In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

Network Intrusion Detection for IoT Security Based on Learning Techniques

Distributed denial of service (DDoS) resilience in cloud

DDoS attacks in cloud computing

The minimal processing and best-e↵ort forwarding of any packet, malicious or not, was the prime concern when the Internet was designed. This architecture creates an unregulated network path, which can be exploited by any cyber attacker motivated by revenge, prestige, politics or money. Denial-of-service (DoS) attacks exploit this to target critical Web services [1, 2, 3, 4, 5]. This type of attack is intended to make a computer resource unavailable to its legitimate users. Denial of service attack programs have been around for many years. Old single source attacks are now countered easily by many defense mechanisms and the source of these attacks can be easily rebu↵ed or shut down with improved tracking capabilities. However, with the astounding growth of the Internet during the last decade, an increasingly large number of vulnerable systems are now available to attackers. Attackers can now employ a large number of these vulnerable hosts to launch an attack instead of using a single server, an approach which is not very e↵ective and detected easily. A distributed denial of service (DDoS) attack [1, 6] is a large-scale, coordinated attack on the availability of services of a victim system or network resources, launched indirectly through many compromised computers on the Internet. The first well-documented DDoS attack appears to have occurred in August 1999, when a DDoS tool called Trinoo was deployed in at least 227 systems, to flood a single University of Minnesota computer, which was knocked down for more than two days1. The first largescale DDoS attack took place on February 20001. On February 7, Yahoo! was the victim of a DDoS attack during which its Internet portal was inaccessible for three hours. On February 8, Amazon, Buy.com, CNN and eBay were all hit by DDoS attacks that caused them to either stop functioning completely or slowed them down significantly1. DDoS attack networks follow two types of architectures: the Agent-Handler architecture and the Internet Relay Chat (IRC)-based architecture as discussed by [7]. The Agent-Handler architecture for DDoS attacks is comprised of clients, handlers, and agents (see Figure 6). The attacker communicates with the rest of the DDoS attack system at the client systems. The handlers are often software packages located throughout the Internet that are used by the client to communicate with the agents. Instances of the agent software are placed in the compromised systems that finally carry out the attack. The owners and users of the agent systems are generally unaware of the situation. In the IRC-based DDoS attack architecture, an IRC communication channel is used to connect the client(s) to the agents. IRC

/pdf/detecting-distributed-denial-of-service-attacks-methods-fw9emofkn3.pdf

Detecting Distributed Denial of Service Attacks: Methods, Tools and Future Directions

Scalable blockchain model using off-chain IPFS storage for healthcare data security and privacy

Distributed Denial of Service (DDoS) attack launched in Cloud computing environment resulted in loss of sensitive information, Data corruption and even rarely lead to service shutdown. Entropy based DDoS mitigation approach analyzes the heuristic data and acts dynamically according to the traffic behavior to effectively segregate the characteristics of incoming traffic. Heuristic data helps in detecting the traffic condition to mitigate the flooding attack. Then, the traffic data is analyzed to distinguish legitimate and attack characteristics. An additional Trust mechanism has been deployed to differentiate legitimate and aggressive legitimate users. Hence, Goodput of Datacenter has been improved by detecting and mitigating the incoming traffic threats at each stage. Simulation results proved that the Enhanced Entropy approach behaves better at DDoS attack prone zones. Profit analysis also proved that the proposed mechanism is deployable at Datacenter for attack mitigation and resource protection which eventually results in beneficial service at slenderized revenue

https://www.ijcnis.org/index.php/ijcnis/article/download/367/118

An Enhanced Entropy Approach to Detect and Prevent DDoS in Cloud Environment

Security in this world of digital computing plays a typical role, since all the operations are automated and large volumes of data are being maintained in the servers. Cloud computing is one of the evolving technologies where a huge volume of storage is made on-line, data and services are also distributed. Because of its distributed nature, they have become easy targets for the intruders to exploit the information. The well known Distributed Denial of Service (DDoS) attack is the most prominent attacks in this area of computing. DDoS is the single largest threat to internet and internet of things. This paper provides a wide survey on various DDoS attacks, their vulnerabilities and countermeasures proposed against them. Also this paper provides an in-depth analysis on effects of DDoS attacks in the Cloud environment. Through the analysis done it will be useful for designing a secured cloud infrastructure which will abide the DDoS attacks.

Distributed Denial of service attacks and its effects on Cloud environment- a survey

Distributed Denial of Service (DDoS) is a major threat to server availability. The attackers hide from view by impersonating their IP addresses as the legitimate users. This Spoofed IP helps the attacker to pass through the authentication phase and to launch the attack. Surviving spoof detection techniques could not resolve different styles of attacks. Packet Resonance Strategy (PRS) armed to detect various types of spoof attacks that destruct the server resources or data theft at Datacenter. PRS ensembles to any Cloud Service Provider (CSP) as they are exclusively responsible for any data leakage and sensitive information hack. PRS uses two-level detection scheme, allows the clients to access Datacenter only when they surpass initial authentication at both levels. PRS provides faster data transmission and time sensitiveness of cloud computing tasks to the authenticated clients. Experimental results proved that the proposed methodology is a better light-weight solution and deployable at server-end.

https://www.ijcnis.org/index.php/ijcnis/article/download/238/102

Packet Resonance Strategy: A Spoof Attack Detection and Prevention Mechanism in Cloud Computing Environment

Cloud computing is a booming field which will be predominating the IT industries soon. Cloud computing is a virtual dynamic version of the present day internet resource access which is static. Cloud computing provides high availability of various large-scale geographically distributed resources for users ranging from small to large-scale on demand. The two key advantages of this model are ease of use and cost effectiveness, i.e., cost as per the usage and maximum resource utilisation. The main issue identified in cloud computing is security. In the specific case of cloud computing systems, the impact of a flooding attack is expected to be amplified drastically as and when the cloud computing operating system notices the high workload on the flooded service, it will start to provide more computational power to cope with the additional workload. The attacker can flood a single, cloud-based address in order to perform a full loss of availability on the intended service. In this paper, the proposed spoofing detection algorithm to detect DDos attacks launched against a server inside a cloud.

N. Jeyanthi

Papers

Scalable blockchain model using off-chain IPFS storage for healthcare data security and privacy

An Enhanced Entropy Approach to Detect and Prevent DDoS in Cloud Environment

Distributed Denial of service attacks and its effects on Cloud environment- a survey

Packet Resonance Strategy: A Spoof Attack Detection and Prevention Mechanism in Cloud Computing Environment

Detection of distributed denial of service attacks in cloud computing by identifying spoofed IP