Predicting the Effects of DDoS Attacks on a Network of Critical Infrastructures

Over the last decade, the level of critical infrastructure technology has been steadily transforming in order to keep pace with the growing demand for the services offered. The implementation of the smart grid, which relies on a complex and intelligent level of interconnectivity, is one example of how vital amenity provision is being refined. However, with this change, the risk of threats from the digital domain must be calculated. Superior interconnectivity between infrastructures means that the future cascading impacts of successful cyber-attacks are unknown. One such threat being faced in the digital domain is the Distributed Denial of Service (DDoS) attack. A DDoS has the goal of incapacitating a server, network or service, by barraging a target with external data traffic in the form of communication requests. DDoS have the potential to cause a critical infrastructure outage, and the subsequent impact on a network of such infrastructures is yet unknown. In this paper, an approach for assessing the future impacts of a cyber-attack in a network of critical infrastructures is presented; with a focus on DDoS attacks. A simulation of a critical infrastructure network provides data to represent both normal run-time and an attack scenario. Using this dataset, a technique for assessing the future impact of disruptions on integrated critical infrastructure network, is demonstrated. Index Terms—Critical Infrastructure, Cyber-Attack Distributed Denial of Service, Simulation, Cascading Failur

Analisis Statistik Log Jaringan untuk Deteksi Serangan Ddos Berbasis Neural Network

Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume, intensitas, dan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi serangan DDoS, berdasarkan log jaringan yang dianalisis secara statistik dengan fungsi neural network sebagai metode deteksi. Data pelatihan dan pengujian diambil dari CAIDA DDoS Attack 2007 dan simulasi mandiri. Pengujian terhadap metode analisis statistik terhadap log jaringan dengan fungsi neural network sebagai metode deteksi menghasilkan prosentase rata-rata pengenalan terhadap tiga kondisi jaringan (normal, slow DDoS, dan DDoS) sebesar 90,52%. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem Intrusion Detection System (IDS) dalam meramalkan terjadinya serangan DDo

A Novel Approach to Mitigate DDoS Attack Using Gateway Mechanism

Intelligent and economical sensors, connected to the network via wireless links and distributed in large quantities, offer unprecedented opportunities to monitor and control homes, cities and the environment. In addition, sensors connected to the network use a wide range of applications within the defence area, generating new features for recognition and surveillance and various tactical applications. Denial of service is one of the most terrible attacks is the cloning attack of the node, where the attacker captures the knot and extracts its secret information, create replicas and enter them in the network field other malevolent behaviour. To detect and mitigate this attack, this paper proposed a Gateway based technique

CRITICAL INFRASTRUCTURE TESTBED FOR CYBER-SECURITY TRAINING AND RESEARCH (4)

Critical infrastructures encompass various sectors such as energy resources, manufacturing and governmental services, which tend to be dispersed over large geographic areas. With recent technological advancements over the last decade, they have developed to be increasingly dependent on Information and Communication Technology (ICT); where control systems and the use of sensor equipment help facilitate operation. In order to sustain the ever-increasing demands, it is essential that these systems can adapt by integrating various new and existing digital technologies. However, this results in an increased vulnerability to cyber-threats. In addition, the persistently evolving global state of ICT has resulted in the emergence of sophisticated cyber-threats. As dependence upon critical infrastructure systems continues to increase, so too does the urgency with which these systems need to be adequately protected. Unfortunately, the consequences of a successful cyber-attack can be dire, potentially resulting in the loss of life or a devastating effect on the operation of government services and the economy. Despite the seriousness of this problem, the development of new and innovative cyber-security methods are being hampered by the lack of access to real-world data for training, research and testing new design methodologies. As such, the project presented in this paper highlights an in-progress project, funded by UKAIS, for the development of an easily-replicable and affordable critical infrastructure testbed for cyber-security training and research

Predicting the Effects of DDoS Attacks on a Network of Critical Infrastructures

International audienceOver the last decade, the level of critical infrastructure technology has been steadily transforming in order to keep pace with the growing demand for the services offered. The implementation of the smart grid, which relies on a complex and intelligent level of interconnectivity, is one example of how vital amenity provision is being refined. However, with this change, the risk of threats from the digital domain must be calculated. Superior interconnectivity between infrastructures means that the future cascading impacts of successful cyber-attacks are unknown. One such threat being faced in the digital domain is the Distributed Denial of Service (DDoS) attack. A DDoS has the goal of incapacitating a server, network or service, by barraging a target with external data traffic in the form of communication requests. DDoS have the potential to cause a critical infrastructure outage, and the subsequent impact on a network of such infrastructures is yet unknown. In this paper, an approach for assessing the future impacts of a cyber-attack in a network of critical infrastructures is presented; with a focus on DDoS attacks. A simulation of a critical infrastructure network provides data to represent both normal run-time and an attack scenario. Using this dataset, a technique for assessing the future impact of disruptions on integrated critical infrastructure network, is demonstrated