Detection of distributed denial-of-service (DDoS) attack with hyperparameter tuning based on machine learning approach

Distributed Denial-of-Service (DDoS) attack is a malicious cyber-attack which targets availability element in CIA triad and to disrupt the availability of network services of a target by performing a huge malicious traffic flood. To conduct the study, a standard benchmark dataset DDoS Attack SDN Dataset is applied. EDA and Data Pre-processing are performed to ensure a clean dataset is produced for obtaining an accurate and meaningful detection performance results. Hyperparameter tuning is performed to enhance the detection performance of the models. It is proposed that DNN shows the promising results as it has shown 99.84% accuracy to detect DDoS attack after performing hyperparameter tuning. It is observed that hyperparameter tuning has improved and increased most of the performance results of DNN and DT, with increment 4.84% in DT while 0.97% in DNN. Besides, the detection results have been increased and their false detection has been reduced. This study could help to reduce the dwell time of DDoS attack, increase the Mean Time To Contain (MTTC) and avoid alarm fatigue

A survey on current malicious javascript behavior of infected web content in detection of malicious web pages

In recent years, the advance growth of cybercrime has become an urgent issue to the security authorities. With the improvement of web technologies enable attackers to launch the web-based attacks and other malicious code easily without having prior expert knowledge. Recently, JavaScript has become the most common attack construction language as it is the primary browser scripting language which allow developer to develop sophisticated client-side interfaces for web application. This lead to the growth of malicious websites and as main platform for distributing malware or malicious script to the user's computer when the user access to these webpages. Initial act and detection on such threats early in a timely manner is vital in order to reduce the damages which have caused billions of dollars lost every year. A number of approaches have been proposed to detect malicious web pages. However, the efficient detection of malicious web pages previously has generated many false alarm by the use of sophisticated obfuscation techniques in benign JavaScript code in web pages. Therefore, in this paper, a thoroughly survey and detailed understanding of malicious JavaScript code features will be provided, which have been collected from the web content. We conduct a thorough analysis and studies on the usage of different JavaScript features and JavaScript detection technique systematically and present the most important features of malicious threats in web pages. Then the analysis will be presented along with different dimensions (features representation, detection techniques analysis, and sample of malicious script)

Performance comparison of priority rule scheduling algorithms using different inter arrival time jobs in grid environment

Recent advancement in meta-heuristics grid scheduling studies have applied various techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Ant Colony Optimization (ACO) to solve the grid scheduling problem. All of these technique requires an initial scheduler in order to initiate the scheduling process and the priority rule algorithms will typically be used. However, from the literature, none of these studies elaborate and justify their selection of a particular priority rule algorithms over another. Since the initial scheduler can significantly affect the entire scheduling process, it is important that the correct initial scheduler be selected. In this paper we quantitatively compared six initial scheduler algorithms to determine the best algorithm performance. We believe the performance comparison would enable users to utilize the best initial scheduler to fit their meta-heuristics grid scheduling studies

An intelligence technique for denial of service (DoS) attack detection

The emergent damage to computer network keeps increasing due to an extensive and prevalent connectivity on the Internet. Nowadays, attack detection strategies have become the most vital component in computer security despite the main preventive measure in detecting the attacks. The main issue with current detection systems is the inability to detect the malicious activity in certain circumstances. Most of the current intrusion detection systems implemented nowadays depend on expert systems where new attacks are not detectable. Therefore, this paper concern about Denial of Service (DoS) attack, detection using Neural Network. The data used in training and testing was KDD 99 data set based on the Defense Advanced Research Projects Agency (DARPA) intrusion detection programme, which is publicly accessible by Lincoln Labs. Special features of connection records have been acknowledged to be used in DoS attacks. The result from this experiment will show the effectiveness of Neural Network using the backpropagation learning algorithm for detecting DoS attack