administration mechanisms were weak in MAC Spoofing attack as the attacker can change his/her own MAC address to client's MAC address. Therefore, an advanced mechanism should be proposed to protect the MAC address spoofing attack. But, existing techniques sequentially compare a sequence number on packet with previous one to distinguish the alteration and modification of MAC address. However, they are not sufficient to actively detect and protect the wireless MAC spoofing attack. In this paper, both AirSensor and AP are used in wireless network for collecting the MAC address on wireless packets. And then proposed module is used for detecting and protecting MAC spoofing attack in real time based on MAC Address Lookup table. The proposed mechanism provides enhanced detection/protection performance and it also provides a real time correspondence mechanism on wireless MAC spoofing attack with minimum delay.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.6
• /
• pp.111-121
• /
• 2006

As Internet lastly grows, network attack techniques are transformed and new attack types are appearing. The existing network-based intrusion detection systems detect well known attack, but the false-positive or false-negative against unknown attack is appearing high. In addition, The existing network-based intrusion detection systems is difficult to real time detection against a large network pack data in the network and to response and recognition against new attack type. Therefore, it requires method to heighten the detection rate about a various large dataset and to reduce the false-positive. In this paper, we propose method to reduce the false-positive using multi-level detection algorithm, that is combine the multidimensional Apriori algorithm and the modified Negative Selection algorithm. And we apply this algorithm in intrusion detection and, to be sure, it has a good performance.

• Journal of IKEEE
• /
• v.27 no.4
• /
• pp.531-536
• /
• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.