• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.937-939
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• 2014

Recently, an intelligent APT(Advanced Persistent Threat) attack which aims to a special target is getting to be greatly increased. It is very hard to protect with existing intrusion detection methods because of the difficulties to protect the initial intrusion of malicious code. In this paper, we analyze out-bound traffics to prevent call-back step after malicious code intrusion, and propose an APT malicious traffic detection method with considering of trust. The proposed method is expected to provide a basement to improve the detection rate in comparing with that of existing detection methods.

• Journal of IKEEE
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• v.23 no.4
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• pp.1469-1472
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• 2019

CAN bus in automotive applications does not assign node addresses. When a node is hacked and it transmits malicious data frame, it is difficult to resolve which node is hacked. However, this CAN bus internal attack seriously threatens the safety of a car, so a prompt counterattack is necessary in the CAN bus physical layer. This paper proposes a counterattack method against malicious CAN bus internal attack. When a malicious data frame is detected, an intrusion detection system in the CAN bus increases the error counter of the malicious node. Then, the malicious node is off from the bus when its error counter exceeds its limit. A CAN controller with the proposed method is implemented in Verilog HDL, and the proposed method is proved to counterattack against malicious CAN bus internal attack.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1849-1856
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• 2013

DOS attack, the short of Denial of Service attack is an internet intrusion technique which harasses service availability of legitimate users. To respond the DDoS attack, a lot of methods focusing attack source, target and intermediate network, have been proposed, but there have not been a clear solution. In this paper, we purpose the prevention of malicious activity and early detection of DDoS attack by detecting and removing the activity of botnets, or other malicious codes. For the purpose, the proposed method monitors the network traffic, especially DSN traffic, which is originated from botnets or malicious codes.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1657-1673
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• 2023

With the advancement of information technology, criminals employ multiple cyberspaces to promote cybercrime. To combat cybercrime and cyber dangers, banks and financial institutions use artificial intelligence (AI). AI technologies assist the banking sector to develop and grow in many ways. Transparency and explanation of AI's ability are required to preserve trust. Deep learning protects client behavior and interest data. Deep learning techniques may anticipate cyber-attack behavior, allowing for secure banking transactions. This proposed approach is based on a user-centric design that safeguards people's private data over banking. Here, initially, the attack data can be generated over banking transactions. Routing is done for the configuration of the nodes. Then, the obtained data can be preprocessed for removing the errors. Followed by hierarchical network feature extraction can be used to identify the abnormal features related to the attack. Finally, the user data can be protected and the malicious attack in the transmission route can be identified by using the Wrapper stepwise ResNet classifier. The proposed work outperforms other techniques in terms of attack detection and accuracy, and the findings are depicted in the graphical format by employing the Python tool.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.676-682
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• 2011

Sybil attack can disrupt proper operations of wireless sensor network by forging its sensor node to multiple identities. To protect the sensor network from such an attack, a number of countermeasure methods based on RSSI (Received Signal Strength Indicator) and LQI (Link Quality Indicator) have been proposed. However, previous works on the Sybil attack detection do not consider the fact that Sybil nodes can change their RSSI and LQI strength for their malicious purposes. In this paper, we present a Sybil attack detection method based on a transmission power range. Our proposed method initially measures range of RSSI and LQI from sensor nodes, and then set the minimum, maximum and average RSSI and LQI strength value. After initialization, monitoring nodes request that each sensor node transmits data with different transmission power strengths. If the value measured by monitoring node is out of the range in transmission power strengths, the node is considered as a malicious node.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.153-160
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• 2010

MANET composed mobile nodes without central concentration control like base station communicate through multi-hop route among nodes. Accordingly, it is hard to maintain stability of network because topology of network change at any time owing to movement of mobile nodes. MANET has security problems because of node mobility and needs intrusion detection system that can detect attack of malicious nodes. Therefore, system is protected from malicious attack of intruder in this environment and it has to correspond to attack immediately. In this paper, we propose intrusion detection system based on rules in order to more accurate intrusion detection. Cluster head perform role of monitor node to raise monitor efficiency of packet. In order to evaluate performance of proposed method, we used jamming attack, selective forwarding attack, repetition attack.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.259-261
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• 2022

Recently, malicious code is not a single technique, but several techniques are combined and merged, and only important parts are extracted. As new malicious codes are created and transformed, attack patterns are gradually diversified and attack targets are also diversifying. In particular, the number of damage cases caused by malicious actions in corporate security is increasing over time. However, even if attackers combine several malicious codes, the APIs for each type of malicious code are repeatedly used and there is a high possibility that the patterns and names of the APIs are similar. For this reason, this paper proposes a classification technique that finds patterns of APIs frequently used in malicious code, calculates the meaning and similarity of APIs, and determines the level of risk.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3738-3760
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• 2022

Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision's correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack types, we provide in-depth simulations to demonstrate the efficiency and security of the proposed WD3S.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4191-4203
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• 2015

WebView is a vital component in smartphone platforms like Android, Windows and iOS that enables smartphone applications (apps) to embed a simple yet powerful web browser inside them. WebView not only provides the same functionalities as web browser, it, more importantly, enables a rich interaction between apps and webpages loaded inside the WebView. However, the design and the features of WebView lays path to tamper the sandbox protection mechanism implemented by browsers. As a consequence, malicious attacks can be launched either against the apps or by the apps through the exploitation of WebView APIs. This paper presents a critical attack called Supplementary Event-Listener Injection (SEI) attack which adds auxiliary event listeners, for executing malicious activities, on the HTML elements in the webpage loaded by the WebView via JavaScript Injection. This paper also proposes an automated static analysis system for analyzing WebView embedded apps to classify the kind of vulnerability possessed by them and a solution for the mitigation of the attack.