Reducing Jamming Attack effects

Jamming as a form of denial-of-service is a commonly-used attack initiated against security at the physical layer of a wireless system. A new paradigm, known as the Internet of Things (IoT), has an extensive applicability in numerous are-as, including healthcare. The full application of this paradigm in healthcare area is a mutual hope because it allows medical centers to function more competently and patients to obtain better treatment

Internet of Things and WBAN: Attacks Presentations

What we are approaching is a world where basic healthcare would become out of reach to most people, a large section of society would go unproductive owing to old age and people would be more prone to chronic disease. A new paradigm, known as the Internet of Things (IoT), has an extensive applicability in numerous areas, including healthcare. The full application of this paradigm in healthcare area is a mutual hope because it allows medical centers to function more competently and patients to obtain better treatment

Distributed Denial of Service Attack in Networks

In communications the area of coverage is very important, such that personal space or long range to send information. The distance refers to class of networks such as per-sonal range or wide area, while the protocols of communications refer to mode or type of networks, such as ad-hoc or self organization etc. Our aim is to provide a tutorial to introduce DDoS attack and its working knowledge as well as rectifications. We will address its issues and suggest how it can overcome

Classification Denial Of Service (Dos) Attack Using Artificial Neural Network Learning Vector Quantization (Lvq)

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%

Classification Denial of Service (DOS) Attack using Artificial Neural Network Learning Vector Quantization (LVQ)

Network security is an important aspect in computer network defense. There are many threats find vulnerabilities and exploits for launching attacks. Threats that purpose to prevent users get the service of the system is Denial of Service (DoS). One of software application that can detect intrusion on is an Intrusion Detection System (IDS). IDS is a defense system to detect suspicious activity on the network. IDS has ability to categorize the various types of attack and not attack. In this research, Learning Vector Quantization (LVQ) neural network is used to classify the type of attacks. LVQ is a method to study the competitive supervised layer. If two input vectors approximately equal, then the competitive layers will put both the input vector into the same class. The results show IDS able to classify PING and UDP Floods are 100%

Comparative Analysis Based on Survey of DDOS Attacks’ Detection Techniques at Transport, Network, and Application Layers

Distributed Denial of Service (DDOS) is one of the most prevalent attacks and can be executed in diverse ways using various tools and codes. This makes it very difficult for the security researchers and engineers to come up with a rigorous and efficient security methodology. Even with thorough research, analysis, real time implementation, and application of the best mechanisms in test environments, there are various ways to exploit the smallest vulnerability within the system that gets overlooked while designing the defense mechanism. This paper presents a comprehensive survey of various methodologies implemented by researchers and engineers to detect DDOS attacks at network, transport, and application layers using comparative analysis. DDOS attacks are most prevalent on network, transport, and application layers justifying the need to focus on these three layers in the OSI model

Cyber Security Evaluation of CentOS Red Hat Based Operating System Under Cyber Attack with Increasing Magnitude

The increasing interest in ‘always-connected’ devices and the Internet of Things has led to electronic devices with Internet connectivity becoming a staple in modern household and workplace. Consequently, this increase has also led to an increase in vulnerable devices, ripe for hijacking by a malicious third party. Distributed Denial of Service (DDoS) attacks have consistently been an issue since the birth of the Internet. With the large number of devices available today, the strength and consistency of these attacks has only grown and will continue to grow. Since, depending on certain variables, these DDoS attacks can effectively render a target system inoperable, precautions must be taken in order to prevent these attacks. Not all devices are created equal; Many harbor flaws that allow them to be used by a separate, malicious host without the knowledge of the owner. There is a myriad of devices on the market today, any of which can be used in a network of zombie machines meant to carry out an attack, a botnet. These botnets are used to flood a system with information, ideally consuming large amounts of resources, such as memory or processing power. If the attack is successful, operation within the target system is effectively halted, often for long periods of time in the more severe attacks. Just like the variety in devices, there is a variety in the software that operates these devices. In this experiment, I focus efforts on comparing the ability of CentOS 15 with Windows Server 2012R to function under attack. I analyze four popular DDoS attacks using simulated network traffic consisting of botnets ranging from of over 16 million systems, 65 thousand systems and 254 systems in a controlled, closed environment