Preventing DDoS using Bloom Filter: A Survey

Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.Comment: 9 pages, 1 figure. This article is accepted for publication in EAI Endorsed Transactions on Scalable Information System

A Review on Security Attacks in Vehicular Ad hoc Network

Whenever a communication takes place between two or more vehicles there has been a need for protection. The attacker can gain access to the network by compromising either the vehicle or road side unit or the communication medium that transfers the messages between vehicles. Vehicular Ad hoc Network (VANET) have motivated the interest towards the passenger comfort and secure driving environment. However, the open-wide communication becomes a tedious challenge for VANET organization. Because of the wireless self-structured background, VANET are prone to many attackers. In this paper, we are focusing on security issues like DoS, Sybil, DDoS, jamming and flooding attacks as well as techniques like TESLA which causes harm to VANET and also security countermeasures like digital signature which are used to prevent the mentioned security issues that alleviate VANET

Denial of Service Attack in VANET: A Survey

ABSTRACT--

A multivariant stream analysis approach to detect and mitigate DDoS attacks in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle-to-Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication. © 2018 Raenu Kolandaisamy et al. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record*

A taxonomy and survey of cyber-physical intrusion detection approaches for vehicles

With the growing threat of cyber and cyber-physical attacks against automobiles, drones, ships, driverless pods and other vehicles, there is also a growing need for intrusion detection approaches that can facilitate defence against such threats. Vehicles tend to have limited processing resources and are energy-constrained. So, any security provision needs to abide by these limitations. At the same time, attacks against vehicles are very rare, often making knowledge-based intrusion detection systems less practical than behaviour-based ones, which is the reverse of what is seen in conventional computing systems. Furthermore, vehicle design and implementation can differ wildly between different types or different manufacturers, which can lead to intrusion detection designs that are vehicle-specific. Equally importantly, vehicles are practically defined by their ability to move, autonomously or not. Movement, as well as other physical manifestations of their operation may allow cyber security breaches to lead to physical damage, but can also be an opportunity for detection. For example, physical sensing can contribute to more accurate or more rapid intrusion detection through observation and analysis of physical manifestations of a security breach. This paper presents a classification and survey of intrusion detection systems designed and evaluated specifically on vehicles and networks of vehicles. Its aim is to help identify existing techniques that can be adopted in the industry, along with their advantages and disadvantages, as well as to identify gaps in the literature, which are attractive and highly meaningful areas of future research

Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing

VANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy