Biblio

Advanced metamorphic malware and ransomware use techniques like obfuscation to alter their internal structure with every attack. Therefore, any signature extracted from such attack, and used to bolster endpoint defense, cannot avert subsequent attacks. Therefore, if even a single such malware intrudes even a single device of an IoT network, it will continue to infect the entire network. Scenarios where an entire network is targeted by a coordinated swarm of such malware is not beyond imagination. Therefore, the IoT era also requires Industry-4.0 grade AI-based solutions against such advanced attacks. But AI-based solutions need a large repository of data extracted from similar attacks to learn robust representations. Whereas, developing a metamorphic malware is a very complex task and requires extreme human ingenuity. Hence, there does not exist abundant metamorphic malware to train AI-based defensive solutions. Also, there is currently no system that could generate enough functionality preserving metamorphic variants of multiple malware to train AI-based defensive systems. Therefore, to this end, we design and develop a novel system, named X-Swarm. X-Swarm uses deep policy-based adversarial reinforcement learning to generate swarm of metamorphic instances of any malware by obfuscating them at the opcode level and ensuring that they could evade even capable, adversarial-attack immune endpoint defense systems.

Consumer IoT devices may suffer malware attacks, and be recruited into botnets or worse. There is evidence that generic advice to device owners to address IoT malware can be successful, but this does not account for emerging forms of persistent IoT malware. Less is known about persistent malware, which resides on persistent storage, requiring targeted manual effort to remove it. This paper presents a field study on the removal of persistent IoT malware by consumers. We partnered with an ISP to contrast remediation times of 760 customers across three malware categories: Windows malware, non-persistent IoT malware, and persistent IoT malware. We also contacted ISP customers identified as having persistent IoT malware on their network-attached storage devices, specifically QSnatch. We found that persistent IoT malware exhibits a mean infection duration many times higher than Windows or Mirai malware; QSnatch has a survival probability of 30% after 180 days, whereby most if not all other observed malware types have been removed. For interviewed device users, QSnatch infections lasted longer, so are apparently more difficult to get rid of, yet participants did not report experiencing difficulty in following notification instructions. We see two factors driving this paradoxical finding: First, most users reported having high technical competency. Also, we found evidence of planning behavior for these tasks and the need for multiple notifications. Our findings demonstrate the critical nature of interventions from outside for persistent malware, since automatic scan of an AV tool or a power cycle, like we are used to for Windows malware and Mirai infections, will not solve persistent IoT malware infections.

Malware detection and analysis can be a burdensome task for incident responders. As such, research has turned to machine learning to automate malware detection and malware family classification. Existing work extracts and engineers static and dynamic features from the malware sample to train classifiers. Despite promising results, such techniques assume that the analyst has access to the malware executable file. Self-deleting malware invalidates this assumption and requires analysts to find forensic evidence of malware execution for further analysis. In this paper, we present and evaluate an approach to detecting malware that executed on a Windows target and further classify the malware into its associated family to provide semantic insight. Specifically, we engineer features from the Windows prefetch file, a file system forensic artifact that archives process information. Results show that it is possible to detect the malicious artifact with 99% accuracy; furthermore, classifying the malware into a fine-grained family has comparable performance to techniques that require access to the original executable. We also provide a thorough security discussion of the proposed approach against adversarial diversity.

In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%.

Many studies have been conducted to detect various malicious activities in cyberspace using classifiers built by machine learning. However, it is natural for any classifier to make mistakes, and hence, human verification is necessary. One method to address this issue is eXplainable AI (XAI), which provides a reason for the classification result. However, when the number of classification results to be verified is large, it is not realistic to check the output of the XAI for all cases. In addition, it is sometimes difficult to interpret the output of XAI. In this study, we propose a machine learning model called classification verifier that verifies the classification results by using the output of XAI as a feature and raises objections when there is doubt about the reliability of the classification results. The results of experiments on malicious website detection and malware detection show that the proposed classification verifier can efficiently identify misclassified malicious activities.

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.

Zero-day attack is a critical network attack. The zero-day attack period (ZDAP) is the period from the release of malware/exploit until a patch becomes available. IDS/IPS cannot effectively block zero-day attacks because they use pattern-based signatures in general. This paper proposes a Prophetic Defender (PD) by which ZDAP can be minimized. Prior to actual attack, hackers scan networks to identify hosts with vulnerable ports. If this port scanning can be detected early, zero-day attacks will become detectable. PD architecture makes use of a honeypot-based pseudo server deployed to detect malicious port scans. A port-scanning honeypot was operated by us in 6 years from 2009 to 2015. By analyzing the 6-year port-scanning log data, we understand that PD is effective for detecting and blocking zero-day attacks. The block rate of the proposed architecture is 98.5%.

The risk of detecting incidents in the field of computer technology in Maritime transport is considered. The structure of the computer incident investigation system and its functions are given. The system of conducting investigations of computer incidents on sea transport is considered. A possible algorithm for investigating the incident using the tools of forensic science and an algorithm for transmitting the received data for further processing are presented.

Risk management is an essential part of software security. Assemblyline is a software security tool developed by the Canadian Centre for Cyber Security (CCCS) for malware detection and analysis. In this paper, we examined the performance of Assemblyline for assessing the risk of executable files. We developed and examined use-cases where Assemblyline is included as part of a security safety net assessing vulnerabilities that would lead to risk. Finally, we considered Assemblyline’s utility in a continuous integration / delivery context using our test results.

The recent focus of cyber security on automated detection of malware for Android apps has omitted the study of some apps used for “legitimate” purposes, such as steganography apps. Mobile steganography apps can be used for delivering harmful messages, and while current research on steganalysis targets the detection of stego images using academic algorithms and well-built benchmarking image data sets, the community has overlooked uncovering a mobile app itself for its ability to perform steganographic embedding. Developing automatic tools for identifying the code in a suspect app as a stego app can be very challenging: steganography algorithms can be represented in a variety of ways, and there exists many image editing algorithms which appear similar to steganography algorithms.This paper proposes the first automated approach to detect Android steganography apps. We use symbolic execution to summarize an app’s image operation behavior into expression trees, and match the extracted expression trees with reference trees that represents the expected behavior of a steganography embedding process. We use a structural feature based similarity measure to calculate the similarity between expression trees. Our experiments show that, the propose approach can detect real world Android stego apps that implement common spatial domain and frequency domain embedding algorithms with a high degree of accuracy. Furthermore, our procedure describes a general framework that has the potential to be applied to other similar questions when studying program behaviors.

As malware detection algorithms and methods become more sophisticated, malware authors adopt equally sophisticated evasion mechanisms to defeat them. Anecdotal evidence claims Living-Off-The-Land (LotL) techniques are one of the major evasion techniques used in many malware attacks. These techniques leverage binaries already present in the system to conduct malicious actions. We present the first large-scale systematic investigation of the use of these techniques by malware on Windows systems.In this paper, we analyse how common the use of these native system binaries is across several malware datasets, containing a total of 31,805,549 samples. We identify an average 9.41% prevalence. Our results show that the use of LotL techniques is prolific, particularly in Advanced Persistent Threat (APT) malware samples where the prevalence is 26.26%, over twice that of commodity malware.To illustrate the evasive potential of LotL techniques, we test the usage of LotL techniques against several fully patched Windows systems in a local sandboxed environment and show that there is a generalised detection gap in 10 of the most popular anti-virus products.

Digitization has been rapidly integrated with manufacturing industries and critical infrastructure to increase efficiency, productivity, and reduce wastefulness, a transition being labeled as Industry 4.0. However, this expansion, coupled with the poor cybersecurity posture of these Industrial Internet of Things (IIoT) devices, has made them prolific targets for exploitation. Moreover, modern Programmable Logic Controllers (PLC) used in the Operational Technology (OT) sector are adopting open-source operating systems such as Linux instead of proprietary software, making such devices susceptible to Linux-based malware. Traditional malware detection approaches cannot be applied directly or extended to such environments due to the unique restrictions of these PLC devices, such as limited computational power and real-time requirements. In this paper, we propose ORRIS, a novel lightweight and out-of-the-device framework that detects malware at both kernel and user-level by processing the information collected using the Joint Test Action Group (JTAG) interface. We evaluate ORRIS against in-the-wild Linux malware achieving maximum detection accuracy of ≈99.7% with very few false-positive occurrences, a result comparable to the state-of-the-art commercial products. Moreover, we also develop and demonstrate a real-time implementation of ORRIS for commercial PLCs.

Malware detection is an important area of cyber security. Computer systems rely on malware detection applications to prevent malware attacks from succeeding. Malware detection is not a straightforward task, as new variants of malware are generated at an increasing rate. Machine learning (ML) has been utilised to generate predictive classification models to identify new malware variants which conventional malware detection methods may not detect. Machine learning, has however, been found to be vulnerable to different types of adversarial attacks, in which an attacker is able to negatively affect the classification ability of the ML model. Several defensive measures to prevent adversarial poisoning attacks have been developed, but they often rely on the use of a trusted clean dataset to help identify and remove adversarial examples from the training dataset. The defence in this paper does not require a trusted clean dataset, but instead, identifies intentional false negatives (zero day malware classified as benign) at the testing stage by examining the activation weights of the ML model. The defence was able to identify 94.07% of the successful targeted poisoning attacks.

Fog computing extends cloud resources to the edge of the network, thus enabling network providers to support real-time applications at low latencies. These applications further demand high security against malicious attacks that target distributed fog servers. One effective defense mechanism here against cyber attacks is the use of honeypots. The latter acts as a potential target for attackers by diverting malicious traffic away from the servers that are dedicated to legitimate users. However, one main limitation of honeypots is the lack of real traffic and network activities. Therefore, it is important to implement a solution that simulates the behavior of the real system to lure attackers without the risk of being exposed. Hence this paper proposes a practical approach to generate network traffic by introducing decoy virtual network functions (VNF) embedded on fog servers, which make the network traffic on honeypots resemble a legitimate, vulnerable fog system to attract cyber attackers. The use of virtualization allows for robust scalability and modification of network functions based on incoming attacks, without the need for dedicated hardware. Moreover, deep learning is leveraged here to build fingerprints for each real VNF, which is subsequently used to support its decoy counterpart against active probes. The proposed framework is evaluated based on CPU utilization, memory usage, disk input/output access, and network latency.

Traditional passive defense methods cannot resist the constantly updated and evolving cyber attacks. The concept of resilience is introducing to measure the ability of the system to maintain its function under attack. It matters in evaluating the security of modern industrial systems. This paper presents a 3D Resilience State Space method to assess Communication-based train control (CBTC) system resilience under malware attack. We model the spread of malware as two functions: the communicability function \$f\$(x) and the susceptibility function 9 (x). We describe the characteristics of these two function in the CBTC complex network by using the percolation theory. Then we use a perturbation formalism to analyze the impact of malware attack on information flow and use it as an indicator of the cyber layer state. The CBTC cyber-physical system resilience metric formalizes as the system state transitions in three-dimensional state space. The three dimensions respectively represent the cyber layer state, the physical layer state, and the transmission layer state. The simulation results reveal that the proposed framework can effectively assess the resilience of the CBTC system. And the anti-malware programs can prevent the spread of malware and improve CBTC system resilience.

Android Operating System becomes a major target for malicious attacks. Static analysis approach is widely used to detect malicious applications. Most of existing studies on static analysis frameworks are limited to certain features. This paper presents an Android Static Analysis Framework (ASAF) which models the overall static analysis phases and approaches for Android applications. ASAF can be implemented for different purposes including Android malicious apps detection. The proposed framework utilizes a parsing tool, Android Static Parse (ASParse) which is also introduced in this paper. Through the extendibility of the ASParse tool, future research studies can easily extend the parsed features and the parsed files to perform parsing based on their specific requirements and goals. Moreover, a case study is conducted to illustrate the implementation of the proposed ASAF.

WebAssembly is a new W3C standard, providing a portable target for compilation for various languages. All major browsers can run WebAssembly programs, and its use extends beyond the web: there is interest in compiling cross-platform desktop applications, server applications, IoT and embedded applications to WebAssembly because of the performance and security guarantees it aims to provide. Indeed, WebAssembly has been carefully designed with security in mind. In particular, WebAssembly applications are sandboxed from their host environment. However, recent works have brought to light several limitations that expose WebAssembly to traditional attack vectors. Visitors of websites using WebAssembly have been exposed to malicious code as a result. In this paper, we propose an automated static program analysis to address these security concerns. Our analysis is focused on information flow and is compositional. For every WebAssembly function, it first computes a summary that describes in a sound manner where the information from its parameters and the global program state can flow to. These summaries can then be applied during the subsequent analysis of function calls. Through a classical fixed-point formulation, one obtains an approximation of the information flow in the WebAssembly program. This results in the first compositional static analysis for WebAssembly. On a set of 34 benchmark programs spanning 196kLOC of WebAssembly, we compute at least 64% of the function summaries precisely in less than a minute in total.

Ransomware is one of the fastest growing threats to internet security. New Ransomware attacks happen around the globe, on a weekly basis. These attacks happen to individual users and groups, from almost any type of business. Many of these attacks involve Ransomware as a service, where one attacker creates a template Malware, which can be purchased and modified by other attackers to perform specific actions. The District Ransomware was a less well-known strain. This work focuses on statically and dynamically analyzing the District Ransomware and presenting the results.

Mobile antivirus technologies incorporate static analysis which involves the analysis of programs without its execution. This process relies on pattern matching against a signature repository to identify malware, which can be easily tricked by transformation techniques such as obfuscation. Obfuscation as an evasion technique renders character strings disguised and incomprehensive, to prevent tampering and reengineering, which poses to be a valuable technique malware developers adopt to evade detection. This paper attempts to study the detection efficiency of static analyzers against obfuscated Android malware. This study is the first step in a larger project attempting to improve the efficiency of malware detectors.

The Android application market will conduct various security analysis on each application to predict its potential harm before put it online. Since almost all the static analysis tools can only detect malicious behaviors in the Java layer, more and more malwares try to avoid static analysis by taking the malicious codes to the Native layer. To provide a solution for the above situation, there's a new research aspect proposed in this paper and defined as Inter-language Static Analysis. As all the involved technologies are introduced, the current research results of them will be captured in this paper, such as static analysis in Java layer, binary analysis in Native layer, Java-Native penetration technology, etc.

The software industry is dominating many are like health care, finance, agriculture and entertainment. Software security has become an essential issue-outsider libraries, which assume a significant part in programming. The finding weaknesses in the binary code is a significant issue that presently cannot seem to be handled, as showed by numerous weaknesses wrote about an everyday schedule. Software seller sells the software to the client if the client wants to check the software's vulnerability it is a cumbersome task. Presently many deep learning-based methods also introduced to find the security weakness in the binary code. This paper present the merits and demerits of binary code analysis used by a different method.

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.

There has been a significant rise in ransomware attacks over the last few years. Cyber attackers have made use of tried and true ransomware viruses to target the government, health care, and educational institutions. Ransomware variants can be purchased on the dark web by amateurs giving them the same attack tools used by professional cyber attackers without experience or skill. Traditional antivirus and antimalware products have improved, but they alone fall short when it comes to catching and stopping ransomware attacks. Employee training has become one of the most important aspects of being prepared for attempted cyberattacks. However, training alone only goes so far; human error is still the main entry point for malware and ransomware infections. In this paper, we propose a multi-layered defense approach to safeguard against ransomware. We have come to the startling realization that it is not a matter of “if” your organization will be hit with ransomware, but “when” your organization will be hit with ransomware. If an organization is not adequately prepared for an attack or how to respond to an attack, the effects can be costly and devastating. Our approach proposes having innovative antimalware software on the local machines, properly configured firewalls, active DNS/Web filtering, email security, backups, and staff training. With the implementation of this layered defense, the attempt can be caught and stopped at multiple points in the event of an attempted ransomware attack. If the attack were successful, the layered defense provides the option for recovery of affected data without paying a ransom.

With the developing technology, cyber threats are developing rapidly, and the motivations and targets of cyber attackers are changing. In order to combat these threats, cyber threat information that provides information about the threats and the characteristics of the attackers is needed. In addition, it is of great importance to cooperate with other stakeholders and share experiences so that more information about threat information can be obtained and necessary measures can be taken quickly. In this context, in this study, it is stated that the establishment of a cooperation mechanism in which cyber threat information is shared will contribute to the cyber security capacity of organizations. And using the Zack Information Gap analysis, the deficiency of organizations in sharing threat information were determined and suggestions were presented. In addition, there are cooperation mechanisms in the USA and the EU where cyber threat information is shared, and it has been evaluated that it would be beneficial to establish a similar mechanism in our country. Thus, it is evaluated that advanced or unpredictable cyber threats can be detected, the cyber security capacities of all stakeholders will increase and a safer cyber ecosystem will be created. In addition, it is possible to collect, store, distribute and share information about the analysis of cyber incidents and malware analysis, to improve existing cyber security products or to encourage new product development, by carrying out joint R&D studies among the stakeholders to ensure that domestic and national cyber security products can be developed. It is predicted that new analysis methods can be developed by using technologies such as artificial intelligence and machine learning.

Because of the rise of the Monroe coin, many JavaScript files with embedded malicious code are used to mine cryptocurrency using the computing power of the browser client. This kind of script does not have any obvious behaviors when it is running, so it is difficult for common users to witness them easily. This feature could lead the browser side cryptocurrency mining abused without the user’s permission. Traditional browser security strategies focus on information disclosure and malicious code execution, but not suitable for such scenes. Thus, we present a novel detection method named MineDetector using a machine learning algorithm and static features for automatically detecting browser-side cryptojacking scripts on the websites. MineDetector extracts five static feature groups available from the abstract syntax tree and text of codes and combines them using the machine learning method to build a powerful cryptojacking classifier. In the real experiment, MineDetector achieves the accuracy of 99.41% and the recall of 93.55% and has better performance in time comparing with present dynamic methods. We also made our work user-friendly by developing a browser extension that is click-to-run on the Chrome browser.