Biblio

SQL injection is one of the major threats to the security of the web applications. Attackers try to gain unauthorized access to the database, which has vital and private information of the users. Many researchers have provided various techniques and practices to protect the web applications from attackers. There is a plethora of techniques available to perform SQL injection and usually not everyone is familiar with every attack. Hence, this kind of attack is still the most prevalent. In this paper, we have presented the types of SQL injections attacks and most dominant ways to prevent them.

Named Data Networking (NDN), a clean-slate data oriented Internet architecture targeting on replacing IP, brings many potential benefits for content distribution. Real deployment of NDN is crucial to verify this new architecture and promote academic research, but work in this field is at an early stage. Due to the fundamental design paradigm difference between NDN and IP, Deploying NDN as IP overlay causes high overhead and inefficient transmission, typically in streaming applications. Aiming at achieving efficient NDN streaming distribution, this paper proposes a transitional architecture of NDN/IP hybrid network dubbed Centaur, which embodies both NDN's smartness, scalability and IP's transmission efficiency and deployment feasibility. In Centaur, the upper NDN module acts as the smart head while the lower IP module functions as the powerful feet. The head is intelligent in content retrieval and self-control, while the IP feet are able to transport large amount of media data faster than that if NDN directly overlaying on IP. To evaluate the performance of our proposal, we implement a real streaming prototype in ndnSIM and compare it with both NDN-Hippo and P2P under various experiment scenarios. The result shows that Centaur can achieve better load balance with lower overhead, which is close to the performance that ideal NDN can achieve. All of these validate that our proposal is a promising choice for the incremental and compatible deployment of NDN.

Remote attestation is a crucial security service particularly relevant to increasingly popular IoT (and other embedded) devices. It allows a trusted party (verifier) to learn the state of a remote, and potentially malware-infected, device (prover). Most existing approaches are static in nature and only check whether benign software is initially loaded on the prover. However, they are vulnerable to runtime attacks that hijack the application's control or data flow, e.g., via return-oriented programming or data-oriented exploits. As a concrete step towards more comprehensive runtime remote attestation, we present the design and implementation of Control-FLow ATtestation (C-FLAT) that enables remote attestation of an application's control-flow path, without requiring the source code. We describe a full prototype implementation of C-FLAT on Raspberry Pi using its ARM TrustZone hardware security extensions. We evaluate C-FLAT's performance using a real-world embedded (cyber-physical) application, and demonstrate its efficacy against control-flow hijacking attacks.

This study stems from the premise that we need to break away from the "reactive" cycle of developing defenses against new DDoS attacks (e.g., amplification) by proactively investigating the potential for new types of DDoS attacks. Our specific focus is on pulsating attacks, a particularly debilitating type that has been hypothesized in the literature. In a pulsating attack, bots coordinate to generate intermittent pulses at target links to significantly reduce the throughput of TCP connections traversing the target. With pulsating attacks, attackers can cause significantly greater damage to legitimate users than traditional link flooding attacks. To date, however, pulsating attacks have been either deemed ineffective or easily defendable for two reasons: (1) they require a central coordinator and can thus be tracked; and (2) they require tight synchronization of pulses, which is difficult even in normal non-congestion scenarios. This paper argues that, in fact, the perceived drawbacks of pulsating attacks are in fact not fundamental. We develop a practical pulsating attack called CICADAS using two key ideas: using both (1) congestion as an implicit signal for decentralized implementation, and (2) a Kalman-filter-based approach to achieve tight synchronization. We validate CICADAS using simulations and wide-area experiments. We also discuss possible countermeasures against this attack.

Mobile platforms are increasingly using Heterogeneous Multi-Processor Systems-on-Chip (HMPSoCs) with differentiated processing cores and GPUs to achieve high performance for graphics-intensive applications such as mobile games. Traditionally, separate CPU and GPU governors are deployed in order to achieve energy efficiency through Dynamic Voltage Frequency Scaling (DVFS), but miss opportunities for further energy savings through coordinated system-level application of DVFS. We present Co-Cap, a cooperative CPU-GPU DVFS strategy that orchestrates energy-efficient CPU and GPU DVFS through coordinated CPU and GPU frequency capping to avoid frequency over-provisioning while maintaining desired performance. Unlike traditional approaches that target a narrow set of mobile games, our Co-Cap approach is applicable across a wide range of mobile games. Our methodology deploys a training phase followed by a deployment phase, allowing not only deployment across a wide range of mobile games with varying graphics workloads, but also across new mobile architectural platforms. Our experimental results across a large set of over 70 mobile games show that Co-Cap improves energy per frame by 10.6% and 10.0% (23.1% and 19.1% in CPU dominant applications) on average and achieves minimal frames per second (FPS) loss by 0.5% and 0.7% (1.3% and 1.7% in CPU dominant applications) on average in training- and deployment sets, respectively, compared to the default CPU and GPU governors, with negligible overhead in execution time and power consumption on the ODROID-XU3 platform.

Graph reordering is a powerful technique to increase the locality of the representations of graphs, which can be helpful in several applications. We study how the technique can be used to improve compression of graphs and inverted indexes. We extend the recent theoretical model of Chierichetti et al. (KDD 2009) for graph compression, and show how it can be employed for compression-friendly reordering of social networks and web graphs and for assigning document identifiers in inverted indexes. We design and implement a novel theoretically sound reordering algorithm that is based on recursive graph bisection. Our experiments show a significant improvement of the compression rate of graph and indexes over existing heuristics. The new method is relatively simple and allows efficient parallel and distributed implementations, which is demonstrated on graphs with billions of vertices and hundreds of billions of edges.

In this work, we constructively combine adaptive wormholes with channel-reciprocity based key establishment (CRKE), which has been proposed as a lightweight security solution for IoT devices and might be even more important for the 5G Tactile Internet and its embedded low-end devices. We present a new secret key generation protocol where two parties compute shared cryptographic keys under narrow-band multi-path fading models over a delayed digital channel. The proposed approach furthermore enables distance-bounding the key establishment process via the coherence time dependencies of the wireless channel. Our scheme is thoroughly evaluated both theoretically and practically. For the latter, we used a testbed based on the IEEE 802.15.4 standard and performed extensive experiments in a real-world manufacturing environment. Additionally, we demonstrate adaptive wormhole attacks (AWOAs) and their consequences on several physical-layer security schemes. Furthermore, we proposed a countermeasure that minimizes the risk of AWOAs.

Coverage-based Greybox Fuzzing (CGF) is a random testing approach that requires no program analysis. A new test is generated by slightly mutating a seed input. If the test exercises a new and interesting path, it is added to the set of seeds; otherwise, it is discarded. We observe that most tests exercise the same few "high-frequency" paths and develop strategies to explore significantly more paths with the same number of tests by gravitating towards low-frequency paths. We explain the challenges and opportunities of CGF using a Markov chain model which specifies the probability that fuzzing the seed that exercises path i generates an input that exercises path j. Each state (i.e., seed) has an energy that specifies the number of inputs to be generated from that seed. We show that CGF is considerably more efficient if energy is inversely proportional to the density of the stationary distribution and increases monotonically every time that seed is chosen. Energy is controlled with a power schedule. We implemented the exponential schedule by extending AFL. In 24 hours, AFLFAST exposes 3 previously unreported CVEs that are not exposed by AFL and exposes 6 previously unreported CVEs 7x faster than AFL. AFLFAST produces at least an order of magnitude more unique crashes than AFL.

One of the adverse effects of shrinking transistor sizes is that processors have become increasingly prone to hardware faults. At the same time, the number of cores per die rises. Consequently, core failures can no longer be ruled out, and future operating systems for many-core machines will have to incorporate fault tolerance mechanisms. We present CSR, a strategy for recovery from unexpected permanent processor faults in commodity operating systems. Our approach overcomes surprise removal of faulty cores, and also tolerates cascading core failures. When a core fails in user mode, CSR terminates the process executing on that core and migrates the remaining processes in its run-queue to other cores. We further show how hardware transactional memory may be used to overcome failures in critical kernel code. Our solution is scalable, incurs low overhead, and is designed to integrate into modern operating systems. We have implemented it in the Linux kernel, using Haswell's Transactional Synchronization Extension, and tested it on a real system.

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

Large scale datacenters are becoming the compute and data platform of large enterprises, but their scale makes them difficult to secure applications running within. We motivate this setting using a real world complex scenario, and propose a data-driven approach to taming this complexity. We discuss several machine learning problems that arise, in particular focusing on inducing so-called whitelist communication policies, from observing masses of communications among networked computing nodes. Briefly, a whitelist policy specifies which machine, or groups of machines, can talk to which. We present some of the challenges and opportunities, such as noisy and incomplete data, non-stationarity, lack of supervision, challenges of evaluation, and describe some of the approaches we have found promising.

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

The usual approach to security for cloud-hosted applications is strong separation. However, it is often the case that the same data is used by different applications, particularly given the increase in data-driven (`big data' and IoT) applications. We argue that access control for the cloud should no longer be application-specific but should be data-centric, associated with the data that can flow between applications. Indeed, the data may originate outside cloud services from diverse sources such as medical monitoring, environmental sensing etc. Information Flow Control (IFC) potentially offers data-centric, system-wide data access control. It has been shown that IFC can be provided at operating system level as part of a PaaS offering, with an acceptable overhead. In this paper we consider how IFC can be integrated with application-specific access control, transparently from application developers, while building from simple IFC primitives, access control policies that align with the data management obligations of cloud providers and tenants.

Trust in SSL-based communication on the Internet is provided by Certificate Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) making sure that it is not revoked. Currently, Certificate Revocation checks (i.e. step (iii) above) are done either via Certificate Revocation Lists (CRLs) or Online Certificate Status Protocol (OCSP) servers. Unfortunately, both current approaches tend to incur such a high overhead that several browsers (including almost all mobile ones) choose not to check certificate revocation status, thereby exposing their users to significant security risks. To address this issue, we propose DCSP: a new low-latency approach that provides up-to-date and accurate certificate revocation information. DCSP capitalizes on the existing scalable and high-performance infrastructure of DNS. DCSP minimizes end user latency while, at the same time, requiring only a small number of cryptographic signatures by the CAs. Our design and initial performance results show that DCSP has the potential to perform an order of magnitude faster than the current state-of-the-art alternatives.

Induction is a successful approach for verification of hardware and software systems. A common practice is to model a system using logical formulas, and then use a decision procedure to verify that some logical formula is an inductive safety invariant for the system. A key ingredient in this approach is coming up with the inductive invariant, which is known as invariant inference. This is a major difficulty, and it is often left for humans or addressed by sound but incomplete abstract interpretation. This paper is motivated by the problem of inductive invariants in shape analysis and in distributed protocols. This paper approaches the general problem of inferring first-order inductive invariants by restricting the language L of candidate invariants. Notice that the problem of invariant inference in a restricted language L differs from the safety problem, since a system may be safe and still not have any inductive invariant in L that proves safety. Clearly, if L is finite (and if testing an inductive invariant is decidable), then inferring invariants in L is decidable. This paper presents some interesting cases when inferring inductive invariants in L is decidable even when L is an infinite language of universal formulas. Decidability is obtained by restricting L and defining a suitable well-quasi-order on the state space. We also present some undecidability results that show that our restrictions are necessary. We further present a framework for systematically constructing infinite languages while keeping the invariant inference problem decidable. We illustrate our approach by showing the decidability of inferring invariants for programs manipulating linked-lists, and for distributed protocols.

A normal computer infected with malware is difficult to detect. There have been several approaches in the last years which analyze the behavior of malware and obtain good results. The malware traffic may be detected, but it is very common to miss-detect normal traffic as malicious and generate false positives. This is specially the case when the methods are tested in real and large networks. The detection errors are generated due to the malware changing and rapidly adapting its domains and patterns to mimic normal connections. To better detect malware infections and separate them from normal traffic we propose to detect the behavior of the group of connections generated by the malware. It is known that malware usually generates various related connections simultaneously and therefore it shows a group pattern. Based on previous experiments, this paper suggests that the behavior of a group of connections can be modelled as a directed cyclic graph with special properties, such as its internal patterns, relationships, frequencies and sequences of connections. By training the group models on known traffic it may be possible to better distinguish between a malware connection and a normal connection.