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2023-02-03
Samuel, Henry D, Kumar, M Santhanam, Aishwarya, R., Mathivanan, G..  2022.  Automation Detection of Malware and Stenographical Content using Machine Learning. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). :889–894.
In recent times, the occurrence of malware attacks are increasing at an unprecedented rate. Particularly, the image-based malware attacks are spreading worldwide and many people get harmful malware-based images through the technique called steganography. In the existing system, only open malware and files from the internet can be identified. However, the image-based malware cannot be identified and detected. As a result, so many phishers make use of this technique and exploit the target. Social media platforms would be totally harmful to the users. To avoid these difficulties, Machine learning can be implemented to find the steganographic malware images (contents). The proposed methodology performs an automatic detection of malware and steganographic content by using Machine Learning. Steganography is used to hide messages from apparently innocuous media (e.g., images), and steganalysis is the approach used for detecting this malware. This research work proposes a machine learning (ML) approach to perform steganalysis. In the existing system, only open malware and files from the internet are identified but in the recent times many people get harmful malware-based images through the technique called steganography. Social media platforms would be totally harmful to the users. To avoid these difficulties, the proposed Machine learning has been developed to appropriately detect the steganographic malware images (contents). Father, the steganalysis method using machine learning has been developed for performing logistic classification. By using this, the users can avoid sharing the malware images in social media platforms like WhatsApp, Facebook without downloading it. It can be also used in all the photo-sharing sites such as google photos.
2023-02-02
El Mouhib, Manal, Azghiou, Kamal, Benali, Abdelhamid.  2022.  Connected and Autonomous Vehicles against a Malware Spread : A Stochastic Modeling Approach. 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). :1–6.
The proliferation of autonomous and connected vehicles on our roads is increasingly felt. However, the problems related to the optimization of the energy consumed, to the safety, and to the security of these do not cease to arise on the tables of debates bringing together the various stakeholders. By focusing on the security aspect of such systems, we can realize that there is a family of problems that must be investigated as soon as possible. In particular, those that may manifest as the system expands. Therefore, this work aims to model and simulate the behavior of a system of autonomous and connected vehicles in the face of a malware invasion. In order to achieve the set objective, we propose a model to our system which is inspired by those used in epidimology, such as SI, SIR, SIER, etc. This being adapted to our case study, stochastic processes are defined in order to characterize its dynamics. After having fixed the values of the various parameters, as well as those of the initial conditions, we run 100 simulations of our system. After which we visualize the results got, we analyze them, and we give some interpretations. We end by outlining the lessons and recommendations drawn from the results.
Samhi, Jordan, Gao, Jun, Daoudi, Nadia, Graux, Pierre, Hoyez, Henri, Sun, Xiaoyu, Allix, Kevin, Bissyandè, Tegawende F., Klein, Jacques.  2022.  JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis. 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE). :1232–1244.
Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-the-art static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JUCIFY approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JUCIFY builds on the Soot internal intermediate representation. We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are “unreachable” in apps' callgraphs, both in goodware and malware. Using JUCIFY, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JUCIFY'S model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JUCIFY we can find sensitive data leaks that pass through native code.
Aggarwal, Naman, Aggarwal, Pradyuman, Gupta, Rahul.  2022.  Static Malware Analysis using PE Header files API. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). :159–162.
In today’s fast pacing world, cybercrimes have time and again proved to be one of the biggest hindrances in national development. According to recent trends, most of the times the victim’s data is breached by trapping it in a phishing attack. Security and privacy of user’s data has become a matter of tremendous concern. In order to address this problem and to protect the naive user’s data, a tool which may help to identify whether a window executable is malicious or not by doing static analysis on it has been proposed. As well as a comparative study has been performed by implementing different classification models like Logistic Regression, Neural Network, SVM. The static analysis approach used takes into parameters of the executables, analysis of properties obtained from PE Section Headers i.e. API calls. Comparing different model will provide the best model to be used for static malware analysis
2023-01-20
Shi, Zhixin, Wang, Xiangyu, Liu, Pengcheng.  2022.  NBP-MS: Malware Signature Generation Based on Network Behavior Profiling. 2022 26th International Conference on Pattern Recognition (ICPR). :1865–1870.
With the proliferation of malware, the detection and classification of malware have been hot topics in the academic and industrial circles of cyber security, and the generation of malware signatures is one of the important research directions. In this paper, we propose NBP-MS, a method of signature generation that is based on network traffic generated by malware. Specifically, we utilize the network traffic generated by malware to perform fine-grained profiling of its network behaviors first, and then cluster all the profiles to generate network behavior signatures to classify malware, providing support for subsequent analysis and defense.
Fujii, Shota, Kawaguchi, Nobutaka, Kojima, Shoya, Suzuki, Tomoya, Yamauchi, Toshihiro.  2022.  Design and Implementation of System for URL Signature Construction and Impact Assessment. 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). :95–100.
The attacker’s server plays an important role in sending attack orders and receiving stolen information, particularly in the more recent cyberattacks. Under these circumstances, it is important to use network-based signatures to block malicious communications in order to reduce the damage. However, in addition to blocking malicious communications, signatures are also required not to block benign communications during normal business operations. Therefore, the generation of signatures requires a high level of understanding of the business, and highly depends on individual skills. In addition, in actual operation, it is necessary to test whether the generated signatures do not interfere with benign communications, which results in high operational costs. In this paper, we propose SIGMA, a system that automatically generates signatures to block malicious communication without interfering with benign communication and then automatically evaluates the impact of the signatures. SIGMA automatically extracts the common parts of malware communication destinations by clustering them and generates multiple candidate signatures. After that, SIGMA automatically calculates the impact on normal communication based on business logs, etc., and presents the final signature to the analyst, which has the highest blockability of malicious communication and non-blockability of normal communication. Our objectives with this system are to reduce the human factor in generating the signatures, reduce the cost of the impact evaluation, and support the decision of whether to apply the signatures. In the preliminary evaluation, we showed that SIGMA can automatically generate a set of signatures that detect 100% of suspicious URLs with an over-detection rate of just 0.87%, using the results of 14,238 malware analyses and actual business logs. This result suggests that the cost for generation of signatures and the evaluation of their impact on business operations can be suppressed, which used to be a time-consuming and human-intensive process.
2023-01-06
Guri, Mordechai.  2022.  ETHERLED: Sending Covert Morse Signals from Air-Gapped Devices via Network Card (NIC) LEDs. 2022 IEEE International Conference on Cyber Security and Resilience (CSR). :163—170.
Highly secure devices are often isolated from the Internet or other public networks due to the confidential information they process. This level of isolation is referred to as an ’air-gap .’In this paper, we present a new technique named ETHERLED, allowing attackers to leak data from air-gapped networked devices such as PCs, printers, network cameras, embedded controllers, and servers. Networked devices have an integrated network interface controller (NIC) that includes status and activity indicator LEDs. We show that malware installed on the device can control the status LEDs by blinking and alternating colors, using documented methods or undocumented firmware commands. Information can be encoded via simple encoding such as Morse code and modulated over these optical signals. An attacker can intercept and decode these signals from tens to hundreds of meters away. We show an evaluation and discuss defensive and preventive countermeasures for this exfiltration attack.
Khalid, Saneeha, Hussain, Faisal Bashir.  2022.  Evaluating Opcodes for Detection of Obfuscated Android Malware. 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :044—049.
Obfuscation refers to changing the structure of code in a way that original semantics can be hidden. These techniques are often used by application developers for code hardening but it has been found that obfuscation techniques are widely used by malware developers in order to hide the work flow and semantics of malicious code. Class Encryption, Code Re-Ordering, Junk Code insertion and Control Flow modifications are Code Obfuscation techniques. In these techniques, code of the application is changed. These techniques change the signature of the application and also affect the systems that use sequence of instructions in order to detect maliciousness of an application. In this paper an ’Opcode sequence’ based detection system is designed and tested against obfuscated samples. It has been found that the system works efficiently for the detection of non obfuscated samples but the performance is effected significantly against obfuscated samples. The study tests different code obfuscation schemes and reports the effect of each on sequential opcode based analytic system.
2023-01-05
Sewak, Mohit, Sahay, Sanjay K., Rathore, Hemant.  2022.  X-Swarm: Adversarial DRL for Metamorphic Malware Swarm Generation. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops). :169–174.
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.
2022-12-23
Rodríguez, Elsa, Fukkink, Max, Parkin, Simon, van Eeten, Michel, Gañán, Carlos.  2022.  Difficult for Thee, But Not for Me: Measuring the Difficulty and User Experience of Remediating Persistent IoT Malware. 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P). :392–409.
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.
Duby, Adam, Taylor, Teryl, Bloom, Gedare, Zhuang, Yanyan.  2022.  Detecting and Classifying Self-Deleting Windows Malware Using Prefetch Files. 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC). :0745–0751.
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.
Huo, Da, Li, Xiaoyong, Li, Linghui, Gao, Yali, Li, Ximing, Yuan, Jie.  2022.  The Application of 1D-CNN in Microsoft Malware Detection. 2022 7th International Conference on Big Data Analytics (ICBDA). :181–187.
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%.
2022-12-01
Fujita, Koji, Shibahara, Toshiki, Chiba, Daiki, Akiyama, Mitsuaki, Uchida, Masato.  2022.  Objection!: Identifying Misclassified Malicious Activities with XAI. ICC 2022 - IEEE International Conference on Communications. :2065—2070.
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.
Dave, Avani, Banerjee, Nilanjan, Patel, Chintan.  2021.  CARE: Lightweight Attack Resilient Secure Boot Architecture with Onboard Recovery for RISC-V based SOC. 2021 22nd International Symposium on Quality Electronic Design (ISQED). :516–521.
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.
Kao, Chia-Nan, Chang, Yung-Cheng, Huang, Nen-Fu, Salim S, I, Liao, I.-Ju, Liu, Rong-Tai, Hung, Hsien-Wei.  2015.  A predictive zero-day network defense using long-term port-scan recording. 2015 IEEE Conference on Communications and Network Security (CNS). :695—696.
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%.
2022-11-25
Shipunov, Ilya S., Nyrkov, Anatoliy P., Ryabenkov, Maksim U., Morozova, Elena V., Goloskokov, Konstantin P..  2021.  Investigation of Computer Incidents as an Important Component in the Security of Maritime Transportation. 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). :657—660.
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.
2022-11-18
Almuhtadi, Wahab, Bahri, Surbhi, Fenwick, Wynn, Henderson, Liam, Henley-Vachon, Liam, Mukasa, Joshua.  2021.  Malware Detection and Security Analysis Capabilities in a Continuous Integration / Delivery Context Using Assemblyline. 2021 IEEE International Conference on Consumer Electronics (ICCE). :1—5.
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.
2022-10-20
Chen, Wenhao, Lin, Li, Newman, Jennifer, Guan, Yong.  2021.  Automatic Detection of Android Steganography Apps via Symbolic Execution and Tree Matching. 2021 IEEE Conference on Communications and Network Security (CNS). :254—262.
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.
Barr-Smith, Frederick, Ugarte-Pedrero, Xabier, Graziano, Mariano, Spolaor, Riccardo, Martinovic, Ivan.  2021.  Survivalism: Systematic Analysis of Windows Malware Living-Off-The-Land. 2021 IEEE Symposium on Security and Privacy (SP). :1557—1574.
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.
2022-09-20
Rajput, Prashant Hari Narayan, Sarkar, Esha, Tychalas, Dimitrios, Maniatakos, Michail.  2021.  Remote Non-Intrusive Malware Detection for PLCs based on Chain of Trust Rooted in Hardware. 2021 IEEE European Symposium on Security and Privacy (EuroS&P). :369—384.
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.
Wood, Adrian, Johnstone, Michael N..  2021.  Detection of Induced False Negatives in Malware Samples. 2021 18th International Conference on Privacy, Security and Trust (PST). :1—6.
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.
2022-09-16
Sutton, Sara, Siasi, Nazli.  2021.  Decoy VNF for Enhanced Security in Fog Computing. 2021 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). :75—81.
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.
2022-08-26
Zhao, Junyi, Tang, Tao, Bu, Bing, Li, Qichang.  2021.  A Three-dimension Resilience State Space-based Approach to Resilience Assessment of CBTC system. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). :3673—3678.
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.
2022-08-12
Al Khayer, Aala, Almomani, Iman, Elkawlak, Khaled.  2020.  ASAF: Android Static Analysis Framework. 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH). :197–202.
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.
Stiévenart, Quentin, Roover, Coen De.  2020.  Compositional Information Flow Analysis for WebAssembly Programs. 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM). :13–24.
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.