Biblio

The rapid development of network information technology, individual’s information networks security has become a very critical issue in our daily life. Therefore, it is necessary to study the malware propagation model system. In this paper, the traditional integer order malware propagation model system is extended to the field of fractional-order. Then we analyze the asymptotic stability of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is 0. Next, the asymptotic stability and bifurcation analysis of the fractional-order malware propagation model system when the equilibrium point is the origin and the time delay is not 0 are carried out. Moreover, we study the asymptotic stability of the fractional-order malware propagation model system with an interior equilibrium point. In the end, so as to verify our theoretical results, many numerical simulations are provided.

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such territory.This paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve.

Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively.

Malware attacks in the cyber world continue to increase despite the efforts of Malware analysts to combat this problem. Recently, Malware samples have been presented as binary sequences and assembly codes. However, most researchers focus only on the raw Malware sequence in their proposed solutions, ignoring that the assembly codes may contain important details that enable rapid Malware detection. In this work, we leveraged the capabilities of deep autoencoders to investigate the presence of feature disparities in the assembly and raw binary Malware samples. First, we treated the task as outliers to investigate whether the autoencoder would identify and justify features as samples from the same family. Second, we added noise to all samples and used Deep Autoencoder to reconstruct the original samples by denoising. Experiments with the Microsoft Malware dataset showed that the byte samples' features differed from the assembly code samples.

The rising use of smartphones each year is matched by the development of the smartphone's operating system, Android. Due to the immense popularity of the Android operating system, many unauthorized users (in this case, the attackers) wish to exploit this vulnerability to get sensitive data from every Android user. The flubot malware assault, which happened in 2021 and targeted Android devices practically globally, is one of the attacks on Android smartphones. It was known at the time that the flubot virus stole information, particularly from banking applications installed on the victim's device. To prevent this from happening again, we research the signature and behavior of flubot malware. In this study, a hybrid analysis will be conducted on three samples of flubot malware that are available on the open-source Hatching Triage platform. Using the Android Virtual Device (AVD) as the primary environment for malware installation, the analysis was conducted with the Android Debug Bridge (ADB) and Burpsuite as supporting tools for dynamic analysis. During the static analysis, the Mobile Security Framework (MobSF) and the Bytecode Viewer were used to examine the source code of the three malware samples. Analysis of the flubot virus revealed that it extracts or drops dex files on the victim's device, where the file is the primary malware. The Flubot virus will clone the messaging application or Short Message Service (SMS) on the default device. Additionally, we discovered a form of flubot malware that operates as a Domain Generation Algorithm (DGA) and communicates with its Command and Control (C&C) server.

The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application.

Malwares are designed to cause harm to the machine without the user's knowledge. Malwares belonging to different families infect the system in its own unique way causing damage which could be irreversible and hence there is a need to detect and analyse the malwares. Manual analysis of all types of malwares is not a practical approach due to the huge effort involved and hence Automated Malware Analysis is resorted to so that the burden on humans can be decreased and the process is made robust. A lot of Automated Malware Analysis tools are present right now both offline and online but the problem arises as to which tool to select while analysing a suspicious binary. A comparative analysis of three most widely used automated tools has been done with different malware class samples. These tools are Cuckoo Sandbox, Any. Run and Intezer Analyze. In order to check the efficacy of the tool in both online and offline analysis, Cuckoo Sandbox was configured for offline use, and Any. Run and Intezer Analyze were configured for online analysis. Individual tools analyse each malware sample and after analysis is completed, a comparative chart is prepared to determine which tool is good at finding registry changes, processes created, files created, network connections, etc by the malicious binary. The findings conclude that Intezer Analyze tool recognizes file changes better than others but otherwise Cuckoo Sandbox and Any. Run tools are better in determining other functionalities.

The static and dynamic malware analysis are used by industrialists and academics to understand malware capabilities and threat level. The antimalware industries calculate malware threat levels using different techniques which involve human involvement and a large number of resources and analysts. As malware complexity, velocity and volume increase, it becomes impossible to allocate so many resources. Due to this reason, it is projected that the number of malware apps will continue to rise, and that more devices will be targeted in order to commit various sorts of cybercrime. It is therefore necessary to develop techniques that can calculate the damage or threat posed by malware automatically as soon as it is identified. In this way, early warnings about zero-day (unknown) malware can assist in allocating resources for carrying out a close analysis of it as soon as it is identified. In this paper, a fuzzy modelling approach is described for calculating the potential risk of malicious programs through static malware analysis.

Any software that runs malicious payloads on victims’ computers is referred to as malware. It is an increasing threat that costs people, businesses, and organizations a lot of money. Attacks on security have developed significantly in recent years. Malware may infiltrate both offline and online media, like: chat, SMS, and spam (email, or social media), because it has a built-in defensive mechanism and may conceal itself from antivirus software or even corrupt it. As a result, there is an urgent need to detect and prevent malware before it damages critical assets around the world. In fact, there are lots of different techniques and tools used to combat versus malware. In this paper, the malware samples were analyzing in the Virtual Box environment using in-depth analysis based on reverse engineering using advanced static malware analysis techniques. The results Obtained from malware analysis which represent a set of valuable information, all anti-malware and anti-virus program companies need for in order to update their products.

This document addresses the issue of the actual security level of PDF documents. Two types of detection approaches are utilized to detect dangerous elements within malware: static analysis and dynamic analysis. Analyzing malware binaries to identify dangerous strings, as well as reverse-engineering is included in static analysis for t1he malware to disassemble it. On the other hand, dynamic analysis monitors malware activities by running them in a safe environment, such as a virtual machine. Each method has its own set of strengths and weaknesses, and it is usually best to employ both methods while analyzing malware. Malware detection could be simplified without sacrificing accuracy by reducing the number of malicious traits. This may allow the researcher to devote more time to analysis. Our worry is that there is no obvious need to identify malware with numerous functionalities when it isn't necessary. We will solve this problem by developing a system that will identify if the given file is infected with malware or not.

A reliable database of Indicators of Compromise (IoC’s) is a cornerstone of almost every malware detection system. Building the database and keeping it up-to-date is a lengthy and often manual process where each IoC should be manually reviewed and labeled by an analyst. In this paper, we focus on an automatic way of identifying IoC’s intended to save analysts’ time and scale to the volume of network data. We leverage relations of each IoC to other entities on the internet to build a heterogeneous graph. We formulate a classification task on this graph and apply graph neural networks (GNNs) in order to identify malicious domains. Our experiments show that the presented approach provides promising results on the task of identifying high-risk malware as well as legitimate domains classification.

The rapidly increasing malware threats must be coped with new effective malware detection methodologies. Current malware threats are not limited to daily personal transactions but dowelled deeply within large enterprises and organizations. This paper introduces a new methodology for detecting and discriminating malicious versus normal applications. In this paper, we employed Ant-colony optimization to generate two behavioural graphs that characterize the difference in the execution behavior between malware and normal applications. Our proposed approach relied on the API call sequence generated when an application is executed. We used the API calls as one of the most widely used malware dynamic analysis features. Our proposed method showed distinctive behavioral differences between malicious and non-malicious applications. Our experimental results showed a comparative performance compared to other machine learning methods. Therefore, we can employ our method as an efficient technique in capturing malicious applications.

This paper provides an in-depth analysis of Android malware that bypassed the strictest defenses of the Google Play application store and penetrated the official Android market between January 2016 and July 2021. We systematically identified 1,238 such malicious applications, grouped them into 134 families, and manually analyzed one application from 105 distinct families. During our manual analysis, we identified malicious payloads the applications execute, conditions guarding execution of the payloads, hiding techniques applications employ to evade detection by the user, and other implementation-level properties relevant for automated malware detection. As most applications in our dataset contain multiple payloads, each triggered via its own complex activation logic, we also contribute a graph-based representation showing activation paths for all application payloads in form of a control- and data-flow graph. Furthermore, we discuss the capabilities of existing malware detection tools, put them in context of the properties observed in the analyzed malware, and identify gaps and future research directions. We believe that our detailed analysis of the recent, evasive malware will be of interest to researchers and practitioners and will help further improve malware detection tools.

With the ever increasing threat of malware, extensive research effort has been put on applying Deep Learning for malware classification tasks. Graph Neural Networks (GNNs) that process malware as Control Flow Graphs (CFGs) have shown great promise for malware classification. However, these models are viewed as black-boxes, which makes it hard to validate and identify malicious patterns. To that end, we propose CFG-Explainer, a deep learning based model for interpreting GNN-oriented malware classification results. CFGExplainer identifies a subgraph of the malware CFG that contributes most towards classification and provides insight into importance of the nodes (i.e., basic blocks) within it. To the best of our knowledge, CFGExplainer is the first work that explains GNN-based mal-ware classification. We compared CFGExplainer against three explainers, namely GNNExplainer, SubgraphX and PGExplainer, and showed that CFGExplainer is able to identify top equisized subgraphs with higher classification accuracy than the other three models.

Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the classes for the training process in ML systems. An advanced malware classification system should classify the known classes correctly while sensitive to the unknown class. In this paper, we introduce a self-supervised pre-training approach for the OSR problem in malware classification. We propose two transformations for the function call graph (FCG) based malware representations to facilitate the pretext task. Also, we present a statistical thresholding approach to find the optimal threshold for the unknown class. Moreover, the experiment results indicate that our proposed pre-training process can improve different performances of different downstream loss functions for the OSR problem.

With the dramatic increase in malicious software, the sophistication and innovation of malware have increased over the years. In particular, the dynamic analysis based on the deep neural network has shown high accuracy in malware detection. However, most of the existing methods only employ the raw API sequence feature, which cannot accurately reflect the actual behavior of malicious programs in detail. The relationship between API calls is critical for detecting suspicious behavior. Therefore, this paper proposes a malware detection method based on the graph neural network. We first connect the API sequences executed by different processes to build a directed process graph. Then, we apply Bert to encode the API sequences of each process into node embedding, which facilitates the semantic execution information inside the processes. Finally, we employ GCN to mine the deep semantic information based on the directed process graph and node embedding. In addition to presenting the design, we have implemented and evaluated our method on 10,000 malware and 10,000 benign software datasets. The results show that the precision and recall of our detection model reach 97.84% and 97.83%, verifying the effectiveness of our proposed method.

The Internet of things (IoT) is proving to be a boon in granting internet access to regularly used objects and devices. Sensors, programs, and other innovations interact and trade information with different gadgets and frameworks over the web. Even in modern times, IoT gadgets experience the ill effects of primary security threats, which expose them to many dangers and malware, one among them being IoT botnets. Botnets carry out attacks by serving as a vector and this has become one of the significant dangers on the Internet. These vectors act against associations and carry out cybercrimes. They are used to produce spam, DDOS attacks, click frauds, and steal confidential data. IoT gadgets bring various challenges unlike the common malware on PCs and Android devices as IoT gadgets have heterogeneous processor architecture. Numerous researches use static or dynamic analysis for detection and classification of botnets on IoT gadgets. Most researchers haven't addressed the multi-architecture issue and they use a lot of computing resources for analyzing. Therefore, this approach attempts to classify botnets in IoT by using PSI-Graphs which effectively addresses the problem of encryption in IoT botnet detection, tackles the multi-architecture problem, and reduces computation time. It proposes another methodology for describing and recognizing botnets utilizing graph-based Machine Learning techniques and Exploratory Data Analysis to analyze the data and identify how separable the data is to recognize bots at an earlier stage so that IoT devices can be prevented from being attacked.

Malicious cybersecurity activities have become increasingly worrisome for individuals and companies alike. While machine learning methods like Graph Neural Networks (GNNs) have proven successful on the malware detection task, their output is often difficult to understand. Explainable malware detection methods are needed to automatically identify malicious programs and present results to malware analysts in a way that is human interpretable. In this survey, we outline a number of GNN explainability methods and compare their performance on a real-world malware detection dataset. Specifically, we formulated the detection problem as a graph classification problem on the malware Control Flow Graphs (CFGs). We find that gradient-based methods outperform perturbation-based methods in terms of computational expense and performance on explainer-specific metrics (e.g., Fidelity and Sparsity). Our results provide insights into designing new GNN-based models for cyber malware detection and attribution.

Nowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.

Most IoT malware is variants generated by editing and reusing parts of the functions based on publicly available source codes. In our previous study, we proposed a method to estimate the functions of a specimen using the Function Call Sequence Graph (FCSG), which is a directed graph of execution sequence of function calls. In the FCSG-based method, the subgraph corresponding to a malware functionality is manually created and called a signature-FSCG. The specimens with the signature-FSCG are expected to have the corresponding functionality. However, this method cannot detect the specimens with a slightly different subgraph from the signature-FSCG. This paper found that these specimens were supposed to have the same functionality for a signature-FSCG. These specimens need more flexible signature matching, and we propose a graph embedding technique to realize it.

With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.

The analysis shows how important Power Network Measuring and Characterization (PSMC) is to the plan. Networks planning and oversight for the transmission of electrical energy is becoming increasingly frequent. In reaction to the current contest of assimilating trying to cut charging in the crate, estimation, information sharing, but rather govern into PSMC reasonable quantities, Electrical Transmit Monitoring and Management provides a thorough outline of founding principles together with smart sensors for domestic spying, security precautions, and control of developed broadening power systems.Electricity supply control must depend increasingly heavily on telecommunications infrastructure to manage and run their processes because of the fluctuation in transmission and distribution of electricity. A wider attack surface will also be available to threat hackers as a result of the more communications. Large-scale blackout have occurred in the past as a consequence of cyberattacks on electrical networks. In order to pinpoint the key issues influencing power grid computer networks, we looked at the network infrastructure supporting electricity grids in this research.

The data of large-scale distributed demand-side iot devices are gradually migrated to the cloud. This cloud deployment mode makes it convenient for IoT devices to participate in the interaction between supply and demand, and at the same time exposes various vulnerabilities of IoT devices to the Internet, which can be easily accessed and manipulated by hackers to launch large-scale DDoS attacks. As an easy-to-understand supervised learning classification algorithm, KNN can obtain more accurate classification results without too many adjustment parameters, and has achieved many research achievements in the field of DDoS detection. However, in the face of high-dimensional data, this method has high operation cost, high cost and not practical. Aiming at this disadvantage, this chapter explores the potential of classical KNN algorithm in data storage structure, K-nearest neighbor search and hyperparameter optimization, and proposes an improved KNN algorithm for DDoS attack detection of demand-side IoT devices.