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Veugen, T., de Haan, R., Cramer, R., Muller, F..  2015.  A Framework for Secure Computations With Two Non-Colluding Servers and Multiple Clients, Applied to Recommendations. Information Forensics and Security, IEEE Transactions on. 10:445-457.

We provide a generic framework that, with the help of a preprocessing phase that is independent of the inputs of the users, allows an arbitrary number of users to securely outsource a computation to two non-colluding external servers. Our approach is shown to be provably secure in an adversarial model where one of the servers may arbitrarily deviate from the protocol specification, as well as employ an arbitrary number of dummy users. We use these techniques to implement a secure recommender system based on collaborative filtering that becomes more secure, and significantly more efficient than previously known implementations of such systems, when the preprocessing efforts are excluded. We suggest different alternatives for preprocessing, and discuss their merits and demerits.

Vhaduri, S., Poellabauer, C..  2017.  Wearable Device User Authentication Using Physiological and Behavioral Metrics. 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). :1–6.

Wearables, such as Fitbit, Apple Watch, and Microsoft Band, with their rich collection of sensors, facilitate the tracking of healthcare- and wellness-related metrics. However, the assessment of the physiological metrics collected by these devices could also be useful in identifying the user of the wearable, e.g., to detect unauthorized use or to correctly associate the data to a user if wearables are shared among multiple users. Further, researchers and healthcare providers often rely on these smart wearables to monitor research subjects and patients in their natural environments over extended periods of time. Here, it is important to associate the sensed data with the corresponding user and to detect if a device is being used by an unauthorized individual, to ensure study compliance. Existing one-time authentication approaches using credentials (e.g., passwords, certificates) or trait-based biometrics (e.g., face, fingerprints, iris, voice) might fail, since such credentials can easily be shared among users. In this paper, we present a continuous and reliable wearable-user authentication mechanism using coarse-grain minute-level physical activity (step counts) and physiological data (heart rate, calorie burn, and metabolic equivalent of task). From our analysis of 421 Fitbit users from a two-year long health study, we are able to statistically distinguish nearly 100% of the subject-pairs and to identify subjects with an average accuracy of 92.97%.

Vi, Bao Ngoc, Noi Nguyen, Huu, Nguyen, Ngoc Tran, Truong Tran, Cao.  2019.  Adversarial Examples Against Image-based Malware Classification Systems. 2019 11th International Conference on Knowledge and Systems Engineering (KSE). :1—5.

Malicious software, known as malware, has become urgently serious threat for computer security, so automatic mal-ware classification techniques have received increasing attention. In recent years, deep learning (DL) techniques for computer vision have been successfully applied for malware classification by visualizing malware files and then using DL to classify visualized images. Although DL-based classification systems have been proven to be much more accurate than conventional ones, these systems have been shown to be vulnerable to adversarial attacks. However, there has been little research to consider the danger of adversarial attacks to visualized image-based malware classification systems. This paper proposes an adversarial attack method based on the gradient to attack image-based malware classification systems by introducing perturbations on resource section of PE files. The experimental results on the Malimg dataset show that by a small interference, the proposed method can achieve success attack rate when challenging convolutional neural network malware classifiers.

Viand, Alexander, Shafagh, Hossein.  2018.  Marble: Making Fully Homomorphic Encryption Accessible to All. Proceedings of the 6th Workshop on Encrypted Computing & Applied Homomorphic Cryptography. :49-60.

With the recent explosion of data breaches and data misuse cases, there is more demand than ever for secure system designs that fundamentally tackle today's data trust models. One promising alternative to today's trust model is true end-to-end encryption without however compromising user experience nor data utility. Fully homomorphic encryption (FHE) provides a powerful tool in empowering users with more control over their data, while still benefiting from computing services of remote services, though without trusting them with plaintext data. However, due to the complexity of fully homomorphic encryption, it has remained reserved exclusively for a small group of domain experts. With our system Marble, we make FHE accessible to the broader community of researchers and developers. Marble takes away the complexity of setup and configuration associated with FHE schemes. It provides a familiar programming environment. Marble allows rapid feasibility assessment and development of FHE-based applications. More importantly, Marble benchmarks the overall performance of an FHE-based application, as part of the feasibility assessment. With real-world application case-studies, we show the practicality of Marble.

Victor Heorhiadi, Shriram Rajagopalan, Hani Jamjoom, Michael K. Reiter, Vyas Sekar.  2016.  Gremlin: Systematic resilience testing of microservices. 36th IEEE International Conference on Distributed Computing Systems.

Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices.  Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.

Victor Heorhiadi, Michael K. Reiter, Vyas Sekar.  2015.  Accelerating the Development of Software-Defined Network Optimization Applications Using SOL.

Software-defined networking (SDN) can enable diverse network management applications such as traffic engineering, service chaining, network function outsourcing, and topology reconfiguration. Realizing the benefits of SDN for these applications, however, entails addressing complex network optimizations that are central to these problems. Unfortunately, such optimization problems require significant manual effort and expertise to express and non-trivial computation and/or carefully crafted heuristics to solve. Our vision is to simplify the deployment of SDN applications using general high-level abstractions for capturing optimization requirements from which we can efficiently generate optimal solutions. To this end, we present SOL, a framework that demonstrates that it is indeed possible to simultaneously achieve generality and efficiency. The insight underlying SOL is that SDN applications can be recast within a unifying path-based optimization abstraction, from which it efficiently generates near-optimal solutions, and device configurations to implement those solutions. We illustrate the generality of SOL by prototyping diverse and new applications. We show that SOL simplifies the development of SDN-based network optimization applications and provides comparable or better scalability than custom optimization solutions.

Vidhya, R, Karthik, P.  2016.  Coexistence of cellular IOT and 4G networks. :555–558.

Increase in M2M use cases, the availability of narrow band spectrum with operators and a need for very low cost modems for M2M applications has led to the discussions around what is called as Cellular IOT (CIOT). In order to develop the Cellular IOT network, discussions are focused around developing a new air interface that can leverage narrow band spectrum as well as lead to low cost modems which can be embedded into M2M/IOT devices. One key issue that arises during the development of a clean slate CIOT network is that of coexistence with the 4G networks. In this paper we explore architectures for Cellular IOT and 4G network harmonization that also addresses the one key requirement of possibly using narrow channels for IOT on the existing 4G networks and not just as a separate standalone Cellular IOT system. We analyze the architectural implication on the core network load in a tightly coupled CIOT-LTE architecture propose a offload mechanism from LTE to CIOT cells.

Vidhya, R., Karthik, P..  2016.  Coexistence of cellular IOT and 4G networks. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). :555–558.

Increase in M2M use cases, the availability of narrow band spectrum with operators and a need for very low cost modems for M2M applications has led to the discussions around what is called as Cellular IOT (CIOT). In order to develop the Cellular IOT network, discussions are focused around developing a new air interface that can leverage narrow band spectrum as well as lead to low cost modems which can be embedded into M2M/IOT devices. One key issue that arises during the development of a clean slate CIOT network is that of coexistence with the 4G networks. In this paper we explore architectures for Cellular IOT and 4G network harmonization that also addresses the one key requirement of possibly using narrow channels for IOT on the existing 4G networks and not just as a separate standalone Cellular IOT system. We analyze the architectural implication on the core network load in a tightly coupled CIOT-LTE architecture propose a offload mechanism from LTE to CIOT cells.

Viegas, P., Borges, D., Montezuma, P., Dinis, R., Silva, M. M..  2019.  Multi-beam Physical Security Scheme: Security Assessment and Impact of Array Impairments on Security and Quality of Service. 2019 PhotonIcs Electromagnetics Research Symposium - Spring (PIERS-Spring). :2368—2375.

Massive multiple-input multiple-output (mMIMO) with perfect channel state information (CSI) can lead array power gain increments proportional to the number of antennas. Despite this fact constrains on power amplification still exist due to envelope variations of high order constellation signals. These constrains can be overpassed by a transmitter with several amplification branches, with each one associated to a component signal that results from the decomposition of a multilevel constellation as a sum of several quasi constant envelope signals that are sent independently. When combined with antenna arrays at the end of each amplification branch the security improves due to the energy separation achieved by beamforming. However, to avoid distortion on the signal resulting from the combination of all components at channel level all the beams of signal components should be directed in same direction. In such conditions it is crucial to assess the impact of misalignments between beams associated to each user, which is the purpose of this work. The set of results presented here show the good tolerance against misalignments of these transmission structures.

Vieira, Leandro, Santos, Leonel, Gon\c calves, Ramiro, Rabadão, Carlos.  2019.  Identifying Attack Signatures for the Internet of Things: An IP Flow Based Approach. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). :1–7.

At the time of more and more devices being connected to the internet, personal and sensitive information is going around the network more than ever. Thus, security and privacy regarding IoT communications, devices, and data are a concern due to the diversity of the devices and protocols used. Since traditional security mechanisms cannot always be adequate due to the heterogeneity and resource limitations of IoT devices, we conclude that there are still several improvements to be made to the 2nd line of defense mechanisms like Intrusion Detection Systems. Using a collection of IP flows, we can monitor the network and identify properties of the data that goes in and out. Since network flows collection have a smaller footprint than packet capturing, it makes it a better choice towards the Internet of Things networks. This paper aims to study IP flow properties of certain network attacks, with the goal of identifying an attack signature only by observing those properties.

Vieira, Luiz F.M., Vieira, Marcos A.M..  2017.  Network Coding for 5G Network and D2D Communication. Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks. :113–120.

Unlike traditional routing where packets are only stored and forward, network coding allows packets to mix together. New packets can be formed by combining other packets. This technique can provide benefits to the network. Network coding has been shown to improve network throughput, reduce energy consumption, improve network robustness and achieve the network capacity. 5G Network is foreseen as a novel network paradigm enabling massive device connectivity and enabling device-to-device communication (D2D). It has many potential applications ranging from ultra high definition video to virtual reality applications. In this paper, we present the advantages, benefits, scenarios, and applications of Network coding for 5G Network and device-to-device communication. We present the state-of-art research, the theoretical benefits, and detail how network coding can improve 5G Networks and D2D communication. Our results show that network coding can almost double the network throughput while increasing network robustness and decreasing the overall time to disseminate messages.

Viet, A. N., Van, L. P., Minh, H. A. N., Xuan, H. D., Ngoc, N. P., Huu, T. N..  2017.  Mitigating HTTP GET flooding attacks in SDN using NetFPGA-based OpenFlow switch. 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :660–663.

In this paper, we propose a hardware-based defense system in Software-Defined Networking architecture to protect against the HTTP GET Flooding attacks, one of the most dangerous Distributed Denial of Service (DDoS) attacks in recent years. Our defense system utilizes per-URL counting mechanism and has been implemented on FPGA as an extension of a NetFPGA-based OpenFlow switch.

Viet, H. N., Kwon, K. R., Kwon, S. K., Lee, E. J., Lee, S. H., Kim, C. Y..  2017.  Implementation of GPS signal simulation for drone security using Matlab/Simulink. 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON). :1–4.
In this paper, a simulation model of digital intermediate frequency (IF) GPS signal is presented. This design is developed based on mathematical model representing the digitized IF GPS signal. In details, C/A code, navigation data and P code, and the noise models are configured some initial settings simultaneously. Simulation results show that the simulated signals share the same properties with real signals (e.g. C/A code correlation properties, and the spread spectrum). The simulated GPS IF signal data can work as input for various signal processing algorithm of GPS receivers, such as acquisition, tracking, carrier-to-noise ratio (C/No) estimation, and GPS spoofing signal generation. Particularly, the simulated GPS signal can conduct scenarios by adjust SNR values of the noise generator during simulation (e.g. signal outages, sudden changes of GPS signal power), which can be used as setup experiments of spoofing/jamming interference to UAVs for drone security applications.
Viet, Hung Nguyen, Van, Quan Nguyen, Trang, Linh Le Thi, Nathan, Shone.  2018.  Using Deep Learning Model for Network Scanning Detection. Proceedings of the 4th International Conference on Frontiers of Educational Technologies. :117-121.

In recent years, new and devastating cyber attacks amplify the need for robust cybersecurity practices. Preventing novel cyber attacks requires the invention of Intrusion Detection Systems (IDSs), which can identify previously unseen attacks. Many researchers have attempted to produce anomaly - based IDSs, however they are not yet able to detect malicious network traffic consistently enough to warrant implementation in real networks. Obviously, it remains a challenge for the security community to produce IDSs that are suitable for implementation in the real world. In this paper, we propose a new approach using a Deep Belief Network with a combination of supervised and unsupervised machine learning methods for port scanning attacks detection - the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. Our proposed approach will be tested with network security datasets and compared with previously existing methods.

Viglianisi, Gabriele, Carminati, Michele, Polino, Mario, Continella, Andrea, Zanero, Stefano.  2018.  SysTaint: Assisting Reversing of Malicious Network Communications. Proceedings of the 8th Software Security, Protection, and Reverse Engineering Workshop. :4:1–4:12.

The ever-increasing number of malware samples demands for automated tools that aid the analysts in the reverse engineering of complex malicious binaries. Frequently, malware communicates over an encrypted channel with external network resources under the control of malicious actors, such as Command and Control servers that control the botnet of infected machines. Hence, a key aspect in malware analysis is uncovering and understanding the semantics of network communications. In this paper we present SysTaint, a semi-automated tool that runs malware samples in a controlled environment and analyzes their execution to support the analyst in identifying the functions involved in the communication and the exchanged data. Our evaluation on four banking Trojan samples from different families shows that SysTaint is able to handle and inspect encrypted network communications, obtaining useful information on the data being sent and received, on how each sample processes this data, and on the inner portions of code that deal with the data processing.

Vigneswaran, Rahul K., Vinayakumar, R., Soman, K.P., Poornachandran, Prabaharan.  2018.  Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). :1—6.
Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-`99' dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.
Vijay Kothari, Dartmouth College, Jim Blythe, University of Southern California, Ross Koppel, University of Pennsylvania, Sean Smith, Dartmouth College.  2015.  Measuring the Security Impacts of Password Policies Using Cognitive Behavioral Agent Based Modeling. Symposium and Bootcamp on the Science of Security (HotSoS).

Agent-based modeling can serve as a valuable asset to security personnel who wish to better understand the security landscape within their organization, especially as it relates to user behavior and circumvention. In this paper, we ar- gue in favor of cognitive behavioral agent-based modeling for usable security, report on our work on developing an agent- based model for a password management scenario, perform a sensitivity analysis, which provides us with valuable insights into improving security (e.g., an organization that wishes to suppress one form of circumvention may want to endorse another), and provide directions for future work.

Vijay Varadharajan, Udaya Tupakula.  2014.  Counteracting security attacks in virtual machines in the cloud using property based attestation. Journal of Network and Computer Applications. 40:31-45.

Cloud computing technologies are receiving a great deal of attention. Furthermore most of the hardware devices such as the PCs and mobile phones are increasingly having a trusted component called Trusted Platform Module embedded in them, which helps to measure the state of the platform and hence reason about its trust. Recently attestation techniques such as binary attestation and property based attestation techniques have been proposed based on the TPM. In this paper, we propose a novel trust enhanced security model for cloud services that helps to detect and prevent security attacks in cloud infrastructures using trusted attestation techniques. We consider a cloud architecture where different services are hosted on virtualized systems on the cloud by multiple cloud customers (multi-tenants). We consider attacker model and various attack scenarios for such hosted services in the cloud. Our trust enhanced security model enables the cloud service provider to certify certain security properties of the tenant virtual machines and services running on them. These properties are then used to detect and minimise attacks between the cloud tenants running virtual machines on the infrastructure and its customers as well as increase the assurance of the tenant virtual machine transactions. If there is a variation in the behaviour of the tenant virtual machine from the certified properties, the model allows us to dynamically isolate the tenant virtual machine or even terminate the malicious services on a fine granular basis. The paper describes the design and implementation of the proposed model and discusses how it deals with the different attack scenarios. We also show that our model is beneficial for the cloud service providers, cloud customers running tenant virtual machines as well as the customers using the services provided by these tenant virtual machines.

Vijay, Savinu T., Pournami, P. N..  2018.  Feature Based Image Registration using Heuristic Nearest Neighbour Search. 2018 22nd International Computer Science and Engineering Conference (ICSEC). :1—3.
Image registration is the process of aligning images of the same scene taken at different instances, from different viewpoints or by heterogeneous sensors. This can be achieved either by area based or by feature based image matching techniques. Feature based image registration focuses on detecting relevant features from the input images and attaching descriptors to these features. Matching visual descriptions of two images is a major task in image registration. This feature matching is currently done using Exhaustive Search (or Brute-Force) and Nearest Neighbour Search. The traditional method used for nearest neighbour search is by representing the data as k-d trees. This nearest neighbour search can also be performed using combinatorial optimization algorithms such as Simulated Annealing. This work proposes a method to perform image feature matching by nearest neighbour search done based on Threshold Accepting, a faster version of Simulated Annealing.The experiments performed suggest that the proposed algorithm can produce better results within a minimum number of iterations than many existing algorithms.
Vijayakumar, P., Bose, S., Kannan, A..  2014.  Chinese remainder theorem based centralised group key management for secure multicast communication. Information Security, IET. 8:179-187.

Designing a centralised group key management with minimal computation complexity to support dynamic secure multicast communication is a challenging issue in secure multimedia multicast. In this study, the authors propose a Chinese remainder theorem-based group key management scheme that drastically reduces computation complexity of the key server. The computation complexity of key server is reduced to O(1) in this proposed algorithm. Moreover, the computation complexity of group member is also minimised by performing one modulo division operation when a user join or leave operation is performed in a multicast group. The proposed algorithm has been implemented and tested using a key-star-based key management scheme and has been observed that this proposed algorithm reduces the computation complexity significantly.

Vijayakumar, R., Selvakumar, K., Kulothungan, K., Kannan, A..  2014.  Prevention of multiple spoofing attacks with dynamic MAC address allocation for wireless networks. Communications and Signal Processing (ICCSP), 2014 International Conference on. :1635-1639.

In wireless networks, spoofing attack is one of the most common and challenging attacks. Due to these attacks the overall network performance would be degraded. In this paper, a medoid based clustering approach has been proposed to detect a multiple spoofing attacks in wireless networks. In addition, a Enhanced Partitioning Around Medoid (EPAM) with average silhouette has been integrated with the clustering mechanism to detect a multiple spoofing attacks with a higher accuracy rate. Based on the proposed method, the received signal strength based clustering approach has been adopted for medoid clustering for detection of attacks. In order to prevent the multiple spoofing attacks, dynamic MAC address allocation scheme using MD5 hashing technique is implemented. The experimental results shows, the proposed method can detect spoofing attacks with high accuracy rate and prevent the attacks. Thus the overall network performance is improved with high accuracy rate.
 

Vijayan, A., Thomas, T..  2014.  Anonymity, unlinkability and unobservability in mobile ad hoc networks. Communications and Signal Processing (ICCSP), 2014 International Conference on. :1880-1884.

Mobile ad hoc networks have the features of open medium, dynamic topology, cooperative algorithms, lack of centralized monitoring etc. Due to these, mobile ad hoc networks are much vulnerable to security attacks when compared to wired networks. There are various routing protocols that have been developed to cope up with the limitations imposed by the ad hoc networks. But none of these routing schemes provide complete unlinkability and unobservability. In this paper we have done a survey about anonymous routing and secure communications in mobile ad hoc networks. Different routing protocols are analyzed based on public/private key pairs and cryptosystems, within that USOR can well protect user privacy against both inside and outside attackers. It is a combination of group signature scheme and ID based encryption scheme. These are run during the route discovery process. We implement USOR on ns2, and then its performance is compared with AODV.

Vikram, A., Kalaivani, S., Gopinath, G..  2019.  A Novel Encryption Algorithm based on DNA Cryptography. 2019 International Conference on Communication and Electronics Systems (ICCES). :1004–1009.
The process of information security entails securing the information by transferring it through the networks preventing the data from attacks. This way of securing the information is known as cryptography. The perspective of converting the plain-text into non-understandable format is known as cryptography that could be possible using certain cryptography algorithms. The security could not be offered by the conventional cryptographic algorithms that lacks in their security for the huge amount of growing data, which could be easily broken by the intruders for their malicious activities. This gives rise to the new cryptographic algorithm known as DNA computing that could strengthen the information security, which does not provide any intruders to get authorized to confidential data. The proposed DNA symmetric cryptography enhances information security. The results reveal that encryption process carried out on plain-text is highly secured.
Villalobos, J. J., Rodero, Ivan, Parashar, Manish.  2017.  An Unsupervised Approach for Online Detection and Mitigation of High-Rate DDoS Attacks Based on an In-Memory Distributed Graph Using Streaming Data and Analytics. Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. :103–112.

A Distributed Denial of Service (DDoS) attack is an attempt to make an online service, a network, or even an entire organization, unavailable by saturating it with traffic from multiple sources. DDoS attacks are among the most common and most devastating threats that network defenders have to watch out for. DDoS attacks are becoming bigger, more frequent, and more sophisticated. Volumetric attacks are the most common types of DDoS attacks. A DDoS attack is considered volumetric, or high-rate, when within a short period of time it generates a large amount of packets or a high volume of traffic. High-rate attacks are well-known and have received much attention in the past decade; however, despite several detection and mitigation strategies have been designed and implemented, high-rate attacks are still halting the normal operation of information technology infrastructures across the Internet when the protection mechanisms are not able to cope with the aggregated capacity that the perpetrators have put together. With this in mind, the present paper aims to propose and test a distributed and collaborative architecture for online high-rate DDoS attack detection and mitigation based on an in-memory distributed graph data structure and unsupervised machine learning algorithms that leverage real-time streaming data and analytics. We have successfully tested our proposed mechanism using a real-world DDoS attack dataset at its original rate in pursuance of reproducing the conditions of an actual large scale attack.

Villarreal-Vasquez, M., Bhargava, B., Angin, P..  2017.  Adaptable Safety and Security in V2X Systems. 2017 IEEE International Congress on Internet of Things (ICIOT). :17–24.

With the advances in the areas of mobile computing and wireless communications, V2X systems have become a promising technology enabling deployment of applications providing road safety, traffic efficiency and infotainment. Due to their increasing popularity, V2X networks have become a major target for attackers, making them vulnerable to security threats and network conditions, and thus affecting the safety of passengers, vehicles and roads. Existing research in V2X does not effectively address the safety, security and performance limitation threats to connected vehicles, as a result of considering these aspects separately instead of jointly. In this work, we focus on the analysis of the tradeoffs between safety, security and performance of V2X systems and propose a dynamic adaptability approach considering all three aspects jointly based on application needs and context to achieve maximum safety on the roads using an Internet of vehicles. Experiments with a simple V2V highway scenario demonstrate that an adaptive safety/security approach is essential and V2X systems have great potential for providing low reaction times.