Biblio

Web application services have gradually become an important support of Internet services, but are also facing increasingly serious security problems. It is extremely necessary to evaluate the security of Web application services to deal with attacks against them effectively. In this paper, in view of the characteristics of the current attack of Web application services, a Web security analysis model based on the kill chain is established, and the possible attacks against Web application services are analyzed in depth from the perspective of the kill chain. Then, the security of Web application services is evaluated in a quantitative manner. In this way, it can make up the defects of insufficient inspection by the existing security vulnerability model and the security specification of the tracking of Web application services, so as to realize the objective and scientific evaluation of the security state of Web application services.

Wide integration of information and communication technology (ICT) in modern power grids has brought many benefits as well as the risk of cyber attacks. A critical step towards defending grid cyber security is to understand the cyber-physical causal chain, which describes the progression of intrusion in cyber-space leading to the formation of consequences on the physical power grid. In this paper, we develop an attack vector for a time delay attack at load frequency control in the power grid. Distinct from existing works, which are separately focused on cyber intrusion, grid response, or testbed validation, the proposed attack vector for the first time provides a full cyber-physical causal chain. It targets specific vulnerabilities in the protocols, performs a denial-of-service (DoS) attack, induces the delays in control loop, and destabilizes grid frequency. The proposed attack vector is proved in theory, presented as an attack tree, and validated in an experimental environment. The results will provide valuable insights to develop security measures and robust controls against time delay attacks.

Multiple techniques for modeling cybersecurity attacks and defense have been developed. The use of tree- structures as well as techniques proposed by several firms (such as Lockheed Martin's Cyber Kill Chain, Microsoft's STRIDE and the MITRE ATT&CK frameworks) have all been demonstrated. These approaches model actions that can be taken to attack or stopped to secure infrastructure and other resources, at different levels of detail.This paper builds on prior work on using the Blackboard Architecture for cyberwarfare and proposes a generalized solution for modeling framework/paradigm-based attacks that go beyond the deployment of a single exploit against a single identified target. The Blackboard Architecture Cyber Command Entity attack Route (BACCER) identification system combines rules and facts that implement attack type determination and attack decision making logic with actions that implement reconnaissance techniques and attack and defense actions. BACCER's efficacy to model examples of tree-structures and other models is demonstrated herein.

With the increasing interdependence of critical infrastructures, the probability of a specific infrastructure to experience a complex cyber-physical attack is increasing. Thus it is important to analyze the risk of an attack and the dynamics of its propagation in order to design and deploy appropriate countermeasures. The attack trees, commonly adopted to this aim, have inherent shortcomings in representing interdependent, concurrent and sequential attacks. To overcome this, the work presented here proposes a stochastic methodology using Petri Nets and Continuous Time Markov Chain (CTMC) to analyze the attacks, considering the individual attack occurrence probabilities and their stochastic propagation times. A procedure to convert a basic attack tree into an equivalent CTMC is presented. The proposed method is applied in a case study to calculate the different attack propagation characteristics. The characteristics are namely, the probability of reaching the root node & sub attack nodes, the mean time to reach the root node and the mean time spent in the sub attack nodes before reaching the root node. Additionally, the method quantifies the effectiveness of specific defenses in reducing the attack risk considering the efficiency of individual defenses.

"Moving fast, and breaking things", instead of "being safe and secure", is the credo of the IT industry. However, if we look at the wide societal impact of IT security incidents in the past years, it seems like it is no longer sustainable. Just like in the case of Equifax, people simply forget updates, just like in the case of Maersk, companies do not use sufficient network segmentation. Security certification does not seem to help with this issue. After all, Equifax was IS027001 compliant.In this paper, we take a look at how we handle and (do not) learn from security incidents in IT security. We do this by comparing IT security incidents to early and later aviation safety. We find interesting parallels to early aviation safety, and outline the governance levers that could make the world of IT more secure, which were already successful in making flying the most secure way of transportation.

Modern JavaScript applications extensively depend on third-party libraries. Especially for the Node.js platform, vulnerabilities can have severe consequences to the security of applications, resulting in, e.g., cross-site scripting and command injection attacks. Existing static analysis tools that have been developed to automatically detect such issues are either too coarse-grained, looking only at package dependency structure while ignoring dataflow, or rely on manually written taint specifications for the most popular libraries to ensure analysis scalability. In this work, we propose a technique for automatically extracting taint specifications for JavaScript libraries, based on a dynamic analysis that leverages the existing test suites of the libraries and their available clients in the npm repository. Due to the dynamic nature of JavaScript, mapping observations from dynamic analysis to taint specifications that fit into a static analysis is non-trivial. Our main insight is that this challenge can be addressed by a combination of an access path mechanism that identifies entry and exit points, and the use of membranes around the libraries of interest. We show that our approach is effective at inferring useful taint specifications at scale. Our prototype tool automatically extracts 146 additional taint sinks and 7 840 propagation summaries spanning 1 393 npm modules. By integrating the extracted specifications into a commercial, state-of-the-art static analysis, 136 new alerts are produced, many of which correspond to likely security vulnerabilities. Moreover, many important specifications that were originally manually written are among the ones that our tool can now extract automatically.

A time-delay switch (TDS) cyber attack is a deliberate attempt by malicious adversaries aiming at destabilizing a power system by impeding the communication signals to/from the centralized controller from/to the network sensors and generating stations that participate in the load frequency control (LFC). A TDS cyber attack can be targeting the sensing loops (transmitting network measurements to the centralized controller) or the control signals dispatched from the centralized controller to the governor valves of the generating stations. A resilient TDS control strategy is proposed and developed in this work that thwarts network instabilities that are caused by delays in the sensing loops, and control commands, and guarantees normal operation of the LFC mechanism. This will be achieved by augmenting the telemetered control commands with locally generated feedback control laws (i.e., “decentralized” control commands) taking measurements that are available and accessible at the power generating stations (locally) independent from all the telemetered signals to/from the centralized controller. Our objective is to devise a controller that is capable of circumventing all types of TDS and DoS (Denial of Service) cyber attacks as well as a broad class of False Data Injection (FDI) cyber attacks.

Recent studies on attacks exploiting processor hardware vulnerabilities have raised significant concern for information security. Particularly, transient execution attacks such as Spectre augment microarchitectural side channels with speculative executions that lead to exfiltration of secretive data not intended to be accessed. Many prior works have demonstrated the manipulation of branch predictors for triggering speculative executions, and thereafter leaking sensitive information through processor microarchitectural components. In this paper, we present a new class of microarchitectural attack, called BranchSpec, that performs information leakage by exploiting state changes of branch predictors in speculative path. Our key observation is that, branch instruction executions in speculative path alter the states of branch pattern history, which are not restored even after the speculatively executed branches are eventually squashed. Unfortunately, this enables adversaries to harness branch predictors as the transmitting medium in transient execution attacks. More importantly, as compared to existing speculative attacks (e.g., Spectre), BranchSpec can take advantage of much simpler code patterns in victim's code base, making the impact of such exploitation potentially even more severe. To demonstrate this security vulnerability, we have implemented two variants of BranchSpec attacks: a side channel where a malicious spy process infers cross-boundary secrets via victim's speculatively executed nested branches, and a covert channel that communicates secrets through intentionally perturbing the branch pattern history structure via speculative branch executions. Our evaluation on Intel Skylake- and Coffee Lake-based processors reveals that these information leakage attacks are highly accurate and successful. To the best of our knowledge, this is the first work to reveal the information leakage threat due to speculative state update in branch predictor. Our studies further broaden the attack surface of processor microarchitecture, and highlight the needs for branch prediction mechanisms that are secure in transient executions.

We observe that operations performed by standard compilers harm fuzzing because the optimizations and the Intermediate Representation (IR) lead to transformations that improve execution speed at the expense of fuzzing. To remedy this problem, we propose `controlled compilation', a set of techniques to automatically re-factor a program's source code and cherry pick beneficial compiler optimizations to improve fuzzing. We design, implement and evaluate controlled compilation by building a new toolchain with Clang/LLVM. We perform an evaluation on 10 open source projects and compare the results of AFL to state-of-the-art grey-box fuzzers and concolic fuzzers. We show that when programs are compiled with this new toolchain, AFL covers 30 % new code on average and finds 21 additional bugs in real world programs. Our study reveals that controlled compilation often covers more code and finds more bugs than state-of-the-art fuzzing techniques, without the need to write a fuzzer from scratch or resort to advanced techniques. We identify two main reasons to explain why. First, it has proven difficult for researchers to appropriately configure existing fuzzers such as AFL. To address this problem, we provide guidelines and new LLVM passes to help automate AFL's configuration. This will enable researchers to perform a fairer comparison with AFL. Second, we find that current coverage-based evaluation measures (e.g. the total number of visited lines, edges or BBs) are inadequate because they lose valuable information such as which parts of a program a fuzzer actually visits and how consistently it does so. Coverage is considered a useful metric to evaluate a fuzzer's performance and devise a fuzzing strategy. However, the lack of a standard methodology for evaluating coverage remains a problem. To address this, we propose a rigorous evaluation methodology based on `qualitative coverage'. Qualitative coverage uniquely identifies each program line to help understand which lines are commonly visited by different fuzzers vs. which lines are visited only by a particular fuzzer. Throughout our study, we show the benefits of this new evaluation methodology. For example we provide valuable insights into the consistency of fuzzers, i.e. their ability to cover the same code or find the same bug across multiple independent runs. Overall, our evaluation methodology based on qualitative coverage helps to understand if a fuzzer performs better, worse, or is complementary to another fuzzer. This helps security practitioners adjust their fuzzing strategies.

Language-based information-flow control (IFC) techniques often rely on special purpose, ad-hoc primitives to address different covert channels that originate in the runtime system, beyond the scope of language constructs. Since these piecemeal solutions may not compose securely, there is a need for a unified mechanism to control covert channels. As a first step towards this goal, we argue for the design of a general interface that allows programs to safely interact with the runtime system and the available computing resources. To coordinate the communication between programs and the runtime system, we propose the use of asynchronous exceptions (interrupts), which, to the best of our knowledge, have not been considered before in the context of IFC languages. Since asynchronous exceptions can be raised at any point during execution-often due to the occurrence of an external event-threads must temporarily mask them out when manipulating locks and shared data structures to avoid deadlocks and, therefore, breaking program invariants. Crucially, the naive combination of asynchronous exceptions with existing features of IFC languages (e.g., concurrency and synchronization variables) may open up new possibilities of information leakage. In this paper, we present MACasync, a concurrent, statically enforced IFC language that, as a novelty, features asynchronous exceptions. We show how asynchronous exceptions easily enable (out of the box) useful programming patterns like speculative execution and some degree of resource management. We prove that programs in MACasync satisfy progress-sensitive non-interference and mechanize our formal claims in the Agda proof assistant.

A wide range of tools exist to assist developers in creating secure software. Many of these tools, such as static analysis engines or security checkers included in compilers, use warnings to communicate security issues to developers. The effectiveness of these tools relies on developers heeding these warnings, and there are many ways in which these warnings could be displayed. Johnson et al. [46] conducted qualitative research and found that warning presentation and integration are main issues. We built on Johnson et al.'s work and examined what developers want from security warnings, including what form they should take and how they should integrate into their workflow and work context. To this end, we conducted a Grounded Theory study with 14 professional software developers and 12 computer science students as well as a focus group with 7 academic researchers to gather qualitative insights. To back up the theory developed from the qualitative research, we ran a quantitative survey with 50 professional software developers. Our results show that there is significant heterogeneity amongst developers and that no one warning type is preferred over all others. The context in which the warnings are shown is also highly relevant, indicating that it is likely to be beneficial if IDEs and other development tools become more flexible in their warning interactions with developers. Based on our findings, we provide concrete recommendations for both future research as well as how IDEs and other security tools can improve their interaction with developers.

A common technique for building ICT systems is to build them as successive layers of bstraction: for instance, the Instruction Set Architecture (ISA) is an abstraction of the hardware, and compilers or interpreters build higher level abstractions on top of the ISA.The functionality of an ICT application can often be understood by considering only a single level of abstraction. For instance the source code of the application defines the functionality using the level of abstraction of the source programming language. Functionality can be well understood by just studying this source code.Many important security issues in ICT system however are cross-layer issues: they can not be understood by considering the system at a single level of abstraction, but they require understanding how multiple levels of abstraction are implemented. Attacks may rely on, or exploit, implementation details of one or more layers below the source code level of abstraction.The purpose of this paper is to illustrate this cross-layer nature of security by discussing old and new examples of cross-layer security issues, and by providing a classification of these issues.

Ethereum, one of the most popular blockchain platforms, provides financial transactions like payments and auctions through smart contracts. Due to the immense interest in smart contracts in academia, the research community of smart contract security has made a significant improvement recently. Researchers have reported various security vulnerabilities in smart contracts, and developed static analysis tools and verification frameworks to detect them. However, it is unclear whether such great efforts from academia has indeed enhanced the security of smart contracts in reality. To understand the security level of smart contracts in the wild, we empirically studied 55,046 real-world Ethereum smart contracts written in Solidity, the most popular programming language used by Ethereum smart contract developers. We first examined how many well-known vulnerabilities the Solidity compiler has patched, and how frequently the Solidity team publishes compiler releases. Unfortunately, we observed that many known vulnerabilities are not yet patched, and some patches are not even sufficient to avoid their target vulnerabilities. Subsequently, we investigated whether smart contract developers use the most recent compiler with vulnerabilities patched. We reported that developers of more than 98% of real-world Solidity contracts still use older compilers without vulnerability patches, and more than 25% of the contracts are potentially vulnerable due to the missing security patches. To understand actual impacts of the missing patches, we manually investigated potentially vulnerable contracts that are detected by our static analyzer and identified common mistakes by Solidity developers, which may cause serious security issues such as financial loss. We detected hundreds of vulnerable contracts and about one fourth of the vulnerable contracts are used by thousands of people. We recommend the Solidity team to make patches that resolve known vulnerabilities correctly, and developers to use the latest Solidity compiler to avoid missing security patches.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

The paper proposes a novel technique of EEG induced Brain-Computer Interface system for user authentication of personal devices. The scheme enables a human user to lock and unlock any personal device using his/her mind generated password. A two stage security verification is employed in the scheme. In the first stage, a 3 × 3 spatial matrix of flickering circles will appear on the screen of which, rows are blinked randomly and user has to mentally select a row which contains his desired circle.P300 is released when the desired row is blinked. Successful selection of row is followed by the selection of a flickering circle in the desired row. Gazing at a particular flickering circle generates SSVEP brain pattern which is decoded to trace the mentally selected circle. User is able to store mentally uttered number in the selected circle, later the number with it's spatial position will serve as the password for the unlocking phase. Here, the user is equipped with a headphone where numbers starting from zero to nine are spelled randomly. Spelled number matching with the mentally uttered number generates auditory P300 in the subject's brain. The particular choice of mentally uttered number is detected by successful detection of auditory P300. A novel weight update algorithm of Recurrent Neural Network (RNN), based on Extended-Kalman Filter and Particle Filter is used here for classifying the brain pattern. The proposed classifier achieves the best classification accuracy of 95.6%, 86.5% and 83.5% for SSVEP, visual P300 and auditory P300 respectively.

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.

Network Intrusion Detection System (NIDS) can help administrators of a server in detecting attacks by analyzing packet data traffic on the network in real-time. If an attack occurs, an alert to the administrator is provided by NIDS so that the attack can be known and responded immediately. On the other hand, the alerts cannot be monitored by administrators all the time. Therefore, a system that automatically sends notifications to administrators in real-time by utilizing social media platforms is needed. This paper provides an analysis of the notification system built using Snort as NIDS with WhatsApp and Telegram as a notification platform. There are three types of attacks that are simulated and must be detected by Snort, which are Ping of Death attacks, SYN flood attacks, and SSH brute force attacks. The results obtained indicate that the system successfully provided notification in the form of attack time, IP source of the attack, source of attack port and type of attack in real-time.

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

The facial recognition time by time takes more importance, due to the extend kind of applications it has, but it is still challenging when faces big variations in the characteristics of the biometric data used in the process and especially referring to the transportation of information through the internet in the internet of things context. Based on the systematic review and rigorous study that supports the extraction of the most relevant information on this topic [1], a software architecture proposal which contains basic security requirements necessary for the treatment of the data involved in the application of facial recognition techniques, oriented to an IoT environment was generated. Concluding that the security and privacy considerations of the information registered in IoT devices represent a challenge and it is a priority to be able to guarantee that the data circulating on the network are only accessible to the user that was designed for this.

The increase of cyber attacks in both the numbers and varieties in recent years demands to build a more sophisticated network intrusion detection system (NIDS). These NIDS perform better when they can monitor all the traffic traversing through the network like when being deployed on a Software-Defined Network (SDN). Because of the inability to detect zero-day attacks, signature-based NIDS which were traditionally used for detecting malicious traffic are beginning to get replaced by anomaly-based NIDS built on neural networks. However, recently it has been shown that such NIDS have their own drawback namely being vulnerable to the adversarial example attack. Moreover, they were mostly evaluated on the old datasets which don't represent the variety of attacks network systems might face these days. In this paper, we present Reconstruction from Partial Observation (RePO) as a new mechanism to build an NIDS with the help of denoising autoencoders capable of detecting different types of network attacks in a low false alert setting with an enhanced robustness against adversarial example attack. Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.

In recent years, attacks against cyber-physical systems have become increasingly frequent and widespread. The inventiveness of such attacks increases significantly. In particular, zero-day attacks are widely used. The rapid development of the industrial Internet of things, the expansion of the application areas of service robots, the advent of the Internet of vehicles and the Internet of military things have led to a significant increase of attention to deceptive attacks. Especially great threat is posed by deceptive attacks that do not use hiding malicious components. Such attacks can naturally be used against robotic systems. In this paper, we consider an approach to the development of an intrusion detection system for closed-loop robotic systems. The system is based on an abnormal behavioral pattern detection technique. The system can be used for detection of zero-day deceptive attacks. We provide an experimental comparison of our approach and other behavior-based intrusion detection systems.

Zero-day Web attacks are arguably the most serious threats to Web security, but are very challenging to detect because they are not seen or known previously and thus cannot be detected by widely-deployed signature-based Web Application Firewalls (WAFs). This paper proposes ZeroWall, an unsupervised approach, which works with an existing WAF in pipeline, to effectively detecting zero-day Web attacks. Using historical Web requests allowed by an existing signature-based WAF, a vast majority of which are assumed to be benign, ZeroWall trains a self-translation machine using an encoder-decoder recurrent neural network to capture the syntax and semantic patterns of benign requests. In real-time detection, a zero-day attack request (which the WAF fails to detect), not understood well by self-translation machine, cannot be translated back to its original request by the machine, thus is declared as an attack. In our evaluation using 8 real-world traces of 1.4 billion Web requests, ZeroWall successfully detects real zero-day attacks missed by existing WAFs and achieves high F1-scores over 0.98, which significantly outperforms all baseline approaches.

Windows is one of the popular operating systems in use today, while Universal Serial Bus (USB) is one of the mechanisms used by many people with practical plug and play functions. USB has long been used as a vector of attacks on computers. One method of attack is Keylogger. The Keylogger can take advantage of existing vulnerabilities in the Windows 10 operating system attacks carried out in the form of recording computer keystroke activity without the victim knowing. In this research, an attack will be carried out by running a Powershell Script using BadUSB to be able to activate the Keylogger program. The script is embedded in the Arduino Pro Micro device. The results obtained in the Keyboard Injection Attack research using Arduino Pro Micro were successfully carried out with an average time needed to run the keylogger is 7.474 seconds with a computer connected to the internet. The results of the keylogger will be sent to the attacker via email.

In the paper, an intrusion detection system to safeguard computer software is proposed. The detection is based on negative selection algorithm, inspired by the human immunity mechanism. It is composed of two stages, generation of receptors and anomaly detection. Experimental results of the proposed system are presented, analyzed, and concluded.