Biblio

Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.

Security is often a critical problem in software systems. The consequences of the failure lead to substantial economic loss or extensive environmental damage. Developing secure software is challenging, and retrofitting existing systems to introduce security is even harder. In this paper, we propose an automated approach for Finding and Repairing Bugs based on security patterns (FireBugs), to repair defects causing security vulnerabilities. To locate and fix security bugs, we apply security patterns that are reusable solutions comprising large amounts of software design experience in many different situations. In the evaluation, we investigated 2,800 Android app repositories to apply our approach to 200 subject projects that use javax.crypto APIs. The vision of our automated approach is to reduce software maintenance burdens where the number of outstanding software defects exceeds available resources. Our ultimate vision is to design more security patterns that have a positive impact on software quality by disseminating correlated sets of best security design practices and knowledge.

In recent years, various benchmark suites have been developed to evaluate the efficacy of Android security analysis tools. Tool developers often choose such suites based on the availability and popularity of suites and not on their characteristics and relevance due to the lack of information about them. In this context, based on a recent effort, we empirically evaluated four Android-specific benchmark suites: DroidBench, Ghera, ICCBench, and UBCBench. For each benchmark suite, we identified the APIs used by the suite that were discussed on Stack Overflow in the context of Android app development and measured the usage of these APIs in a sample of 227K real-world apps (coverage). We also identified security-related APIs used in real-world apps but not in any of the above benchmark suites to assess the opportunities to extend benchmark suites (gaps).

With the advancements in enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). API management and classification is a cumbersome task considering the rapid increase in the number of APIs, and API to API calls. API Mashups, domain APIs and API service mesh are a few recommended techniques for ease of API creation, management, and monitoring. API service mesh is considered as one of the techniques in this regard, in which the service plane and the control plane are separated for improving efficiency as well as security. In this paper, we propose and implement a security framework for the creation of a secure API service mesh using Istio and Kubernetes. Afterwards, we propose an smart association model for automatic association of new APIs to already existing categories of service mesh. To the best of our knowledge, this smart association model is the first of its kind.

Malware variants exhibit polymorphic attacks due to the tremendous growth of the present technologies. For instance, ransomware, an astonishingly growing set of monetary-gain threats in the recent years, is peculiarized as one of the most treacherous cyberthreats against innocent individuals and businesses by locking their devices and/or encrypting their files. Many proposed attempts have been introduced by cybersecurity researchers aiming at mitigating the epidemic of the ransomware attacks. However, this type of malware is kept refined by utilizing new evasion techniques, such as sophisticated codes, dynamic payloads, and anti-emulation techniques, in order to survive against detection systems. This paper introduces RanDetector, a new automated and lightweight system for detecting ransomware applications in Android platform based on their behavior. In particular, this detection system investigates the appearance of some information that is related to ransomware operations in an inspected application before integrating some supervised machine learning models to classify the application. RanDetector is evaluated and tested on a dataset of more 450 applications, including benign and ransomware. Hence, RanDetector has successfully achieved more that 97.62% detection rate with nearly zero false positive.

Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.

In this vision paper, we focus on a key aspect of the modern software developer's potential to write secure software: their (lack of) success in securely using cryptography APIs. In particular, we note that most ongoing research tends to focus on identifying concrete problems software developers experience, and providing workable solutions, but that such solutions still require developers to identify the appropriate API calls to make and, worse, to be familiar with and configure sometimes obscure parameters of such calls. In contrast, we envision identifying and employing targeted visual metaphors to allow developers to simply select the most appropriate cryptographic functionality they need.

In Android malware detection, recent work has shown that using contextual information of sensitive API invocation in the modeling of applications is able to improve the classification accuracy. However, the improvement brought by this context-awareness varies depending on how this information is used in the modeling. In this paper, we perform a comprehensive study on the effectiveness of using the contextual information in prior state-of-the-art detection systems. We find that this information has been "over-used" such that a large amount of non-essential metadata built into the models weakens the generalizability and longevity of the model, thus finally affects the detection accuracy. On the other hand, we find that the entrypoint of API invocation has the strongest impact on the classification correctness, which can further improve the accuracy if being properly captured. Based on this finding, we design and implement a lightweight, circumstance-aware detection system, named "PIKADROID" that only uses the API invocation and its entrypoint in the modeling. For extracting the meaningful entrypoints, PIKADROID applies a set of static analysis techniques to extract and sanitize the reachable entrypoints of a sensitive API, then constructs a frequency model for classification decision. In the evaluation, we show that this slim model significantly improves the detection accuracy on a data set of 23,631 applications by achieving an f-score of 97.41%, while maintaining a false positive rating of 0.96%.

The Android research community has long focused on building an Android API permission specification, which can be leveraged by app developers to determine the optimum set of permissions necessary for a correct and safe execution of their app. However, while prominent existing efforts provide a good approximation of the permission specification, they suffer from a few shortcomings. Dynamic approaches cannot generate complete results, although accurate for the particular execution. In contrast, static approaches provide better coverage, but produce imprecise mappings due to their lack of path-sensitivity. In fact, in light of Android's access control complexity, the approximations hardly abstract the actual co-relations between enforced protections. To address this, we propose to precisely derive Android protection specification in a path-sensitive fashion, using a novel graph abstraction technique. We further showcase how we can apply the generated maps to tackle security issues through logical satisfiability reasoning. Our constructed maps for 4 Android Open Source Project (AOSP) images highlight the significance of our approach, as \textasciitilde41% of APIs' protections cannot be correctly modeled without our technique.

Code reuse attacks have been a threat to software security since the introduction of non-executable memory protections. Despite significant advances in various types of additional defenses, such as control flow integrity (CFI) and leakage-resilient code randomization, recent code reuse attacks have demonstrated that these defenses are often not enough to prevent successful exploitation. Sophisticated exploits can reuse code comprising larger code fragments that conform to the enforced CFI policy and which are not affected by randomization. As a step towards improving our defenses against code reuse attacks, in this paper we present Shredder, a defense-in-depth exploit mitigation tool for the protection of closed-source applications. In a preprocessing phase, Shredder statically analyzes a given application to pinpoint the call sites of potentially useful (to attackers) system API functions, and uses backwards data flow analysis to derive their expected argument values and generate whitelisting policies in a best-effort way. At runtime, using library interposition, Shredder exposes to the protected application only specialized versions of these critical API functions, and blocks any invocation that violates the enforced policy. We have experimentally evaluated our prototype implementation for Windows programs using a large set of 251 shellcode and 30 code reuse samples, and show that it improves significantly upon code stripping, a state-of-the-art code surface reduction technique, by blocking a larger number of malicious payloads with negligible runtime overhead.

Although the Android system has been continuously hardened against side-channel attacks, there are still plenty of APIs available that can be exploited. However, most side-channel analyses in the literature consider specifically chosen APIs (or resources) in the Android framework, after a manual analysis of APIs for possible information leaks has been performed. Such a manual analysis is a tedious, time consuming, and error-prone task, meaning that information leaks tend to be overlooked. To overcome this tedious task, we introduce SCANDROID, a framework that automatically profiles the Java-based Android API for possible information leaks. Events of interest, such as website launches, Google Maps queries, or application starts, are triggered automatically, and while these events are being triggered, the Java-based Android API is analyzed for possible information leaks that allow inferring these events later on. To assess the Android API for information leaks, SCANDROID relies on dynamic time warping. By applying SCANDROID on Android 8 (Android Oreo), we identified several Android APIs that allow inferring website launches, Google Maps queries, and application starts. The triggered events are by no means exhaustive but have been chosen to demonstrate the broad applicability of SCANDROID. Among the automatically identified information leaks are, for example, the java.io.File API, the android.os.storage.StorageManager API, and several methods within the android.net. Traffics tats API. Thereby, we identify the first side-channel leaks in the Android API on Android 8 (Android Oreo).

Security assurance is the confidence that a system meets its security requirements based on specific evidences that an assurance technique provide. The notion of measuring security is complex and tricky. Existing approaches either (1) consider one aspect of assurance, like security requirements fulfillment, or threat/vulnerability existence, or (2) do not consider the relevance of the different security requirements to the evaluated application context. Furthermore, they are mostly qualitative in nature and are heavily based on manual processing, which make them costly and time consuming. Therefore, they are not widely used and applied, especially by small and medium-sized enterprises (SME), which constitute the backbone of the Norwegian economy. In this paper, we propose a quantification method that aims at evaluating security assurance of systems by measuring (1) the level of confidence that the mechanisms fulfilling security requirements are present and (2) the vulnerabilities associated with possible security threats are absent. Additionally, an assurance evaluation process is proposed. Two case studies applying our method are presented. The case studies use our assurance method to evaluate the security level of two REST APIs developed by Statistics Norway, where one of the authors is employed. Analysis shows that the API with the most security mechanisms implemented got a slightly higher security assurance score. Security requirement relevance and vulnerability impact played a role in the overall scores.

Software Defined Networking (SDN) is a programmable network technology which aims to move an existing controller role in hardware equipment into an area of software. The control layer employs an application programming interface (API) to communicate with the application and infrastructure layers as it is centered between two layers. As the Southbound API used in communication with the infrastructure layer, the OpenFlow is defined as the current de factor standard in most SDN controllers. In contrast, the Northbound API used in communication with the application layer had no standard. Only REST API is used in Floodlight or OpenDaylight. Thus, the development in application area where SDN's true value lies to achieve network intelligence is not promoted well enough. In this paper, a gRPC protocol is proposed as useable Northbound API rather than REST API used in some controllers, and applicability of new standard as Northbound API is investigated.

Deprecation is a language feature that allows API producers to mark a feature as obsolete. We aim to gain a deep understanding of the needs of API producers and consumers alike regarding deprecation. To that end, we investigate why API producers deprecate features, whether they remove deprecated features, how they expect consumers to react, and what prompts an API consumer to react to deprecation. To achieve this goal we conduct semi-structured interviews with 17 third-party Java API producers and survey 170 Java developers. We observe that the current deprecation mechanism in Java and the proposal to enhance it does not address all the needs of a developer. This leads us to propose and evaluate three further enhancements to the deprecation mechanism.

It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.

In this paper we present a case study of applying fitness dimensions in API design assessment. We argue that API assessment is company specific and should take into consideration various stakeholders in the API ecosystem. We identified new fitness dimensions and introduced the notion of design considerations for fitness dimensions such as priorities, tradeoffs, and technical versus cognitive classification.

The growing popularity of Android applications makes them vulnerable to security threats. There exist several studies that focus on the analysis of the behaviour of Android applications to detect the repackaged and malicious ones. These techniques use a variety of features to model the application's behaviour, among which the calls to Android API, made by the application components, are shown to be the most reliable. To generate the APIs that an application calls is not an easy task. This is because most malicious applications are obfuscated and do not come with the source code. This makes the problem of identifying the API methods invoked by an application an interesting research issue. In this paper, we present HyDroid, a hybrid approach that combines static and dynamic analysis to generate API call traces from the execution of an application's services. We focus on services because they contain key characteristics that allure attackers to misuse them. We show that HyDroid can be used to extract API call trace signatures of several malware families.

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.

Cryptographic APIs are often vulnerable to attacks that compromise sensitive cryptographic keys. In the literature we find many proposals for preventing or mitigating such attacks but they typically require to modify the API or to configure it in a way that might break existing applications. This makes it hard to adopt such proposals, especially because security APIs are often used in highly sensitive settings, such as financial and critical infrastructures, where systems are rarely modified and legacy applications are very common. In this paper we take a different approach. We propose an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. The method collects logs for various devices and cryptographic services and is able to detect, offline, any leakage of sensitive keys, under the assumption that a key fingerprint is provided for each sensitive key. We define key security formally and we prove that the method is sound, complete and efficient. We also show that without key fingerprinting completeness is lost, i.e., some attacks cannot be detected. We discuss possible practical implementations and we develop a proof-of-concept log analysis tool for PKCS\#11 that is able to detect, on a significant fragment of the API, all key-management attacks from the literature.

Biometric authentication has been extremely popular in large scale industries. The face biometric has been used widely in various applications. Handling large numbers of face images is a challenging task in authentication of biometric system. It requires large amount of secure storage, where the registered user information can be stored. Maintaining centralized data centers to store the information requires high investment and maintenance cost, therefore there is a need for deployment of cloud services. However as there is no guaranty of the security in the cloud, user needs to implement an additional or extra layer of security before storing facial data of all registered users. In this work a unique cloud based biometric authentication system is developed using Microsoft cognitive face API. Because most of the cloud based biometric techniques are scalable it is paramount to implement a security technique which can handle the scalability. Any users can use this system for single enterprise application base over the entire enterprise application. In this work the identification number which is text information associated with each biometric image is protected by AES algorithm. The proposed technique also works under distributed system in order to have wider accessibility. The system is also being extended to validate the registered user with an image of aadhar card. An accuracy of 96% is achieved with 100 registered users face images and aadhar card images. Earlier research carried out for the development of biometric system either suffers from development of distributed system are security aspects to handle multiple biometric information such as facial image and aadhar card image.

Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.

Honeypots are servers or systems built to mimic critical parts of a network, distracting attackers while logging their information to develop attack profiles. This paper discusses the design and implementation of a honeypot disguised as a REpresentational State Transfer (REST) Application Programming Interface (API). We discuss the motivation for this work, design features of the honeypot, and experimental performance results under various traffic conditions. We also present analyses of both a distributed denial of service (DDoS) attack and a cross-site scripting (XSS) malware insertion attempt against this honeypot.

Internet of Things devices (IoT-D) have limited resource capacity. But these devices can share resources. Hence, they are being used in variety of applications in various fields including smart city, smart energy, healthcare etc. Traditional practice of medicine and healthcare is mostly heuristic driven. There exist big gaps in our understanding of human body, disease and health. We can use upcoming digital revolution to turn healthcare upside down with data-driven medical science. Various healthcare companies now provide remote healthcare services. Healthcare professionals are also adapting remote healthcare monitoring practices so as to monitor patients who are either hospitalized or executing their normal lifestyle activities at remote locations. Wearable devices available in the market calculate different health parameters and corresponding applications pass the information to server through their proprietary platforms. However, these devices or applications cannot directly communicate or share the data. So, there needs an API to access health and wellness data from different wearable medical devices and applications. This paper proposes and demonstrates an API to connect different wearable healthcare devices and transfer patient personal information securely to the doctor or health provider.

Use of digital token - which certifies the bearer's rights to some kind of products or services - is quite common nowadays for its convenience, ease of use and cost-effectiveness. Many of such digital tokens, however, are produced with software alone, making them vulnerable to forgery, including alteration and duplication. For a more secure safeguard for both token owner's right and service provider's accountability, digital tokens should be tamper-resistant as much as possible in order for them to withstand physical attacks as well. In this paper, we present a rights management system that leverages tamper-resistant digital tokens created by hardware-software collaboration in our eTRON architecture. The system features the complete life cycle of a digital token from generation to storage and redemption. Additionally, it provides a secure mechanism for transfer of rights in a peer-to-peer manner over the Internet. The proposed system specifies protocols for permissible manipulation on digital tokens, and subsequently provides a set of APIs for seamless application development. Access privileges to the tokens are strictly defined and state-of-the-art asymmetric cryptography is used for ensuring their confidentiality. Apart from the digital tokens being physically tamper-resistant, the protocols involved in the system are proven to be secure against attacks. Furthermore, an authentication mechanism is implemented that invariably precedes any operation involving the digital token in question. The proposed system presents clear security gains compared to existing systems that do not take tamper-resistance into account, and schemes that use symmetric key cryptography.