Wednesday, 27 April 2022

Lupine Publishers| Detecting Distributed Denial-of-Service DDoS Attacks

 Lupine Publishers| Journal Computer Sciences & Applications


Since the number of damage cases resulting from distributed denial-of-service (DDoS) attacks has recently been increasing, the need for agile detection and appropriate response mechanisms against DDoS attacks has also been increasing. The latest DDoS attack has the property of swift propagation speed and various attack patterns. There is therefore a need to create a lighter mechanism to detect and respond to such new and transformed types of attacks with greater speed. In a wireless network system, the security is a main concern for a user.


Security of information is of utmost importance to organization striving to survive in a competitive marketplace. Network security has been an issue since computer networks became prevalent, most especially now that internet is changing the face of computing. As dependency on Internet increases on daily basis for business transaction, so also is cyber-attacks by intruder who exploit flaws in Internet architecture, protocol, operating systems and application software to carry out their nefarious activities Such hosts can be compromised within a short time to run arbitrary and potentially malicious attack code transported in a worm or virus or injected through installed backdoors. Distributed denial of service (DDoS) use such poorly secured hosts as attack platform and cause degradation and interruption of Internet services, which result in major financial losses, especially if commercial servers are affected (Duberdorfer, 2004).

Related Works

Brignoli et al. [1] proposed DDoS detection based on traffic selfsimilarity estimation, this approach is a relatively new approach which is built on the notion that undisturbed network traffic displays fractal like properties. These fractal-like properties are known to degrade in presence of abnormal traffic conditions like DDoS. Detection is possible by observing the changes in the level of self-similarity in the traffic flow at the target of the attack. Existing literature assumes that DDoS traffic lacks the self-similar properties of undisturbed traffic. The researcher shows how existing bot- nets could be used to generate a self-similar traffic flow and thus break such assumptions. Streilien et al,2005. Worked on detection of DoS attacks through the polling of Remote Monitoring (RMON) capable devices. The researchers developed a detection algorithm for simulated flood-based DoS attacks that achieves a high detection rate and low false alarm rate.

Yeonhee Lee [2] focused on a scalability issue of the anomaly detection and introduced a Hadoop based DDoS detection scheme to detect multiple attacks from a huge volume of traffic. Different from other single host-based approaches trying to enhance memory efficiency or to customize process complexity, our method leverages Hadoop to solve the scalability issue by parallel data processing. From experiments, we show that a simple counterbased DDoS attack detection method could be easily implemented in Hadoop and shows its performance gain of using multiple nodes in parallel. It is expected that a signature-based approach could be well suited with Hadoop. However, we need to tackle a problem to develop a real time defense system, because the current Hadoop is oriented to batch processing.

Proposed System Architecture of Intrusion Detection Based on Association Rule

The structure of the proposed architecture for real time detection of Dos instruction detection via association rule mining, it is divided into two phases: learning and testing. The network sniffer processed the tcpdump binary into standard format putting into learning, during the learning phase, duplicate records as well as columns with single same data were expunge from the record so as to reduce operational. Another table Hashmap was created by the classification model to keep track of the count of various likely classmark that can match the current read network traffic, this table will be discarded once the classmark with highest count had been selected. Depicted in Table 1 is the Association rule classifier algorithm (Tables 2-4).

Table 1: Association Rule Mining Classifier Algorithm.


Table 2: Sample Rules.


Table 3: Sampled number combination table.


Table 4: Sample Network Traffic Data.


System Implementation

This chapter presents implementation of Association rule classifier model, documentation of the designed system and the user interfaces. The software and hardware requirement needed for the system and also the testing of the system for verification and validation of functions, as well as the result [3-10].

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