Biblio
Timing slacks possibly lead to reliability issues and/or security vulnerabilities, as they may hide small delay defects and malicious circuitries injected during fabrication, namely, hardware Trojans. While possibly harmless immediately after production, small delay defects may trigger reliability problems as the part is being used in field, presenting a significant threat for mission-critical applications. Hardware Trojans remain dormant while the part is tested and validated, but then get activated to launch an attack when the chip is deployed in security-critical applications. In this paper, we take a deeper look into these problems and their underlying reasons, and propose a design technique to maximize the detection of small delay defects as well as the hardware Trojans. The proposed technique eliminates all slacks by judiciously inserting delay units in a small set of locations in the circuit, thereby rendering a simple set of transition fault patterns quite effective in catching parts with small delay defects or Trojans. Experimental results also justify the efficacy of the proposed technique in improving the quality of test while retaining the pattern count and care bit density intact.
Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) and Cross-Site Scripting (XSS) vulnerabilities are most dangerous security vulnerabilities exploited in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on defensive programming, vulnerability detection and attack prevention techniques has been quite intensive in the past decade. Defensive programming is a set of coding guidelines to develop secure applications. But, mostly developers do not follow security guidelines and repeat same type of programming mistakes in their code. Attack prevention techniques protect the applications from attack during their execution in actual environment. The difficulties associated with accurate detection of SQLI and XSS vulnerabilities in coding phase of software development life cycle. This paper proposes a classification of software security approaches used to develop secure software in various phase of software development life cycle. It also presents a survey of static analysis based approaches to detect SQL Injection and cross-site scripting vulnerabilities in source code of web applications. The aim of these approaches is to identify the weaknesses in source code before their exploitation in actual environment. This paper would help researchers to note down future direction for securing legacy web applications in early phases of software development life cycle.
Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.
Cross-Site Scripting (XSS) is a common attack technique that lets attackers insert the code in the output application of web page which is referred to the web browser of visitor and then the inserted code executes automatically and steals the sensitive information. In order to prevent the users from XSS attack, many client- side solutions have been implemented; most of them being used are the filters that sanitize the malicious input. However, many of these filters do not provide prevention to the newly designed sophisticated attacks such as multiple points of injection, injection into script etc. This paper proposes and implements an approach based on encoding unfiltered reflections for detecting vulnerable web applications which can be exploited using above mentioned sophisticated attacks. Results prove that the proposed approach provides accurate higher detection rate of exploits. In addition to this, an implementation of blocking the execution of malicious scripts have contributed to XSS-Me: an open source Mozilla Firefox security extension that detects for reflected XSS vulnerabilities which can be considered as an effective solution if it is integrated inside the browser rather than being enforced as an extension.
In the field of scene understanding, researchers have mainly focused on using video/images to extract different elements in a scene. The computational as well as monetary cost associated with such implementations is high. This paper proposes a low-cost system which uses sound-based techniques in order to jointly perform localization as well as fingerprinting of the sound sources. A network of embedded nodes is used to sense the sound inputs. Phase-based sound localization and Support-Vector Machine classification are used to locate and classify elements of the scene, respectively. The fusion of all this data presents a complete “picture” of the scene. The proposed concepts are applied to a vehicular-traffic case study. Experiments show that the system has a fingerprinting accuracy of up to 97.5%, localization error less than 4 degrees and scene prediction accuracy of 100%.
The National Cyber Range (NCR) is an innovative Department of Defense (DoD) resource originally established by the Defense Advanced Research Projects Agency (DARPA) and now under the purview of the Test Resource Management Center (TRMC). It provides a unique environment for cyber security testing throughout the program development life cycle using unique methods to assess resiliency to advanced cyberspace security threats. This paper describes what a cyber security range is, how it might be employed, and the advantages a program manager (PM) can gain in applying the results of range events. Creating realism in a test environment isolated from the operational environment is a special challenge in cyberspace. Representing the scale and diversity of the complex DoD communications networks at a fidelity detailed enough to realistically portray current and anticipated attack strategies (e.g., Malware, distributed denial of service attacks, cross-site scripting) is complex. The NCR addresses this challenge by representing an Internet-like environment by employing a multitude of virtual machines and physical hardware augmented with traffic emulation, port/protocol/service vulnerability scanning, and data capture tools. Coupled with a structured test methodology, the PM can efficiently and effectively engage with the Range to gain cyberspace resiliency insights. The NCR capability, when applied, allows the DoD to incorporate cyber security early to avoid high cost integration at the end of the development life cycle. This paper provides an overview of the resources of the NCR which may be especially helpful for DoD PMs to find the best approach for testing the cyberspace resiliency of their systems under development.
In an attempt to support customization, many web applications allow the integration of third-party server-side plugins that offer diverse functionality, but also open an additional door for security vulnerabilities. In this paper we study the use of static code analysis tools to detect vulnerabilities in the plugins of the web application. The goal is twofold: 1) to study the effectiveness of static analysis on the detection of web application plugin vulnerabilities, and 2) to understand the potential impact of those plugins in the security of the core web application. We use two static code analyzers to evaluate a large number of plugins for a widely used Content Manage-ment System. Results show that many plugins that are current-ly deployed worldwide have dangerous Cross Site Scripting and SQL Injection vulnerabilities that can be easily exploited, and that even widely used static analysis tools may present disappointing vulnerability coverage and false positive rates.
This scientific paper reveals an intelligent system for data acquisition for dam monitoring and diagnose. This system is built around the RS485 communication standard and uses its own communication protocol [2]. The aim of the system is to monitor all signal levels inside the communication bus, respectively to detect the out of action data loggers. The diagnose test extracts the following functional parameters: supply voltage and the absolute value and common mode value for differential signals used in data transmission (denoted with “A” and “B”). Analyzing this acquired information, it's possible to find short-circuits or open-circuits across the communication bus. The measurement and signal processing functions, for flaws, are implemented inside the system's central processing unit. The next testing step is finding the out of action data loggers and is being made by trying to communicate with every data logger inside the network. The lack of any response from a data logger is interpreted as an error and using the code of the data logger's microcontroller, it is possible to find its exact position inside the dam infrastructure. The novelty of this procedure is the fact that it completely automates the diagnose procedure, which, until now, was made visually by checking every data logger.
Practical intrusion detection in Wireless Multihop Networks (WMNs) is a hard challenge. The distributed nature of the network makes centralized intrusion detection difficult, while resource constraints of the nodes and the characteristics of the wireless medium often render decentralized, node-based approaches impractical. We demonstrate that an active-probing-based network intrusion detection system (AP-NIDS) is practical for WMNs. The key contribution of this paper is to optimize the active probing process: we introduce a general Bayesian model and design a probe selection algorithm that reduces the number of probes while maximizing the insights gathered by the AP-NIDS. We validate our model by means of testbed experimentation. We integrate it to our open source AP-NIDS DogoIDS and run it in an indoor wireless mesh testbed utilizing the IEEE 802.11s protocol. For the example of a selective packet dropping attack, we develop the detection states for our Bayes model, and show its feasibility. We demonstrate that our approach does not need to execute the complete set of probes, yet we obtain good detection rates.
Dynamic taint analysis and forward symbolic execution are quickly becoming staple techniques in security analyses. Example applications of dynamic taint analysis and forward symbolic execution include malware analysis, input filter generation, test case generation, and vulnerability discovery. Despite the widespread usage of these two techniques, there has been little effort to formally define the algorithms and summarize the critical issues that arise when these techniques are used in typical security contexts. The contributions of this paper are two-fold. First, we precisely describe the algorithms for dynamic taint analysis and forward symbolic execution as extensions to the run-time semantics of a general language. Second, we highlight important implementation choices, common pitfalls, and considerations when using these techniques in a security context.