Biblio

Intelligent connected vehicle platoon technology can reduce traffic congestion and vehicle fuel. However, attacks on the data transmitted by the platoon are one of the primary challenges encountered by the platoon during its travels. The false data injection (FDI) attack can lead to road congestion and even vehicle collisions, which can impact the platoon. However, the complexity of the cellular - vehicle to everything (C-V2X) environment, the single source of the message and the poor data processing capability of the on board unit (OBU) make the traditional detection methods’ success rate and response time poor. This study proposes a platoon state information fusion method using the communication characteristics of the platoon in C-V2X and proposes a novel platoon intrusion detection model based on this fusion method combined with sequential importance sampling (SIS). The SIS is a measured strategy of Monte Carlo integration sampling. Specifically, the method takes the status information of the platoon members as the predicted value input. It uses the leader vehicle status information as the posterior probability of the observed value to the current moment of the platoon members. The posterior probabilities of the platoon members and the weights of the platoon members at the last moment are used as input to update the weights of the platoon members at the current moment and obtain the desired platoon status information at the present moment. Moreover, it compares the status information of the platoon members with the desired status information to detect attacks on the platoon. Finally, the effectiveness of the method is demonstrated by simulation.

DDoS attack detection in a single dimension cannot cope with complex and new attacks. Aiming at the problems existing in single dimension detection, this paper proposes an algorithm to detect DDoS attack based on multi-dimensional entropy. Firstly, the algorithm selects multiple dimensions and establishes corresponding decision function for each dimension and calculates its information entropy. Secondly, the multidimensional sliding window CUSUM algorithm without parameters is used to synthesize the detection results of three dimensions to determine whether it is attacked by DDoS. Finally, the data set published by MIT Lincoln Laboratory is used for testing. Experimental results show that compared with single dimension detection algorithm, this method has good detection rate and low false alarm rate.

Software metrics help developers discover and fix mistakes. However, despite promising empirical evidence, vulnerability discovery metrics are seldom relied upon in practice. In prior research, the effectiveness of these metrics has typically been expressed using precision and recall of a prediction model that uses the metrics as explanatory variables. These prediction models, being black boxes, may not be perceived as useful by developers. However, by systematically interpreting the models and metrics, we can provide developers with nuanced insights about factors that have led to security mistakes in the past. In this paper, we present a preliminary approach to using vulnerability discovery metrics to provide insightful feedback to developers as they engineer software. We collected ten metrics (churn, collaboration centrality, complexity, contribution centrality, nesting, known offender, source lines of code, \# inputs, \# outputs, and \# paths) from six open-source projects. We assessed the generalizability of the metrics across two contextual dimensions (application domain and programming language) and between projects within a domain, computed thresholds for the metrics using an unsupervised approach from literature, and assessed the ability of these unsupervised thresholds to classify risk from historical vulnerabilities in the Chromium project. The observations from this study feeds into our ongoing research to automatically aggregate insights from the various analyses to generate natural language feedback on security. We hope that our approach to generate automated feedback will accelerate the adoption of research in vulnerability discovery metrics.

Dependency on cloud computing are increasing day by day due to its beneficial aspects. As day by day we are relying on cloud computing, the securities issues are coming up. There are lots of security protocols but now-a-days those protocol are not secured enough to provide a high security. One of those protocols which were once highly secured, is Kerberos authentication protocol. With the advancement of technology, Kerberos authentication protocol is no longer as secured as it was before. Many authors have thought about the improvement of Kerberos authentication protocol and consequently they have proposed different types of protocol models by using a renowned public key cryptography named RSA cryptography. Though RSA cryptography is good to some extent but this cryptography has some flaws that make this cryptography less secured as well as less efficient. In this paper, we are combining Elliptic Curve Cryptography (ECC) as well as Threshold Cryptography to create a new version of Kerberos authentication protocol. Our proposed model will provide secure transaction of data which will not only be hard to break but also increase memory efficiency, cost efficiency, and reduce the burden of computation.

The average computer user is no longer restricted to one device. They may have several devices and expect their applications to work on all of them. A challenge arises when these applications need the cryptographic private key of the devices' owner. Here the device owner typically has to manage keys manually with a "keychain" app, which leads to private keys being transferred insecurely between devices – or even to other people. Even with intuitive synchronization mechanisms, theft and malware still pose a major risk to keys. Phones and watches are frequently removed or set down, and a single compromised device leads to the loss of the owner's private key, a catastrophic failure that can be quite difficult to recover from. We introduce Shatter, an open-source framework that runs on desktops, Android, and Android Wear, and performs key distribution on a user's behalf. Shatter uses threshold cryptography to turn the security weakness of having multiple devices into a strength. Apps that delegate cryptographic operations to Shatter have their keys compromised only when a threshold number of devices are compromised by the same attacker. We demonstrate how our framework operates with two popular Android apps (protecting identity keys for a messaging app, and encryption keys for a note-taking app) in a backwards-compatible manner: only Shatter users need to move to a Shatter-aware version of the app. Shatter has minimal impact on app performance, with signatures and decryption being calculated in 0.5s and security proofs in 14s.