• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.81-88
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• 2009

This paper proposed a session key exchange and authentication scheme on non-interactive key distribution algorithm using a community member's ID in ubiquitous networks. In ubiquitous network environment, User's context-awareness information is collected and used to provide a context-awareness service for someone who need it. However, in ubiquitous network environment, this kind of the Context-awareness information could be abused by a malicious nodes. The proposed scheme using the community member ID provides a session key exchange and mutual authentication between community members, and supports secure data communication. Also, when exchanging the session key and authenticating each other, this scheme reduces communication overhead and authentication delay compared to the AAA server scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.655-659
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• 2016

In this paper, we propose new detection and detour methods against packet drop attacks for availability in the Internet of Things (IoT) based on the IEEE 802.15.4e and RPL protocol standards that employ IPv6. We consider the rank value of RPL and the consecutive packet drops to improve the detection metrics, and also take into account the use of both sibling and child nodes on a RPL routing path to construct the detour method. Our simulation results show that the proposed detection method is faster than the previous result, and the detour method improves the detour success rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.783-786
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• 2015

Hole attack, which is disturbed transmission function through change of routing information, can cause critical results for MANET as non-infrastructure network. Backhole attack, as a typical hole attack, is one malicious attack which is disabled network transmission function by assumption of transmission data through modification of routing information. It is very important to evaluate transmission performance caused by blackhole attack, because transmission performance of MANET is affective with blackhole attack. In this paper, transmission performance is analyzed with MANET under multiple blackhole attacks caused multiple blackhole nodes. Computer simulation based on NS-2 is used as analysis tool and voice traffic is considered ad application service on MANET.

• Convergence Security Journal
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• v.13 no.6
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• pp.11-16
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• 2013

Mobile node must move optionally and perform the router and the host functions at the same time. These characteristics of nodes have become a potential threatening element of a variety of attacks. In particular, a black hole which malicious node causes packet loss among them is one of the most important issues. In this paper, we propose distributed detection technique using monitoring tables in all node and cooperative detection technique based cluster for an efficient detection of black hole attack. The proposed technique performs by dividing into local detection and cooperative detection process which is composed of process of step 4 in order to improve the accuracy of the attack detection. Cluster head uses a black hole list to cooperative detection. The performance of the proposed technique was evaluated using ns-2 simulator and its excellent performance could be confirmed in the experiment result.

• Current Optics and Photonics
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• v.6 no.5
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• pp.453-462
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• 2022

Blockchain technology provides a decentralized and peer-to-peer network, which has the advantages of transparency and immutability. In this paper, a novel secure authentication scheme applying digital holography to blockchain technology is proposed to protect privacy information in network nodes. The transactional information of the node is chained permanently and immutably in the blockchain to ensure network security. By designing a novel two-dimensional (2D) array data structure of the block, a proof of work (PoW) in the blockchain is executed through digital holography technology to verify true authentication and legal block linkage. A hash generated from the proposed algorithm reveals a random number of 2D array data. The real identity of each node in the network cannot be forged by a hacker's tampering because the privacy information of the node is encrypted using digital holography and stored in the blockchain. The reliability and feasibility of the proposed scheme are analyzed with the help of the research results, which evaluate the effectiveness of the proposed method. Forgery by a malicious node is impossible with the proposed method by rejecting a tampered transaction. The principal application is a secure anonymity system guaranteeing privacy information protection for handling of large information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.147-169
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• 2024

In pace with the development of network technology at lightning speed, social networks have been extensively applied in our lives. However, as social networks retain a large number of users' sensitive information, the openness of this information makes social networks vulnerable to attacks by malicious attackers. To preserve the link privacy of individuals in social networks, an uncertain graph method based on node random response is devised, which satisfies differential privacy while maintaining expected data utility. In this method, to achieve privacy preserving, the random response is applied on nodes to achieve edge modification on an original graph and node differential privacy is introduced to inject uncertainty on the edges. Simultaneously, to keep data utility, a divide and conquer strategy is adopted to decompose the original graph into many sub-graphs and each sub-graph is dealt with separately. In particular, only some larger sub-graphs selected by the exponent mechanism are modified, which further reduces the perturbation to the original graph. The presented method is proven to satisfy differential privacy. The performances of experiments demonstrate that this uncertain graph method can effectively provide a strict privacy guarantee and maintain data utility.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.153-169
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• 2011

Today using Internet environment is considered absolutely essential for establishing corporate marketing strategy. Companies have promoted their products and services through various ways of on-line marketing activities such as providing gifts and points to customers in exchange for participating in events, which is based on customers' membership data. Since companies can use these membership data to enhance their marketing efforts through various data analysis, appropriate website membership management may play an important role in increasing the effectiveness of on-line marketing campaign. Despite the growing interests in proper membership management, however, there have been difficulties in identifying inappropriate members who can weaken on-line marketing effectiveness. In on-line environment, customers tend to not reveal themselves clearly compared to off-line market. Customers who have malicious intent are able to create duplicate IDs by using others' names illegally or faking login information during joining membership. Since the duplicate members are likely to intercept gifts and points that should be sent to appropriate customers who deserve them, this can result in ineffective marketing efforts. Considering that the number of website members and its related marketing costs are significantly increasing, it is necessary for companies to find efficient ways to screen and exclude unfavorable troublemakers who are duplicate members. With this motivation, this study proposes an approach for managing duplicate membership based on the social network analysis and verifies its effectiveness using membership data gathered from real websites. A social network is a social structure made up of actors called nodes, which are tied by one or more specific types of interdependency. Social networks represent the relationship between the nodes and show the direction and strength of the relationship. Various analytical techniques have been proposed based on the social relationships, such as centrality analysis, structural holes analysis, structural equivalents analysis, and so on. Component analysis, one of the social network analysis techniques, deals with the sub-networks that form meaningful information in the group connection. We propose a method for managing duplicate memberships using component analysis. The procedure is as follows. First step is to identify membership attributes that will be used for analyzing relationship patterns among memberships. Membership attributes include ID, telephone number, address, posting time, IP address, and so on. Second step is to compose social matrices based on the identified membership attributes and aggregate the values of each social matrix into a combined social matrix. The combined social matrix represents how strong pairs of nodes are connected together. When a pair of nodes is strongly connected, we expect that those nodes are likely to be duplicate memberships. The combined social matrix is transformed into a binary matrix with '0' or '1' of cell values using a relationship criterion that determines whether the membership is duplicate or not. Third step is to conduct a component analysis for the combined social matrix in order to identify component nodes and isolated nodes. Fourth, identify the number of real memberships and calculate the reliability of website membership based on the component analysis results. The proposed procedure was applied to three real websites operated by a pharmaceutical company. The empirical results showed that the proposed method was superior to the traditional database approach using simple address comparison. In conclusion, this study is expected to shed some light on how social network analysis can enhance a reliable on-line marketing performance by efficiently and effectively identifying duplicate memberships of websites.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.17-31
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• 2020

With the rapid development of block chain technology, attempts have been made to put the block chain technology into practical use in various fields such as finance and logistics, and also in the public sector where data integrity is very important. Defense Operations In addition, strengthening security and ensuring complete integrity of the command communication network is crucial for operational operation under the network-centered operational environment (NCOE). For this purpose, it is necessary to construct a command communication network applying the block chain network. However, the block chain technology up to now can not solve the security issues such as the 51% attack. In particular, the Practical Byzantine fault tolerance (PBFT) algorithm which is now widely used in blockchain, does not have a penalty factor for nodes that behave maliciously, and there is a problem of failure to make a consensus even if malicious nodes are more than 33% of all nodes. In this paper, we propose a Adaptive Consensus Bound PBFT (ACB-PBFT) algorithm that incorporates a penalty mechanism for anomalous behavior by combining the Trust model to improve the security of the PBFT, which is the main agreement algorithm of the blockchain.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.69-78
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• 2011

A large-scale sensor network usually operates in open and unattended environments, hence individual sensor node is vulnerable to various attacks. Therefore, malicious attackers can physically capture sensor nodes and inject false reports into the network easily through compromised nodes. These false reports are forwarded to the base station. The false report injection attack causes not only false alarms, but also the depletion of the restricted energy resources in a battery powered network. The statistical en-route filtering (SEF) mechanism was proposed to detect and drop false reports en route. In SEF, the choice of routing paths largely affect the energy consumption rate and the detecting power of the false report. To sustain the secure routing path, when and how to execute the path re-selection is greatly need by reason of the frequent network topology change and the nodes's limitations. In this paper, the regional path re-selection period determination method is proposed for efficient usage of the limited energy resource. A fuzzy logic system is exploited in order to dynamically determine the path re-selection period and compose the routing path. The simulation results show that up to 50% of the energy is saved by applying the proposed method.