Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userControlled Update of Software Components using Concurrent Exection of Patched and Unpatched Versions
51
0
0.0
(
0
)
Added by
Stjepan Gro\\v{s}
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Software patching is a common method of removing vulnerabilities in software components to make IT systems more secure. However, there are many cases where software patching is not possible due to the critical nature of the application, especially when the vendor providing the application guarantees correct operation only in a specific configuration. In this paper, we propose a method to solve this problem. The idea is to run unpatched and patched application instances concurrently, with the unpatched one having complete control and the output of the patched one being used only for comparison, to watch for differences that are consequences of introduced bugs. To test this idea, we developed a system that allows us to run web applications in parallel and tested three web applications. The experiments have shown that the idea is promising for web applications from the technical side. Furthermore, we discuss the potential limitations of this system and the idea in general, how long two instances should run in order to be able to claim with some probability that the patched version has not introduced any new bugs, other potential use cases of the proposed system where two application instances run concurrently, and finally the potential uses of this system with different types of applications, such as SCADA systems.
rate research
Read More
Software debloating is an emerging field of study aimed at improving the security and performance of software by removing excess library code and features that are not needed by the end user (called bloat). Software bloat is pervasive, and several debloating techniques have been proposed to address this problem. While these techniques are effective at reducing bloat, they are not practical for the average user, risk creating unsound programs and introducing vulnerabilities, and are not well suited for debloating complex software such as network protocol implementations. In this paper, we propose CARVE, a simple yet effective security-focused debloating technique that overcomes these limitations. CARVE employs static source code annotation to map software features source code, eliminating the need for advanced software analysis during debloating and reducing the overall level of technical sophistication required by the user. CARVE surpasses existing techniques by introducing debloating with replacement, a technique capable of preserving software interoperability and mitigating the risk of creating an unsound program or introducing a vulnerability. We evaluate CARVE in 12 debloating scenarios and demonstrate security and performance improvements that meet or exceed those of existing techniques.
In spite of the availability of DNSSEC, which protects against cache poisoning even by MitM attackers, many caching DNS resolvers still rely for their security against poisoning on merely validating that DNS responses contain some unpredictable values, copied from the re- quest. These values include the 16 bit identifier field, and other fields, randomised and validated by different patches to DNS. We investigate the prominent patches, and show how attackers can circumvent all of them, namely: - We show how attackers can circumvent source port randomisation, in the (common) case where the resolver connects to the Internet via different NAT devices. - We show how attackers can circumvent IP address randomisation, using some (standard-conforming) resolvers. - We show how attackers can circumvent query randomisation, including both randomisation by prepending a random nonce and case randomisation (0x20 encoding). We present countermeasures preventing our attacks; however, we believe that our attacks provide additional motivation for adoption of DNSSEC (or other MitM-secure defenses).
Software defined networking (SDN) has been adopted to enforce the security of large-scale and complex networks because of its programmable, abstract, centralized intelligent control and global and real-time traffic view. However, the current SDN-based security enforcement mechanisms require network managers to fully understand the underlying configurations of network. Facing the increasingly complex and huge SDN networks, we urgently need a novel security policy management mechanism which can be completely transparent to any underlying information. That is it can permit network managers to define upper-level security policies without containing any underlying information of network, and by means of model transformation system, these upper-level security policies can be transformed into their corresponding lower-level policies containing underlying information automatically. Moreover, it should ensure system model updated by the generated lower-level policies can hold all of security properties defined in upper-level policies. Based on these insights, we propose a security policy model transformation and verification approach for SDN in this paper. We first present the formal definition of a security policy model (SPM) which can be used to specify the security policies used in SDN. Then, we propose a model transformation system based on SDN system model and mapping rules, which can enable network managers to convert SPM model into corresponding underlying network configuration policies automatically, i.e., flow table model (FTM). In order to verify SDN system model updated by the generated FTM models can hold the security properties defined in SPM models, we design a security policy verification system based on model checking. Finally, we utilize a comprehensive case to illustrate the feasibility of the proposed approach.
Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software.Most existing inference approaches assume access to code to collect execution sequences. In this paper, we investigate a black-box scenario, where the system under analysis cannot be instrumented, in this granular fashion.This scenario is particularly prevalent with control systems log analysis in the form of continuous signals. In this situation, an execution trace amounts to a multivariate time-series of input and output signals, where different states of the system correspond to different `phases` in the time-series. The main challenge is to detect when these phase changes take place. Unfortunately, most existing solutions are either univariate, make assumptions on the data distribution, or have limited learning power.Therefore, we propose a hybrid deep neural network that accepts as input a multivariate time series and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns over time.We show how this approach can be used to accurately detect state changes, and how the inferred models can be successfully applied to transfer-learning scenarios, to accurately process traces from different products with similar execution characteristics. Our experimental results on two UAV autopilot case studies indicate that our approach is highly accurate (over 90% F1 score for state classification) and significantly improves baselines (by up to 102% for change point detection).Using transfer learning we also show that up to 90% of the maximum achievable F1 scores in the open-source case study can be achieved by reusing the trained models from the industrial case and only fine tuning them using as low as 5 labeled samples, which reduces the manual labeling effort by 98%.
This paper provides an informal discussion of the formal aspects of software doping.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
