• Journal of Information Processing Systems
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• v.15 no.1
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• pp.100-115
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• 2019

Malicious code distribution on the Internet is one of the most critical Internet-based threats and distribution technology has evolved to bypass detection systems. As a new defense against the detection bypass technology of malicious attackers, this study proposes the automated tracing of malicious websites in a malware distribution network (MDN). The proposed technology extracts automated links and classifies websites into malicious and normal websites based on link structure. Even if attackers use a new distribution technology, website classification is possible as long as the connections are established through automated links. The use of a real web-browser and proxy server enables an adequate response to attackers' perception of analysis environments and evasion technology and prevents analysis environments from being infected by malicious code. The validity and accuracy of the proposed method for classification are verified using 20,000 links, 10,000 each from normal and malicious websites.

• Journal of IKEEE
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• v.26 no.3
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• pp.380-388
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• 2022

The traditional malware detection technologies collect known malicious programs and analyze their characteristics. Then such a detection technology makes a blacklist based on the analyzed malicious characteristics and checks programs in the user's system based on the blacklist to determine whether each program is malware. However, such an approach can detect known malicious programs, but responding to unknown or variant malware is challenging. In addition, since such detection technologies generally monitor all programs in the system in real-time, there is a disadvantage that they can degrade the system performance. In order to solve such problems, various methods have been proposed to analyze major behaviors of malicious programs and to respond to them. The main characteristic of ransomware is to access and encrypt the user's file. So, a new approach is to produce the whitelist of programs installed in the user's system and allow the only programs listed on the whitelist to access the user's files. However, although it applies such an approach, attackers can still perform malicious behavior by performing a DLL(Dynamic-Link Library) injection attack on a regular program registered on the whitelist. This paper proposes a method to respond effectively to attacks using DLL injection.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.2
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• pp.382-389
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• 2009

As the use of mobile device is popularized, the needs of variable services of WiMAX technique and the importance of security is increasing. There is a problem that can be easily attacked from a malicious attack because the action is achieved connectionlessly between neighbor link establishing procedure and TEK exchange procedure in mobile WiMAX even though typical 1 hop network security technique is adapted to WiMAX for satisfying these security requirement. In this paper, security connected mechanism which safely connects neighbor link establishing procedure of WiMAX and TEK exchange procedure additional to the basic function provided by IEEE 802.16e standard to satisfy security requirement of mobile WiMAX is proposed. The proposed mechanism strengthens the function of security about SS and BS by application random number and private value which generated by SS and BS to public key of neighbor link establishing procedure and TEK exchange procedure. Also, we can prevent from inside attack like man-in-the-middle which can occur in the request of TEK through cryptographic connection of neighbor link establishing procedure and TEK exchange procedure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.667-677
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• 2012

Vulnerable web applications have been the primary method used by the attackers to spread their malware to a large number of victims. Such attacks commonly make use of malicious links to remotely execute a rather advanced malicious code. The attackers often deploy malwares that utilizes unknown vulnerabilities so-called "zero-day vulnerabilities." The existing computer vaccines are mostly signature-based and thus are effective only against known attack patterns, but not capable of detecting zero-days attacks. To mitigate such limitations of the current solutions, there have been a numerous works that takes a behavior-based approach to improve detection against unknown malwares. However, behavior-based solutions arbitrarily introduced a several limitations that made them unsuitable for real-life situations. This paper proposes an advanced web browser based malicious behavior detection system that solves the problems and limitations of the previous approaches.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.248-252
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• 2012

The sensor network standard DASH7 was proposed to improve transmission quality and low power communication. Specifications for the standard are currently being developed, so the security specification has not been firmly implemented. However, without a security specification, a network cannot work due to threats from malicious users. Thus we must ensure confidentiality and authentication of data packets by using a cryptography method. To contribute to the DASH7 security specification, this paper shows the implementation results of network and data link layer security by using advanced encryption standard (AES) counter with CBC-MAC (CCM) over CC430 sensor nodes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.89-100
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• 2011

As the Internet usage with web browser is more increasing, the web-based malware which is distributed in websites is going to more serious problem than ever. The central type malicious website detection method based on crawling has the problem that the cost of detection is increasing geometrically if the crawling level is lowered more. In this paper, we proposed a security tool based on web browser which can detect the malicious web pages dynamically and support user's safe web browsing by stopping navigation to a certain malicious URL injected to those web pages. By applying these tools with many distributed web browser users, all those users get to participate in malicious website detection and feedback. As a result, we can detect the lower link level of websites distributed and dynamically.

• 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.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.