Biblio

While the number of mobile applications are rapidly growing, these applications are often coming with numerous security flaws due to the lack of appropriate coding practices. Security issues must be addressed earlier in the development lifecycle rather than fixing them after the attacks because the damage might already be extensive. Early elimination of possible security vulnerabilities will help us increase the security of our software and mitigate or reduce the potential damages through data losses or service disruptions caused by malicious attacks. However, many software developers lack necessary security knowledge and skills required at the development stage, and Secure Mobile Software Development (SMSD) is not yet well represented in academia and industry. In this paper, we present a static analysis-based security analysis approach through design and implementation of a plugin for Android Development Studio, namely DroidPatrol. The proposed plugins can support developers by providing list of potential vulnerabilities early.

Application Programming Interfaces (APIs) usually have usage constraints, such as call conditions or call orders. Incorrect usage of these constraints, called API misuse, will result in system crashes, bugs, and even security problems. It is crucial to detect such misuses early in the development process. Though many approaches have been proposed over the last years, recent studies show that API misuses are still prevalent, especially the ones specific to individual projects. In this paper, we strive to improve current API-misuse detection capability for large-scale C programs. First, We propose IMSpec, a lightweight domain-specific language enabling developers to specify API usage constraints in three different aspects (i.e., parameter validation, error handling, and causal calling), which are the majority of API-misuse bugs. Then, we have tailored a constraint guided static analysis engine to automatically parse IMSpec rules and detect API-misuse bugs with rich semantics. We evaluate our approach on widely used benchmarks and real-world projects. The results show that our easily extensible approach performs better than state-of-the-art tools. We also discover 19 previously unknown bugs in real-world open-source projects, all of which have been confirmed by the corresponding developers.

IoT malware detection using control flow graph (CFG)-based features and deep learning networks are widely explored. The main goal of this study is to investigate the robustness of such models against adversarial learning. We designed two approaches to craft adversarial IoT software: off-the-shelf methods and Graph Embedding and Augmentation (GEA) method. In the off-the-shelf adversarial learning attack methods, we examine eight different adversarial learning methods to force the model to misclassification. The GEA approach aims to preserve the functionality and practicality of the generated adversarial sample through a careful embedding of a benign sample to a malicious one. Intensive experiments are conducted to evaluate the performance of the proposed method, showing that off-the-shelf adversarial attack methods are able to achieve a misclassification rate of 100%. In addition, we observed that the GEA approach is able to misclassify all IoT malware samples as benign. The findings of this work highlight the essential need for more robust detection tools against adversarial learning, including features that are not easy to manipulate, unlike CFG-based features. The implications of the study are quite broad, since the approach challenged in this work is widely used for other applications using graphs.

With the rapid development of the mobile Internet, Android has been the most popular mobile operating system. Due to the open nature of Android, c countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect Android malware with 97.49% detection accuracy.

This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pre-trained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware's executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. Signatures generated using TrustSign well represent the real malware behavior during runtime. By leveraging the cloud's virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure. Additionally, by removing the dependency on the malware's executable, our method is capable of signing fileless malware. Thus, we focus our research on in-browser cryptojacking attacks, which current antivirus solutions have difficulty to detect. However, TrustSign is not limited to cryptojacking attacks, as our evaluation included various ransomware samples. TrustSign's signature generation process does not require feature engineering or any additional model training, and it is done in a completely unsupervised manner, obviating the need for a human expert. Therefore, our method has the advantage of dramatically reducing signature generation and distribution time. The results of our experimental evaluation demonstrate TrustSign's ability to generate signatures invariant to the process state over time. By using the signatures generated by TrustSign as input for various supervised classifiers, we achieved 99.5% classification accuracy.

Ransomware is currently one of the most significant cyberthreats to both national infrastructure and the individual, often requiring severe treatment as an antidote. Triaging ran-somware based on its similarity with well-known ransomware samples is an imperative preliminary step in preventing a ransomware pandemic. Selecting the most appropriate triaging method can improve the precision of further static and dynamic analysis in addition to saving significant t ime a nd e ffort. Currently, the most popular and proven triaging methods are fuzzy hashing, import hashing and YARA rules, which can ascertain whether, or to what degree, two ransomware samples are similar to each other. However, the mechanisms of these three methods are quite different and their comparative assessment is difficult. Therefore, this paper presents an evaluation of these three methods for triaging the four most pertinent ransomware categories WannaCry, Locky, Cerber and CryptoWall. It evaluates their triaging performance and run-time system performance, highlighting the limitations of each method.

Operating systems such as Linux break the power of the root user into separate privileges (which Linux calls capabilities) and give processes the ability to enable privileges only when needed and to discard them permanently when the program no longer needs them. However, there is no method of measuring how well the use of such facilities reduces the risk of privilege escalation attacks if the program has a vulnerability. This paper presents PrivAnalyzer, an automated tool that measures how effectively programs use Linux privileges. PrivAnalyzer consists of three components: 1) AutoPriv, an existing LLVM-based C/C++ compiler which uses static analysis to transform a program that uses Linux privileges into a program that safely removes them when no longer needed, 2) ChronoPriv, a new LLVM C/C++ compiler pass that performs dynamic analysis to determine for how long a program retains various privileges, and 3) ROSA, a new bounded model checker that can model the damage a program can do at each program point if an attacker can exploit the program and abuse its privileges. We use PrivAnalyzer to determine how long five privileged open source programs retain the ability to cause serious damage to a system and find that merely transforming a program to drop privileges does not significantly improve security. However, we find that simple refactoring can considerably increase the efficacy of Linux privileges. In two programs that we refactored, we reduced the percentage of execution in which a device file can be read and written from 97% and 88% to 4% and 1%, respectively.

The main challenge for malware researchers is the large amount of data and files that need to be evaluated for potential threats. Researchers analyze a large number of new malware daily and classify them in order to extract common features. Therefore, a system that can ensure and improve the efficiency and accuracy of the classification is of great significance for the study of malware characteristics. A high-performance, high-efficiency automatic classification system based on multi-feature selection fusion of machine learning is proposed in this paper. Its performance and efficiency, according to our experiments, have been greatly improved compared to single-featured systems.

Context: Managing technical debt (TD) associated with external cybersecurity attacks on an organization can significantly improve decisions made when prioritizing which security weaknesses require attention. Whilst source code vulnerabilities can be found using static analysis techniques, malicious external attacks expose the vulnerabilities of a system at runtime and can sometimes remain hidden for long periods of time. By mapping malicious attack tactics to the consequences of weaknesses (i.e. exploitable source code vulnerabilities) we can begin to understand and prioritize the refactoring of the source code vulnerabilities that cause the greatest amount of technical debt on a system. Goal: To establish an approach that maps common external attack tactics to system weaknesses. The consequences of a weakness associated with a specific attack technique can then be used to determine the technical debt principal of said violation; which can be measured in terms of loss of business rather than source code maintenance. Method: We present a position study that uses Jaccard similarity scoring to examine how 11 malicious attack tactics can relate to Common Weakness Enumerations (CWEs). Results: We conduct a study to simulate attacks, and generate dependency graphs between external attacks and the technical consequences associated with CWEs. Conclusion: The mapping of cyber security attacks to weaknesses allows operational staff (SecDevOps) to focus on deploying appropriate countermeasures and allows developers to focus on refactoring the vulnerabilities with the greatest potential for technical debt.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

Static application security testing (SAST) detects vulnerability warnings through static program analysis. Fixing the vulnerability warnings tremendously improves software quality. However, SAST has not been fully utilized by developers due to various reasons: difficulties in handling a large number of reported warnings, a high rate of false warnings, and lack of guidance in fixing the reported warnings. In this paper, we collaborated with security experts from a commercial SAST product and propose a set of approaches (Priv) to help developers better utilize SAST techniques. First, Priv identifies preferred fix locations for the detected vulnerability warnings, and group them based on the common fix locations. Priv also leverages visualization techniques so that developers can quickly investigate the warnings in groups and prioritize their quality-assurance effort. Second, Priv identifies actionable vulnerability warnings by removing SAST-specific false positives. Finally, Priv provides customized fix suggestions for vulnerability warnings. Our evaluation of Priv on six web applications highlights the accuracy and effectiveness of Priv. For 75.3% of the vulnerability warnings, the preferred fix locations found by Priv are identical to the ones annotated by security experts. The visualization based on shared preferred fix locations is useful for prioritizing quality-assurance efforts. Priv reduces the rate of SAST-specific false positives significantly. Finally, Priv is able to provide fully complete and correct fix suggestions for 75.6% of the evaluated warnings. Priv is well received by security experts and some features are already integrated into industrial practice.

Practitioners use infrastructure as code (IaC) scripts to provision servers and development environments. While developing IaC scripts, practitioners may inadvertently introduce security smells. Security smells are recurring coding patterns that are indicative of security weakness and can potentially lead to security breaches. The goal of this paper is to help practitioners avoid insecure coding practices while developing infrastructure as code (IaC) scripts through an empirical study of security smells in IaC scripts. We apply qualitative analysis on 1,726 IaC scripts to identify seven security smells. Next, we implement and validate a static analysis tool called Security Linter for Infrastructure as Code scripts (SLIC) to identify the occurrence of each smell in 15,232 IaC scripts collected from 293 open source repositories. We identify 21,201 occurrences of security smells that include 1,326 occurrences of hard-coded passwords. We submitted bug reports for 1,000 randomly-selected security smell occurrences. We obtain 212 responses to these bug reports, of which 148 occurrences were accepted by the development teams to be fixed. We observe security smells can have a long lifetime, e.g., a hard-coded secret can persist for as long as 98 months, with a median lifetime of 20 months.

Security vulnerabilities and software defects are prevalent in software systems, threatening every aspect of cyberspace. The complexity of modern software makes it hard to secure systems. Security vulnerabilities and software defects become a major target of cyberattacks which can lead to significant consequences. Manual identification of vulnerabilities and defects in software systems is very time-consuming and tedious. Many tools have been designed to help analyze software systems and to discover vulnerabilities and defects. However, these tools tend to miss various types of bugs. The bugs that are not caught by these tools usually include vulnerabilities and defects that are too complicated to find or do not fall inside of an existing rule-set for identification. It was hypothesized that these undiscovered vulnerabilities and defects do not occur randomly, rather, they share certain common characteristics. A methodology was proposed to detect the probability of a bug existing in a code structure. We used a comprehensive experimental evaluation to assess the methodology and report our findings.

Cryptojacking is the permissionless use of a target device to covertly mine cryptocurrencies. With cryptojacking attackers use malicious JavaScript codes to force web browsers into solving proof-of-work puzzles, thus making money by exploiting resources of the website visitors. To understand and counter such attacks, we systematically analyze the static, dynamic, and economic aspects of in-browser cryptojacking. For static analysis, we perform content-, currency-, and code-based categorization of cryptojacking samples to 1) measure their distribution across websites, 2) highlight their platform affinities, and 3) study their code complexities. We apply unsupervised learning to distinguish cryptojacking scripts from benign and other malicious JavaScript samples with 96.4% accuracy. For dynamic analysis, we analyze the effect of cryptojacking on critical system resources, such as CPU and battery usage. Additionally, we perform web browser fingerprinting to analyze the information exchange between the victim node and the dropzone cryptojacking server. We also build an analytical model to empirically evaluate the feasibility of cryptojacking as an alternative to online advertisement. Our results show a large negative profit and loss gap, indicating that the model is economically impractical. Finally, by leveraging insights from our analyses, we build countermeasures for in-browser cryptojacking that improve upon the existing remedies.

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

With the rapid growth of Linux-based IoT devices such as network cameras and routers, the security becomes a concern and many attacks utilize vulnerabilities to compromise the devices. It is crucial for researchers to find vulnerabilities in IoT systems before attackers. Fuzzing is an effective vulnerability discovery technique for traditional desktop programs, but could not be directly applied to Linux-based IoT programs due to the special execution environment requirement. In our paper, we propose an efficient greybox fuzzing scheme for Linux-based IoT programs which consist of two phases: binary static analysis and IoT program greybox fuzzing. The binary static analysis is to help generate useful inputs for efficient fuzzing. The IoT program greybox fuzzing is to reinforce the IoT firmware kernel greybox fuzzer to support IoT programs. We implement a prototype system and the evaluation results indicate that our system could automatically find vulnerabilities in real-world Linux-based IoT programs efficiently.

Context: Managing technical debt (TD) associated with potential security breaches found during design can lead to catching vulnerabilities (i.e., exploitable weaknesses) earlier in the software lifecycle; thus, anticipating TD principal and interest that can have decidedly negative impacts on businesses. Goal: To establish an approach to help assess TD associated with security weaknesses by leveraging the Common Weakness Enumeration (CWE) and its scoring mechanism, the Common Weakness Scoring System (CWSS). Method: We present a position study with a five-step approach employing the Quamoco quality model to operationalize the scoring of architectural CWEs. Results: We use static analysis to detect design level CWEs, calculate their CWSS scores, and provide a relative ranking of weaknesses that help practitioners identify the highest risks in an organization with a potential to impact TD. Conclusion: CWSS is a community agreed upon method that should be leveraged to help inform the ranking of security related TD items.

This paper presents Checked C, an extension to C designed to support spatial safety, implemented in Clang and LLVM. Checked C's design is distinguished by its focus on backward-compatibility, incremental conversion, developer control, and enabling highly performant code. Like past approaches to a safer C, Checked C employs a form of checked pointer whose accesses can be statically or dynamically verified. Performance evaluation on a set of standard benchmark programs shows overheads to be relatively low. More interestingly, Checked C introduces the notions of a checked region and bounds-safe interfaces.

To obtain precise and sound results, most of existing static analyzers require whole program analysis with complete source code. However, in reality, the source code of an application always interacts with many third-party libraries, which are often not easily accessible to static analyzers. Worse still, more than 30% of legacy projects [1] cannot be compiled easily due to complicated configuration environments (e.g., third-party libraries, compiler options and macros), making ideal "whole-program analysis" unavailable in practice. This paper presents CoBOT [2], a static analysis tool that can detect bugs in the presence of incomplete code. It analyzes function APIs unavailable in application code by either using function summarization or automatically downloading and analyzing the corresponding library code as inferred from the application code and its configuration files. The experiments show that CoBOT is not only easy to use, but also effective in detecting bugs in real-world programs with incomplete code. Our demonstration video is at: https://youtu.be/bhjJp3e7LPM.

Web applications are now considered one of the common platforms to represent data and conducting service releases throughout the World Wide Web. A number of the most commonly utilised frameworks for web applications are written in PHP. They became main targets because a vast number of servers are running these applications throughout the world. This increase in web application utilisation has made it more attractive to both users and hackers. According to the latest web security reports and research, cross site scripting (XSS) is the most popular vulnerability in PHP web application. XSS is considered an injection type of attack, which results in the theft of sensitive data, cookies, and sessions. Several tools and approaches have focused on detecting this kind of vulnerability in PHP source code. However, it is still a current problem in PHP web applications. This paper describes the popularity of PHP technology among other technologies, and highlight the approaches used to detect the most common vulnerabilities on PHP web applications, which is XSS. In addition, the discussion and the conclusion with future direction of research within this domain are highlighted.

When implemented on real systems, cryptographic algorithms are vulnerable to attacks observing their execution behavior, such as cache-timing attacks. Designing protected implementations must be done with knowledge and validation tools as early as possible in the development cycle. In this article we propose a methodology to assess the robustness of the candidates for the NIST post-quantum standardization project to cache-timing attacks. To this end we have developed a dedicated vulnerability research tool. It performs a static analysis with tainting propagation of sensitive variables across the source code and detects leakage patterns. We use it to assess the security of the NIST post-quantum cryptography project submissions. Our results show that more than 80% of the analyzed implementations have at least one potential flaw, and three submissions total more than 1000 reported flaws each. Finally, this comprehensive study of the competitors security allows us to identify the most frequent weaknesses amongst candidates and how they might be fixed.

The article considers the approach to identifying potentially unsafe data in program code of embedded systems which can lead to errors and fails in the functioning of equipment. The sources of invalid data are revealed and the process of changing the status of this data in process of static code analysis is shown. The mechanism for annotating functions that operate on unsafe data is described, which allows to control the entire process of using them and thus it will improve the quality of the output code.

Cloud server systems such as Hadoop and Cassandra have enabled many real-world data-intensive applications running inside computing clouds. However, those systems present many data-corruption and performance problems which are notoriously difficult to debug due to the lack of diagnosis information. In this paper, we present DScope, a tool that statically detects data-corruption related software hang bugs in cloud server systems. DScope statically analyzes I/O operations and loops in a software package, and identifies loops whose exit conditions can be affected by I/O operations through returned data, returned error code, or I/O exception handling. After identifying those loops which are prone to hang problems under data corruption, DScope conducts loop bound and loop stride analysis to prune out false positives. We have implemented DScope and evaluated it using 9 common cloud server systems. Our results show that DScope can detect 42 real software hang bugs including 29 newly discovered software hang bugs. In contrast, existing bug detection tools miss detecting most of those bugs.

Smartphones have evolved over the years from simple devices to communicate with each other to fully functional portable computers although with comparatively less computational power but inholding multiple applications within. With the smartphone revolution, the value of personal data has increased. As technological complexities increase, so do the vulnerabilities in the system. Smartphones are the latest target for attacks. Android being an open source platform and also the most widely used smartphone OS draws the attention of many malware writers to exploit the vulnerabilities of it. Attackers try to take advantage of these vulnerabilities and fool the user and misuse their data. Malwares have come a long way from simple worms to sophisticated DDOS using Botnets, the latest trends in computer malware tend to go in the distributed direction, to evade the multiple anti-virus apps developed to counter generic viruses and Trojans. However, the recent trend in android system is to have a combination of applications which acts as malware. The applications are benign individually but when grouped, these may result into a malicious activity. This paper proposes a new category of distributed malware in android system, how it can be used to evade the current security, and how it can be detected with the help of graph matching algorithm.

Software vulnerabilities are a primary concern in the IT security industry, as malicious hackers who discover these vulnerabilities can often exploit them for nefarious purposes. However, complex programs, particularly those written in a relatively low-level language like C, are difficult to fully scan for bugs, even when both manual and automated techniques are used. Since analyzing code and making sure it is securely written is proven to be a non-trivial task, both static analysis and dynamic analysis techniques have been heavily investigated, and this work focuses on the former. The contribution of this paper is a demonstration of how it is possible to catch a large percentage of bugs by extracting text features from functions in C source code and analyzing them with a machine learning classifier. Relatively simple features (character count, character diversity, entropy, maximum nesting depth, arrow count, "if" count, "if" complexity, "while" count, and "for" count) were extracted from these functions, and so were complex features (character n-grams, word n-grams, and suffix trees). The simple features performed unexpectedly better compared to the complex features (74% accuracy compared to 69% accuracy).