• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2512-2531
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• 2014

We propose a new Distributed Denial of Service (DDoS) defense mechanism that protects http web servers from application-level DDoS attacks based on the two methodologies: whitelist-based admission control and busy period-based attack flow detection. The attack flow detection mechanism detects attach flows based on the symptom or stress at the server, since it is getting more difficult to identify bad flows only based on the incoming traffic patterns. The stress is measured by the time interval during which a given client makes the server busy, referred to as a client-induced server busy period (CSBP). We also need to protect the servers from a sudden surge of attack flows even before the malicious flows are identified by the attack flow detection mechanism. Thus, we use whitelist-based admission control mechanism additionally to control the load on the servers. We evaluate the performance of the proposed scheme via simulation and experiment. The simulation results show that our defense system can mitigate DDoS attacks effectively even under a large number of attack flows, on the order of thousands, and the experiment results show that our defense system deployed on a linux machine is sufficiently lightweight to handle packets arriving at a rate close to the link rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3738-3750
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• 2015

Sybil attack is a special kind of attack which is difficult to be detected in Wireless Sensor Network (WSN). So a lightweight detection mechanism based on LEACH-RSSI-ID (LRD) is proposed in this paper. Due to the characteristic of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, none of nodes can be the cluster head forever.

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

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1255-1261
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• 2020

Cache side-channel attack is a class of attacks to retrieve sensitive information from a system by exploiting shared cache resources in CPUs. As the attacks are delivered to wide range of environments from mobile systems to cloud systems recently, many detection strategies have been proposed. Since the conventional cache side-channel attacks are likely to incur tremendous number of cache events, most of the previous detection mechanisms were designed to carefully monitor mostly cache events. However, recently proposed attacks tend to incur less cache events during the attack. PRIME+ABORT attack, for example, leverages the Intel TSX instead of accessing cache to measure access time. Because of the characteristic, attack detection mechanisms based on cache events may hardly detect the attack. In this paper, we conduct an in-depth analysis of the PRIME+ABORT attack to identify the other useful hardware events for detection rather than cache events. Based on our finding, we present a novel mechanism called PRIME+ABORT Detector to detect the PRIME+ABORT attack and demonstrate that the detection mechanism can achieve 99.5% success rates with 0.3% performance overhead.

• Journal of Advanced Navigation Technology
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• v.14 no.5
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• pp.725-736
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• 2010

Nowadays, followed the internet service contents increasing makes also increase attack case on the web system. Usually web attack use mixed many kinds of attack mechanism for successfully attack to the server system. These increasing of the kinds attack mechanism, however web attack defence mechanism is not follow the spread of the attack. Therefore, for the defends web application, web attack should be categorizing and analysing for the effective defense. In this paper, we analyze web attack specification evidence and behavior system that use for effective expressions what we proposed. Also, we generate web attack scenario, it is for using verification of our proposed expressions.

• ETRI Journal
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• v.37 no.6
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• pp.1108-1119
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• 2015

Multicast communication of mobile ad hoc networks is vulnerable to internal attacks due to its routing structure and high scalability of its participants. Though existing intrusion detection systems (IDSs) act smartly to defend against attack strategies, adversaries also accordingly update their attacking plans intelligently so as to intervene in successful defending schemes. In our work, we present a novel indirect internal stealthy attack on a tree-based multicast routing protocol. Such an indirect stealthy attack intelligently makes neighbor nodes drop their routing-layer unicast control packets instead of processing or forwarding them. The adversary targets the collision avoidance mechanism of the Medium Access Control (MAC) protocol to indirectly affect the routing layer process. Simulation results show the success of this attacking strategy over the existing "stealthy attack in wireless ad hoc networks: detection and countermeasure (SADEC)" detection system. We design a cross-layer automata-based stealthy attack on multicast routing protocols (SAMRP) attacker detection system to identify and isolate the proposed attacker. NS-2 simulation and analytical results show the efficient performance, against an indirect internal stealthy attack, of SAMRP over the existing SADEC and BLM attacker detection systems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.208-218
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• 2006

In this paper, we introduce the creation methods of attack detection model using data mining technologies that can classify the latest attack types, and can detect the modification of existing attacks as well as the novel attacks. Also, we evaluate comparatively these attack detection models in the view of detection accuracy and detection time. As the important factors for creating detection models, there are data, attribute, and detection algorithm. Thus, we used NetFlow data gathered at the real network, and KDD Cup 1999 data for the experiment in large quantities. And for attribute selection, we used a heuristic method and a theoretical method using decision tree algorithm. We evaluate comparatively detection models using a single supervised/unsupervised data mining approach and a combined supervised data mining approach. As a result, although a combined supervised data mining approach required more modeling time, it had better detection rate. All models using data mining techniques could detect the attacks within 1 second, thus these approaches could prove the real-time detection. Also, our experimental results for anomaly detection showed that our approaches provided the detection possibility for novel attack, and especially SOM model provided the additional information about existing attack that is similar to novel attack.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.3
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• pp.479-490
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• 2013

Recently, the number of attacks by malicious application has significantly increased, targeting Android-platform mobile terminal such as Samsung Galaxy Note and Galaxy Tab 10.1. The malicious application can be distributed to currently used mobile devices through open market masquerading as an normal application. An attacker inserts malicious code into an application, which might threaten privacy by rooting attack. Once the rooting attack is successful, malicious code can collect and steal private data stored in mobile terminal, for example, SMS messages, contacts list, and public key certificate for banking. To protect the private information from the malicious attack, malicious code detection, rooting attack detection and countermeasure method are required. To meet this end, this paper investigates rooting attack mechanism for Android-platform mobile terminal. Based on that, this paper proposes countermeasure system that enables to extract and collect events related to attacks occurring from mobile terminal, which contributes to active protection from malicious attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.2
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• pp.279-286
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• 2013

The recent power shortages due to surge in demand for electricity highlights the importance of smart grid technologies for efficient use of power. The experimental content for vulnerability against availability of smart meter, an essential component in smart grid networks, has been reported. Designing availability protection mechanism to boost the realization possibilities of the secure smart grid is essential. In this paper, we propose a mechanism to detect the availability infringement attack for smart meter and also to find anomaly nodes through analyzing smart grid structure and traffic patterns. The proposed detection mechanism uses approximate entropy technique to decrease the detection load and increase the detection rate with few samples and utilizes the signal information(CIR or RSSI, etc.) that the anomaly node can not be changed to find the anomaly nodes. Finally simulation results of proposed method show that the detection performance and the feasibility.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.375-381
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• 2010

A flooding attack, such as DoS or Worm, can be easily created or even downloaded from the Internet, thus, it is one of the main threats to servers on the Internet. This paper presents an online real-time network response system, which can determine whether a LAN is suffering from a flooding attack within a very short time unit. The detection engine of the system is based on the incremental mining of fuzzy association rules from network packets, in which membership functions of fuzzy variables are optimized by a genetic algorithm. The incremental mining approach makes the system suitable for detecting, and thus, responding to an attack in real-time. This system is evaluated by 47 flooding attacks, only one of which is missed, with no false positives occurring. The proposed online system belongs to anomaly detection, not misuse detection. Moreover, a mechanism for dynamic firewall updating is embedded in the proposed system for the function of eliminating suspicious connections when necessary.