• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.151-158
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• 2019

FLUSH+RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH+RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH+RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• Convergence Security Journal
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• v.12 no.3
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• pp.59-65
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• 2012

Clouding computing which is developing newly as IT and network technology develops become changed to internet and service environment of company. Especially, it can lend IT resource at low costs and no need to build up infra. Clouding computing environment become popular more and more because various computing environment using virtualization is provided. The attack threat range also becomes wider in proportion to broaden various connection ways and service supply range at these clouding computing. Therefore, intrusion detection system which can protect resource from various attack having malignant attempts is necessary. In this study, we analyzed about characteristic of intrusion detection system at cloud computing environment having big damage than other computing environment when intrusion happen by sharing of resource and virtualization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.91-97
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• 2006

As increasing the network bandwidth, the threat of a network also increases with emerging various new services. For a high-performance network security, It is generally used that high-speed packet classification methods which employ hardware like TCAM. There needs an method using these devices efficiently because they are expensive and their capacity is not sufficient. In this paper, we propose an efficient packet classification using a Ternary-CAM(TCAM) which is widely used device for high-speed packet classification in which we have applied Snort rule set for the well-known intrusion detection system. In order to save the size of an expensive TCAM, we have eliminated duplicated IP addresses and port numbers in the rule according to the partitioning of a table in the TCAM, and we have represented negation and range rules with reduced TCAM size. We also keep advantages of low TCAM capacity consumption and reduce the number of TCAM lookups by decreasing the TCAM partitioning using combining port numbers. According to simulation results on our TCAM partitioning, the size of a TCAM can be reduced by upto 98$\%$ and the performance does not degrade significantly for high-speed packet classification with a large amount of rules.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.146-149
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• 2013

For in recently years, global cellular network service is changing rapidly to 4G. However, the fast introducing of 4G has been going with not enough research about security threat, it could be many kind of vulnerability. Therefore the research about security threat on 4G network is ongoing in external countries, but not sufficient in domestic. particularly in domestic situation of rapidly increased subscribers, The security threats which are hindering stability and usability could make a fatal effect on many users. 4G network should be considered about the feature of mobile network to protect 4G network stable. Mobile network has limited radio resources, it releases the radio resource which is not used in selected time and reallocate when detected the data transmission. Many signaling messages are transferred in the network entities to allocate or release the radio resource. In this paper, it will be introduced the technology to detect signaling DoS traffic hindering the stability and usability of network entities managing the radio resources by huge signaling message from the repetitive wireless connection/release message.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.1-9
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• 2022

As various services are linked to IoT(Internet of Things) and portable communication terminals, cyber attacks that exploit security vulnerabilities of the devices are rapidly increasing. In particular, cyber attacks targeting heterogeneous devices in large-scale network environments through advanced persistent threat (APT) attacks are on the rise. Therefore, in order to improve the effectiveness of the response system in the event of a breach, it is necessary to apply a data enrichment mechanism for the collected artifact data to improve threat analysis and detection performance. Therefore, in this study, by analyzing the data supplementation common elements performed in the existing incident management framework for the artifacts collected for the analysis of intrusion accidents, characteristic elements applicable to the actual system were derived, and based on this, an improved accident analysis framework The prototype structure was presented and the suitability of the derived data supplementary extension elements was verified. Through this, it is expected to improve the detection performance when analyzing cyber incidents targeting artifacts collected from heterogeneous devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3671-3689
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• 2019

Software defined networking brings unique security risks such as control plane saturation attack while enhancing the performance of wireless sensor networks. The attack is a new type of distributed denial of service (DDoS) attack, which is easy to launch. However, it is difficult to detect and hard to defend. In response to this, the attack threat model is discussed firstly, and then a DDoS attack prevention extension, called FuzzyGuard, is proposed. In FuzzyGuard, a control network with both the protection of data flow and the convergence of attack flow is constructed in the data plane by using the idea of independent routing control flow. Then, the attack detection is implemented by fuzzy inference method to output the current security state of the network. Different probabilistic suppression modes are adopted subsequently to deal with the attack flow to cost-effectively reduce the impact of the attack on the network. The prototype is implemented on SDN-WISE and the simulation experiment is carried out. The evaluation results show that FuzzyGuard could effectively protect the normal forwarding of data flow in the attacked state and has a good defensive effect on the control plane saturation attack with lower resource requirements.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.3
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• pp.107-117
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• 2009

Network administrator recognizes the abnormal phenomenon in the managed network by using the alert messages generated in the security devices including the intrusion detection system, intrusion prevention system, firewall, and etc. And then the series of task, which searches for the traffic related to the alert message and analyzes the traffic data, are required to determine where the abnormal phenomenon is the real network security threat or not. There are many alert messages to have to inspect in order to determine the network security situation. Also the much times are needed so that the network administrator can analyze the security condition using existing methods. Therefore, in this paper, we proposed an efficient method for analyzing network security situation using visualization. The proposed method monitors anomalies occurred in the entire IP address's space and displays the detail information of a security event. In addition, it represents the physical locations of the attackers or victims by linking GIS information and IP address. Therefore, it is helpful for network administrator to rapidly analyze the security status of managed network.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.312-318
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• 2021

Lack of knowledge and digital skills is a threat to the information security of the state and society, so the formation and development of organizational culture of information security is extremely important to manage this threat. The purpose of the article is to assess the state of information security of the state and society. The research methodology is based on a quantitative statistical analysis of the information security culture according to the EU-27 2019. The theoretical basis of the study is the theory of defense motivation (PMT), which involves predicting the individual negative consequences of certain events and the desire to minimize them, which determines the motive for protection. The results show the passive behavior of EU citizens in ensuring information security, which is confirmed by the low level of participation in trainings for the development of digital skills and mastery of basic or above basic overall digital skills 56% of the EU population with a deviation of 16%. High risks to information security in the context of damage to information assets, including software and databases, have been identified. Passive behavior of the population also involves the use of standard identification procedures when using the Internet (login, password, SMS). At the same time, 69% of EU citizens are aware of methods of tracking Internet activity and access control capabilities (denial of permission to use personal data, access to geographical location, profile or content on social networking sites or shared online storage, site security checks). Phishing and illegal acquisition of personal data are the biggest threats to EU citizens. It have been identified problems related to information security: restrictions on the purchase of products, Internet banking, provision of personal information, communication, etc. The practical value of this research is the possibility of applying the results in the development of programs of education, training and public awareness of security issues.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.497-506
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• 2024

In this study, we proposed security information and event management for ship as a technology to respond to maritime cybersecurity regulations and evolving cyber threats. We analyze the main technologies of network management system and security information and event management, which are representative technologies for responding to ship cyber security, and propose SIEM for ships based on this. Optimized for ships based on the International Maritime Organization's Maritime Cyber Threat Management Guidelines, IACS UR E26, 27, etc. Derive the main functions of the SIEM for ship, linkage and normalization plan for the ship's heterogeneous equipment, ship's cyber threat and ship detection policy to identify ship's cyber security threats, and ship's operating environment and operating personnel.