Biblio
With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.
Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants' personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants' privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.
Mixed-Criticality Systems (MCS) are real-time systems characterized by two or more distinct levels of criticality. In MCS, it is imperative that high-critical flows meet their deadlines while low critical flows can tolerate some delays. Sharing resources between flows in Network-On-Chip (NoC) can lead to different unpredictable latencies and subsequently complicate the implementation of MCS in many-core architectures. This paper proposes a new virtual channel router designed for MCS deployed over NoCs. The first objective of this router is to reduce the worst-case communication latency of high-critical flows. The second aim is to improve the network use rate and reduce the communication latency for low-critical flows. The proposed router, called DAS (Double Arbiter and Switching router), jointly uses Wormhole and Store And Forward techniques for low and high-critical flows respectively. Simulations with a cycle-accurate SystemC NoC simulator show that, with a 15% network use rate, the communication delay of high-critical flows is reduced by 80% while communication delay of low-critical flow is increased by 18% compared to usual solutions based on routers with multiple virtual channels.
Attacks on airport information network services in the form of Denial of Service (DoS), Distributed DoS (DDoS), and hijacking are the most effective schemes mostly explored by cyber terrorists in the aviation industry running Mission Critical Services (MCSs). This work presents a case for Airport Information Resource Management Systems (AIRMS) which is a cloud based platform proposed for the Nigerian aviation industry. Granting that AIRMS is susceptible to DoS attacks, there is need to develop a robust counter security network model aimed at pre-empting such attacks and subsequently mitigating the vulnerability in such networks. Existing works in literature regarding cyber security DoS and other schemes have not explored embedded Stateful Packet Inspection (SPI) based on OpenFlow Application Centric Infrastructure (OACI) for securing critical network assets. As such, SPI-OACI was proposed to address the challenge of Vulnerability Bandwidth Depletion DDoS Attacks (VBDDA). A characterization of the Cisco 9000 router firewall as an embedded network device with support for Virtual DDoS protection was carried out in the AIRMS threat mitigation design. Afterwards, the mitigation procedure and the initial phase of the design with Riverbed modeler software were realized. For the security Quality of Service (QoS) profiling, the system response metrics (i.e. SPI-OACI delay, throughput and utilization) in cloud based network were analyzed only for normal traffic flows. The work concludes by offering practical suggestion for securing similar enterprise management systems running on cloud infrastructure against cyber terrorists.