Biblio

Vehicular Ad-Hoc Networks (VANET) are the creation of several vehicles communicating with each other in order to create a network capable of communication and data exchange. One of the most promising methods for security and trust amongst vehicular networks is the usage of Public Key Infrastructure (PKI). However, current implementations of PKI as a security solution for determining the validity and authenticity of vehicles in a VANET is not efficient due to the usage of large amounts of delay and computational overhead. In this paper, we investigate the potential of PKI when predictively and preemptively passing along certificates to roadside units (RSU) in an effort to lower delay and computational overhead in a dynamic environment. We look to accomplish this through utilizing fog computing and propose a new protocol to pass certificates along the projected path.

The increased number of cyber attacks makes the availability of services a major security concern. One common type of cyber threat is distributed denial of service (DDoS). A DDoS attack is aimed at disrupting the legitimate users from accessing the services. It is easier for an insider having legitimate access to the system to deceive any security controls resulting in insider attack. This paper proposes an Early Detection and Isolation Policy (EDIP)to mitigate insider-assisted DDoS attacks. EDIP detects insider among all legitimate clients present in the system at proxy level and isolate it from innocent clients by migrating it to attack proxy. Further an effective algorithm for detection and isolation of insider is developed with the aim of maximizing attack isolation while minimizing disruption to benign clients. In addition, concept of load balancing is used to prevent proxies from getting overloaded.

Since MANETs are infrastructure-less, they heavily use secret sharing techniques to distribute and decentralize the role of a trusted third party, where the MANET secret s is shared among the legitimate nodes using (t, n) threshold secret sharing scheme. For long lived MANETs, the shared secret is periodically updated without changing the MANET secret based on proactive secret sharing using Elliptic Curve Cryptography(ECC). Hence, the adversary trying to learn the secret, needs to gain at-least t partial shares in the same time period. If the time period and the threshold value t are selected properly, proactive verifiable secret sharing can maintain the overall security of the information in long lived MANETs. The conventional cryptographic algorithms are heavy weight, require lot of computation power thus consuming lot of resources. In our proposal we used Elliptic Curve Cryptography to verify commitments as it requires smaller keys compared to existing proactive secret sharing techniques and makes it useful for MANETs, Which are formed of resource constraint devices.

Cyber-physical systems (CPS) are interconnections of heterogeneous hardware and software components (e.g., sensors, actuators, physical systems/processes, computational nodes and controllers, and communication subsystems). Increasing network connectivity of CPS computational nodes facilitates maintenance and on-demand reprogrammability and reduces operator workload. However, such increasing connectivity also raises the potential for cyber-attacks that attempt unauthorized modifications of run-time parameters or control logic in the computational nodes to hamper process stability or performance. In this paper, we analyze the effectiveness of real-time monitoring using digital and analog side channels. While analog side channels might not typically provide sufficient granularity to observe each iteration of a periodic loop in the code in the CPS device, the temporal averaging inherent to side channel sensory modalities enables observation of persistent changes to the contents of a computational loop through their resulting effect on the level of activity of the device. Changes to code can be detected by observing readings from side channel sensors over a period of time. Experimental studies are performed on an ARM-based single board computer.

Malware technology makes it difficult for malware analyst to detect same malware files with different obfuscation technique. In this paper we are trying to tackle that problem by analyzing the sequence of system call from an executable file. Malware files which actually are the same should have almost identical or at least a similar sequence of system calls. In this paper, we are going to create a model for each malware class consists of malwares from different families based on its sequence of system calls. Method/algorithm that's used in this paper is profile hidden markov model which is a very well-known tool in the biological informatics field for comparing DNA and protein sequences. Malware classes that we are going to build are trojan and worm class. Accuracy for these classes are pretty high, it's above 90% with also a high false positive rate around 37%.

Most popular Web applications rely on persistent databases based on languages like SQL for declarative specification of data models and the operations that read and modify them. As applications scale up in user base, they often face challenges responding quickly enough to the high volume of requests. A common aid is caching of database results in the application's memory space, taking advantage of program-specific knowledge of which caching schemes are sound and useful, embodied in handwritten modifications that make the program less maintainable. These modifications also require nontrivial reasoning about the read-write dependencies across operations. In this paper, we present a compiler optimization that automatically adds sound SQL caching to Web applications coded in the Ur/Web domain-specific functional language, with no modifications required to source code. We use a custom cache implementation that supports concurrent operations without compromising the transactional semantics of the database abstraction. Through experiments with microbenchmarks and production Ur/Web applications, we show that our optimization in many cases enables an easy doubling or more of an application's throughput, requiring nothing more than passing an extra command-line flag to the compiler.

This article presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable emcient and accurate computation of a target's location while preserving the privacy of the observers.

This work concerns distributed consensus algorithms and application to a network intrusion detection system (NIDS) [21]. We consider the problem of defending the system against multiple data falsification attacks (Byzantine attacks), a vulnerability of distributed peer-to-peer consensus algorithms that has not been widely addressed in its practicality. We consider both naive (independent) and colluding attackers. We test three defense strategy implementations, two classified as outlier detection methods and one reputation-based method. We have narrowed our attention to outlier and reputation-based methods because they are relatively light computationally speaking. We have left out control theoretic methods which are likely the most effective methods, however their computational cost increase rapidly with the number of attackers. We compare the efficiency of these three implementations for their computational cost, detection performance, convergence behavior and possible impacts on the intrusion detection accuracy of the NIDS. Tests are performed based on simulations of distributed denial of service attacks using the KSL-KDD data set.

High-accuracy localization is a prerequisite for many wireless applications. To obtain accurate location information, it is often required to share users' positional knowledge and this brings the risk of leaking location information to adversaries during the localization process. This paper develops a theory and algorithms for protecting location secrecy. In particular, we first introduce a location secrecy metric (LSM) for a general measurement model of an eavesdropper. Compared to previous work, the measurement model accounts for parameters such as channel conditions and time offsets in addition to the positions of users. We determine the expression of the LSM for typical scenarios and show how the LSM depends on the capability of an eavesdropper and the quality of the eavesdropper's measurement. Based on the insights gained from the analysis, we consider a case study in wireless localization network and develop an algorithm that diminish the eavesdropper's capabilities by exploiting the reciprocity of channels. Numerical results show that the proposed algorithm can effectively increase the LSM and protect location secrecy.

Today's computing devices keep considerable amounts of sensitive data unencrypted in RAM. When stolen, lost or simply unattended, attackers are capable of accessing the data in RAM with ease. Valuable and possibly classified data falling into the wrongs hands can lead to severe consequences, for instance when disclosed or reused to log in to accounts or to make transactions. We present a lightweight and hardware-independent mechanism to protect confidential data on suspended Linux devices against physical attackers. Our mechanism rapidly encrypts the contents of RAM during suspension and thereby prevents attackers from retrieving confidential data from the device. Existing systems can easily be extended with our mechanism while fully preserving the usability for end users.

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

In vehicular networks, each message is signed by the generating node to ensure accountability for the contents of that message. For privacy reasons, each vehicle uses a collection of certificates, which for accountability reasons are linked at a central authority. One such design is the Security Credential Management System (SCMS) [1], which is the leading credential management system in the US. The SCMS is composed of multiple components, each of which has a different task for key management, which are logically separated. The SCMS is designed to ensure privacy against a single insider compromise, or against outside adversaries. In this paper, we demonstrate that the current SCMS design fails to achieve its design goal, showing that a compromised authority can gain substantial information about certificate linkages. We propose a solution that accommodates threshold-based detection, but uses relabeling and noise to limit the information that can be learned from a single insider adversary. We also analyze our solution using techniques from differential privacy and validate it using traffic-simulator based experiments. Our results show that our proposed solution prevents privacy information leakage against the compromised authority in collusion with outsider attackers.

Routing security has a great importance to the security of Mobile Ad Hoc Networks (MANETs). There are various kinds of attacks when establishing routing path between source and destination. The adversaries attempt to deceive the source node and get the privilege of data transmission. Then they try to launch the malicious behaviors such as passive or active attacks. Due to the characteristics of the MANETs, e.g. dynamic topology, open medium, distributed cooperation, and constrained capability, it is difficult to verify the behavior of nodes and detect malicious nodes without revealing any privacy. In this paper, we present PVad, an approach conducting privacy-preserving verification in the routing discovery phase of MANETs. PVad tries to find the existing communication rules by association rules instead of making the rules. PVad consists of two phases, a reasoning phase deducing the expected log data of the peers, and a verification phase using Merkle Hash Tree to verify the correctness of derived information without revealing any privacy of nodes on expected routing paths. Without deploying any special nodes to assist the verification, PVad can detect multiple malicious nodes by itself. To show our approach can be used to guarantee the security of the MANETs, we conduct our experiments in NS3 as well as the real router environment, and we improved the detection accuracy by 4% on average compared to our former work.

Mobile Ad hoc Networks (MANETs) always bring challenges to the designers in terms of its security deployment due to their dynamic and infrastructure less nature. In the past few years different researchers have proposed different solutions for providing security to MANETs. In most of the cases however, the solution prevents either a particular attack or provides security at the cost of sacrificing the QoS. In this paper we introduce a model that deploys security in MANETs and takes care of the Quality of Services issues to some extent. We have adopted the concept of analyzing the behavior of the node as we believe that if nodes behave properly and in a coordinated fashion, the insecurity level goes drastically down. Our methodology gives the advantage of using this approach

Distributed Denial-of-Service (DDoS) attacks are increasing in frequency and volume on the Internet, and there is evidence that cyber-criminals are turning to Internet-of-Things (IoT) devices such as cameras and vending machines as easy launchpads for large-scale attacks. This paper quantifies the capability of consumer IoT devices to participate in reflective DDoS attacks. We first show that household devices can be exposed to Internet reflection even if they are secured behind home gateways. We then evaluate eight household devices available on the market today, including lightbulbs, webcams, and printers, and experimentally profile their reflective capability, amplification factor, duration, and intensity rate for TCP, SNMP, and SSDP based attacks. Lastly, we demonstrate reflection attacks in a real-world setting involving three IoT-equipped smart-homes, emphasising the imminent need to address this problem before it becomes widespread.

IT system risk assessments are indispensable due to increasing cyber threats within our ever-growing IT systems. Moreover, laws and regulations urge organizations to conduct risk assessments regularly. Even though there exist several risk management frameworks and methodologies, they are in general high level, not defining the risk metrics, risk metrics values and the detailed risk assessment formulas for different risk views. To address this need, we define a novel risk assessment methodology specific to IT systems. Our model is quantitative, both asset and vulnerability centric and defines low and high level risk metrics. High level risk metrics are defined in two general categories; base and attack graph-based. In our paper, we provide a detailed explanation of formulations in each category and make our implemented software publicly available for those who are interested in applying the proposed methodology to their IT systems.

When transferring sensitive data to a non-trusted party, end-users require that the data be kept private. Mobile and IoT application developers want to leverage the sensitive data to provide better user experience and intelligent services. Unfortunately, existing programming abstractions make it impossible to reconcile these two seemingly conflicting objectives. In this paper, we present a novel programming mechanism for distributed managed execution environments that hides sensitive user data, while enabling developers to build powerful and intelligent applications, driven by the properties of the sensitive data. Specifically, the sensitive data is never revealed to clients, being protected by the runtime system. Our abstractions provide declarative and configurable data query interfaces, enforced by a lightweight distributed runtime system. Developers define when and how clients can query the sensitive data's properties (i.e., how long the data remains accessible, how many times its properties can be queried, which data query methods apply, etc.). Based on our evaluation, we argue that integrating our novel mechanism with the Java Virtual Machine (JVM) can address some of the most pertinent privacy problems of IoT and mobile applications.

As modern attacks become more stealthy and persistent, detecting or preventing them at their early stages becomes virtually impossible. Instead, an attack investigation or provenance system aims to continuously monitor and log interesting system events with minimal overhead. Later, if the system observes any anomalous behavior, it analyzes the log to identify who initiated the attack and which resources were affected by the attack and then assess and recover from any damage incurred. However, because of a fundamental tradeoff between log granularity and system performance, existing systems typically record system-call events without detailed program-level activities (e.g., memory operation) required for accurately reconstructing attack causality or demand that every monitored program be instrumented to provide program-level information. To address this issue, we propose RAIN, a Refinable Attack INvestigation system based on a record-replay technology that records system-call events during runtime and performs instruction-level dynamic information flow tracking (DIFT) during on-demand process replay. Instead of replaying every process with DIFT, RAIN conducts system-call-level reachability analysis to filter out unrelated processes and to minimize the number of processes to be replayed, making inter-process DIFT feasible. Evaluation results show that RAIN effectively prunes out unrelated processes and determines attack causality with negligible false positive rates. In addition, the runtime overhead of RAIN is similar to existing system-call level provenance systems and its analysis overhead is much smaller than full-system DIFT.

Cloud computing presents unlimited prospects for Information Technology (IT) industry and business enterprises alike. Rapid advancement brings a dark underbelly of new vulnerabilities and challenges unfolding with alarming regularity. Although cloud technology provides a ubiquitous environment facilitating business enterprises to conduct business across disparate locations, security effectiveness of this platform interspersed with threats which can bring everything that subscribes to the cloud, to a halt raises questions. However advantages of cloud platforms far outweighs drawbacks and study of new challenges helps overcome drawbacks of this technology. One such emerging security threat is of ransomware attack on the cloud which threatens to hold systems and data on cloud network to ransom with widespread damaging implications. This provides huge scope for IT security specialists to sharpen their skillset to overcome this new challenge. This paper covers the broad cloud architecture, current inherent cloud threat mechanisms, ransomware vulnerabilities posed and suggested methods to mitigate it.

This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.

Ransomware attacks are becoming prevalent nowadays with the flourishing of crypto-currencies. As the most harmful variant of ransomware crypto-ransomware encrypts the victim's valuable data, and asks for ransom money. Paying the ransom money, however, may not guarantee recovery of the data being encrypted. Most of the existing work for ransomware defense purely focuses on ransomware detection. A few of them consider data recovery from ransomware attacks, but they are not able to defend against ransomware which can obtain a high system privilege. In this work, we design RDS3, a novel Ransomware Defense Strategy, in which we Stealthily back up data in the Spare space of a computing device, such that the data encrypted by ransomware can be restored. Our key idea is that the spare space which stores the backup data is fully isolated from the ransomware. In this way, the ransomware is not able to ``touch'' the backup data regardless of what privilege it can obtain. Security analysis and experimental evaluation show that RDS3 can mitigate ransomware attacks with an acceptable overhead.

Image and video super-resolution (SR) has been explored for several decades. However, few works are integrated into practical systems for real-time image and video SR. In this work, we present a real-time deep video SpaTial Resolution UpConversion SysTem (STRUCT++). Our demo system achieves real-time performance (50 fps on CPU for CIF sequences and 45 fps on GPU for HDTV videos) and provides several functions: 1) batch processing; 2) full resolution comparison; 3) local region zooming in. These functions are convenient for super-resolution of a batch of videos (at most 10 videos in parallel), comparisons with other approaches and observations of local details of the SR results. The system is built on a Global context aggregation and Local queue jumping Network (GLNet). It has a thinner and deeper network structure to aggregate global context with an additional local queue jumping path to better model local structures of the signal. GLNet achieves state-of-the-art performance for real-time video SR.

A hidden dimension of software and hardware security is secret-revealing information disseminated through side channels. Even the most secure systems tend to reveal their secrets through secret-dependent computation. Secret-dependent computation is detectable by monitoring a system's time, power, outputs, and electromagnetic signature. Common defenses to side channel emanations include adding noise to the channel or making algorithmic changes to eliminate specific side channels. Unfortunately, existing solutions are either, not automatic, not comprehensive, and/or not practical. We propose an isolation-based approach for eliminating power and timing side-channels that is automatic, comprehensive, and practical. Our approach eliminates side channels by leveraging energy harvesting techniques to isolate trusted computation from the rest of the system. Software has the ability to request a fixed-power and fixed-time quantum of isolated computation. By discretizing power and time, our approach controls the granularity of side channel leakage; the only burden on programmers is to ensure that all secret-dependent execution differences converge within a single power/time quantum. We design and implement three approaches to power/time-based quantization and isolation: a wholly-digital version, a hybrid version that uses capacitors for time tracking, and a full-custom version. A key insight we leverage is that capacitors act as resource efficient, workload and environment independent time trackers. We explore the trade-offs of the three designs and look at the challenges ahead.

Ransomware techniques have evolved over time with the most resilient attacks making data recovery practically impossible. This has driven countermeasures to shift towards recovery against prevention but in this paper, we model ransomware attacks from an infection vector point of view. We follow the basic infection chain of crypto ransomware and use Bayesian network statistics to infer some of the most common ransomware infection vectors. We also employ the use of attack and sensor nodes to capture uncertainty in the Bayesian network.

Software attacks are commonly performed against embedded systems in order to access private data or to run restricted services. In this work, we demonstrate some vulnerabilities of commonly use processor which can be leveraged by hackers to attack a system. The targeted devices are based on open processor architectures OpenRISC and RISC-V. Several software exploits are discussed and demonstrated while a hardware countermeasure is proposed and validated on OpenRISC against Return Oriented Programming attack.