IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. GAN is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks.

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Terrorist attacks are increasing each year as are the number of deaths associated with them. Recent incidents have seen a shift in tactics with the use of multiple terrorists across multiple locations with firearms or knives, referred to as the marauding terrorist attack. These methods are becoming more prevalent alongside the use of vehicles deliberately aimed at pedestrians. Management of these incidents can be challenging. Not only it involves a large number of casualties but also the management of a dynamic scene in terms of both location and threat from attack. In order to improve response, and potentially outcomes, a system or response needs to have preplanned and practised procedures in place. This article reviews major incident management for those unfamiliar with current prehospital practice and details some of the findings from recent marauding terrorist firearm attacks, in particular the evolution of newer scene management tools such as 3 Echo and THREAT. It highlights the importance of haemorrhage control and the public initiatives focusing on actions during a terrorist incident.

Intrusion Detection Systems (IDS) are essential components in preventing malicious traffic from penetrating networks and systems. Recently, these systems have been enhancing their detection ability using machine learning algorithms. This development also forces attackers to look for new methods for evading these advanced Intrusion Detection Systemss. Polymorphic attacks are among potential candidates that can bypass the pattern matching detection systems. To alleviate the danger of polymorphic attacks, the IDS must be trained with datasets that include these attacks. Generative Adversarial Network (GAN) is a method proven in generating adversarial data in the domain of multimedia processing, text, and voice, and can produce a high volume of test data that is indistinguishable from the original training data. In this paper, we propose a model to generate adversarial attacks using Wasserstein GAN (WGAN). The attack data synthesized using the proposed model can be used to train an IDS. To evaluate the trained IDS, we study several techniques for updating the attack feature profile for the generation of polymorphic data. Our results show that by continuously changing the attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks; meanwhile, their detection rates improve incrementally until the polymorphic attack exhausts its profile variables.

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