Biblio

Malware created by the Advanced Persistent Threat (APT) groups do not typically carry out the attacks in a single stage. The “Cyber Kill Chain” framework developed by Lockheed Martin describes an APT through a seven stage life cycle [5] . APT groups are generally nation state actors [1] . They perform highly targeted attacks and do not stop until the goal is achieved [7] . Researchers are always working toward developing a system and a process to create an environment safe from APT type attacks [2] . In this paper, the threat considered is ransomware which are developed by APT groups. WannaCry is an example of a highly sophisticated ransomware created by the Lazurus group of North Korea and its level of sophistication is evident from the existence of a contingency plan of attack upon being discovered [3] [6] . The major contribution of this research is the analysis of APT type ransomware using game theory to present optimal strategies for the defender through the development of equilibrium solutions when faced with APT type ransomware attack. The goal of the equilibrium solutions is to help the defender in preparedness before the attack and in minimization of losses during and after the attack.

Ransomware uses encryption methods to make data inaccessible to legitimate users. To date a wide range of ransomware families have been developed and deployed, causing immense damage to governments, corporations, and private users. As these cyberthreats multiply, researchers have proposed a range of ransom ware detection and classification schemes. Most of these methods use advanced machine learning techniques to process and analyze real-world ransomware binaries and action sequences. Hence this paper presents a survey of this critical space and classifies existing solutions into several categories, i.e., including network-based, host-based, forensic characterization, and authorship attribution. Key facilities and tools for ransomware analysis are also presented along with open challenges.

Recent years have witnessed a surge in ransomware attacks. Especially, many a new variant of ransomware has continued to emerge, employing more advanced techniques distributing the payload while avoiding detection. This renders the traditional static ransomware detection mechanism ineffective. In this paper, we present our Hardware Anomaly Realtime Detection - Lightweight (HARD-Lite) framework that employs semi-supervised machine learning method to detect ransomware using low-level hardware information. By using an LSTM network with a weighted majority voting ensemble and exponential moving average, we are able to take into consideration the temporal aspect of hardware-level information formed as time series in order to detect deviation in system behavior, thereby increasing the detection accuracy whilst reducing the number of false positives. Testing against various ransomware across multiple families, HARD-Lite has demonstrated remarkable effectiveness, detecting all cases tested successfully. What's more, with a hierarchical design that distributing the classifier from the user machine that is under monitoring to a server machine, Hard-Lite enables good scalability as well.

Ransomware groups represent a significant cyber threat to Western states. Most high-end ransomware actors reside in territorial safe-haven jurisdictions and prove to be resistant to traditional law enforcement activities. This has prompted public sector and cybersecurity industry leaders to perceive ransomware as a national security threat requiring a whole-of-government approach, including cyber operations. In this paper, we investigate whether cyber operations or the threat of cyber operations influence the ransomware ecosystem. Subsequently, we assess the vectors of influence and characteristics of past operations that have disrupted the ecosystem. We describe the specifics of the ransomware-as-a-service system and provide three case studies (DarkSide/BlackMatter, REvil, Conti) highly representative of the current ecosystem and the effect cyber operations have on it. Additionally, we present initial observations about the influence of cyber operations on the system, including best practices from cyber operations against non-state groups. We conclude that even professional, highly skilled, and top-performing ransomware groups can be disrupted through cyber operations. In fact, cyber operations can even bypass some limits imposed on law enforcement operations. Even when ransomware groups rebrand or resurface after a hiatus, we suggest their infrastructure (both technical, human, and reputational) will still suffer mid-to long-term disruption. Although cyber operations are unlikely to be a silver bullet, they are an essential tool in the whole-of-government and multinational efforts and may even grow in importance in the next several years.1‘Releasing the hounds’ is a term for offensive cyber operations aimed at disrupting global ransomware gangs, especially those conducted by militaries or intelligence agencies. First use is found in Patrick Gray and Adam Boileau, ‘Feature Podcast: Releasing the Hounds with Bobby Chesney’, Risky Business, 28 May 2020, https://risky.biz/HF6/.

Ransomware is an emerging threat that imposed a \$ 5 billion loss in 2017, rose to \$ 20 billion in 2021, and is predicted to hit \$ 256 billion in 2031. While initially targeting PC (client) platforms, ransomware recently leaped over to server-side databases-starting in January 2017 with the MongoDB Apocalypse attack and continuing in 2020 with 85,000 MySQL instances ransomed. Previous research developed countermeasures against client-side ransomware. However, the problem of server-side database ransomware has received little attention so far. In our work, we aim to bridge this gap and present DIMAQS (Dynamic Identification of Malicious Query Sequences), a novel anti-ransomware solution for databases. DIMAQS performs runtime monitoring of incoming queries and pattern matching using two classification approaches (Colored Petri Nets (CPNs) and Deep Neural Networks (DNNs)) for attack detection. Our system design exhibits several novel techniques like dynamic color generation to efficiently detect malicious query sequences globally (i.e., without limiting detection to distinct user connections). Our proof-of-concept and ready-to-use implementation targets MySQL servers. The evaluation shows high efficiency without false negatives for both approaches and a false positive rate of nearly 0%. Both classifiers show very moderate performance overheads below 6%. We will publish our data sets and implementation, allowing the community to reproduce our tests and results.

This paper presents the machine learning algorithm to detect whether an executable binary is benign or ransomware. The ransomware cybercriminals have targeted our infrastructure, businesses, and everywhere which has directly affected our national security and daily life. Tackling the ransomware threats more effectively is a big challenge. We applied a machine-learning model to classify and identify the security level for a given suspected malware for ransomware detection and prevention. We use the feature selection data preprocessing to improve the prediction accuracy of the model.

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim’s files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges.

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.

Cyber security is turning into a significant angle in each industry like in banking part, force and computerization segments. Servers are basic resources in these enterprises where business basic touch information is put away. These servers frequently join web servers in them through which any business information and tasks are performed remotely. Thus, clearly for a solid activity, security of web servers is extremely basic. This paper gives another testing way to deal with defenselessness appraisal of web applications by methods for breaking down and utilizing a consolidated arrangement of apparatuses to address a wide scope of security issues.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

We demonstrate an in-house built Endpoint Detection and Response (EDR) for linux systems using open-sourced tools like Osquery and Elastic. The advantage of building an in-house EDR tools against using commercial EDR tools provides both the knowledge and the technical capability to detect and investigate security incidents. We discuss the architecture of the tools and advantages it offers. Specifically, in our method all the endpoint logs are collected at a common server which we leverage to perform correlation between events happening on different endpoints and automatically detect threats like pivoting and lateral movements. We discuss various attacks that can be detected by our tool.

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.

Critical infrastructure is a key area in cybersecurity. In the U.S., it was front and center in 1997 with the report from the President’s Commission on Critical Infrastructure Protection (PCCIP), and now affects countries worldwide. Critical Infrastructure Protection must address all types of cybersecurity threats - insider threat, ransomware, supply chain risk management issues, and so on. Unsurprisingly, in the past 25 years, the risks and incidents have increased rather than decreased and appear in the news daily. As an important component of critical infrastructure protection, secure supply chain risk management must be integrated into development projects. Both areas have important implications for security requirements engineering.

Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.

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.

Smart grids can be exposed to relay attacks (or wormhole attacks) resulting from weaknesses in cryptographic operations such as authentication and key derivation associated with process automation protocols. Relay attacks refer to attacks in which authentication is evaded without needing to attack the smart grid itself. By using a universal composability model that provides a strong security notion for designing cryptographic operations, we formulate the necessary relay resilience settings for strengthening authentication and key derivation and enhancing relay security in process automation protocols in this paper. We introduce R-Chain, a universally composable relay resilience framework that prevents bypass of cryptographic operations. Our framework provides an ideal chaining functionality that integrates all cryptographic operations such that all outputs from a preceding operation are used as input to the subsequent operation to support relay resilience. We apply R-Chain to provide relay resilience in a practical smart grid process automation protocol, namely WirelessHART.

In the era of rapid technological growth, malicious traffic has drawn increased attention. Most well-known offensive security assessment todays are heavily focused on pre-compromise. The amount of anomalous data in today's context is massive. Analyzing the data using primitive methods would be highly challenging. Solution to it is: If we can detect adversary behaviors in the early stage of compromise, one can prevent and safeguard themselves from various attacks including ransomwares and Zero-day attacks. Integration of new technologies Artificial Intelligence & Machine Learning with manual Anomaly Detection can provide automated machine-based detection which in return can provide the fast, error free, simplify & scalable Threat Detection & Response System. Endpoint Detection & Response (EDR) tools provide a unified view of complex intrusions using known adversarial behaviors to identify intrusion events. We have used the EMBER dataset, which is a labelled benchmark dataset. It is used to train machine learning models to detect malicious portable executable files. This dataset consists of features derived from 1.1 million binary files: 900,000 training samples among which 300,000 were malicious, 300,000 were benevolent, 300,000 un-labelled, and 200,000 evaluation samples among which 100K were malicious, 100K were benign. We have also included open-source code for extracting features from additional binaries, enabling the addition of additional sample features to the dataset.

A new method for the detection of ransomware in an infected host is described and evaluated. The method utilizes data streams from on-board sensors to fingerprint the initiation of a ransomware infection. These sensor streams, which are common in modern computing systems, are used as a side channel for understanding the state of the system. It is shown that ransomware detection can be achieved in a rapid manner and that the use of slight, yet distinguishable changes in the physical state of a system as derived from a machine learning predictive model is an effective technique. A feature vector, consisting of various sensor outputs, is coupled with a detection criteria to predict the binary state of ransomware present versus normal operation. An advantage of this approach is that previously unknown or zero-day version s of ransomware are vulnerable to this detection method since no apriori knowledge of the malware characteristics are required. Experiments are carried out with a variety of different system loads and with different encryption methods used during a ransomware attack. Two test systems were utilized with one having a relatively low amount of available sensor data and the other having a relatively high amount of available sensor data. The average time for attack detection in the "sensor-rich" system was 7.79 seconds with an average Matthews correlation coefficient of 0.8905 for binary system state predictions regardless of encryption method and system load. The model flagged all attacks tested.

Threats, posed by ransomware, are rapidly increasing, and its cost on both national and global scales is becoming significantly high as evidenced by the recent events. Ransomware carries out an irreversible process, where it encrypts victims' digital assets to seek financial compensations. Adversaries utilize different means to gain initial access to the target machines, such as phishing emails, vulnerable public-facing software, Remote Desktop Protocol (RDP), brute-force attacks, and stolen accounts. To combat these threats of ransomware, this paper aims to help researchers gain a better understanding of ransomware application profiles through static analysis, where we identify a list of suspicious indicators and similarities among 727 active ran-somware samples. We start with generating portable executable (PE) metadata for all the studied samples. With our domain knowledge and exploratory data analysis tasks, we introduce some of the suspicious indicators of the structure of ransomware files. We reduce the dimensionality of the generated dataset by using the Principal Component Analysis (PCA) technique and discover clusters by applying the KMeans algorithm. This motivates us to utilize the one-class classification algorithms on the generated dataset. As a result, the algorithms learn the common data boundary in the structure of our studied ransomware samples, and thereby, we achieve the data-driven similarities. We use the findings to evaluate the trained classifiers with the test samples and observe that the Local Outlier Factor (LoF) performs better on all the selected feature spaces compared to the One-Class SVM and the Isolation Forest algorithms.

The number of prominent ransomware attacks has increased recently. In this research, we detect ransomware by analyzing network traffic by using machine learning algorithms and comparing their detection performances. We have developed multi-class classification models to detect families of ransomware by using the selected network traffic features, which focus on the Transmission Control Protocol (TCP). Our experiment showed that decision trees performed best for classifying ransomware families with 99.83% accuracy, which is slightly better than the random forest algorithm with 99.61% accuracy. The experimental result without feature selection classified six ransomware families with high accuracy. On the other hand, classifiers with feature selection gave nearly the same result as those without feature selection. However, using feature selection gives the advantage of lower memory usage and reduced processing time, thereby increasing speed. We discovered the following ten important features for detecting ransomware: time delta, frame length, IP length, IP destination, IP source, TCP length, TCP sequence, TCP next sequence, TCP header length, and TCP initial round trip.

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.

Ransomware is a major malware attack experienced by large corporations and healthcare services. Ransomware employs the idea of cryptovirology, which uses cryptography to design malware. The goal of ransomware is to extort ransom by threatening the victim with the destruction of their data. Ransomware typically involves a 3-step process: analyzing the victim’s network traffic, identifying a vulnerability, and then exploiting it. Thus, the detection of ransomware has become an important undertaking that involves various sophisticated solutions for improving security. To further enhance ransomware detection capabilities, this paper focuses on an Application Programming Interface (API)-based ransomware detection approach in combination with machine learning (ML) techniques. The focus of this research is (i) understanding the life cycle of ransomware on the Windows platform, (ii) dynamic analysis of ransomware samples to extract various features of malicious code patterns, and (iii) developing and validating machine learning-based ransomware detection models on different ransomware and benign samples. Data were collected from publicly available repositories and subjected to sandbox analysis for sampling. The sampled datasets were applied to build machine learning models. The grid search hyperparameter optimization algorithm was employed to obtain the best fit model; the results were cross-validated with the testing datasets. This analysis yielded a high ransomware detection accuracy of 99.18% for Windows-based platforms and shows the potential for achieving high-accuracy ransomware detection capabilities when using a combination of API calls and an ML model. This approach can be further utilized with existing multilayer security solutions to protect critical data from ransomware attacks.

Crypto-ransomware is a malware that uses the system’s cryptography functions to encrypt user data. The irreversible effect of crypto-ransomware makes it challenging to survive the attack compared to other malware categories. When a crypto-ransomware attack encrypts user files, it becomes difficult to access these files without having the decryption key. Due to the availability of ransomware development tool kits like Ransomware as a Service (RaaS), many ransomware variants are being developed. This contributes to the rise of ransomware attacks witnessed nowadays. However, the conventional approaches employed by malware detection solutions are not suitable to detect ransomware. This is because ransomware needs to be detected as early as before the encryption takes place. These attacks can effectively be handled only if detected during the pre-encryption phase. Early detection of ransomware attacks is challenging due to the limited amount of data available before encryption. An adaptive pre-encryption model is proposed in this paper which is expected to deal with the population concept drift of crypto-ransomware given the limited amount of data collected during the pre-encryption phase of the attack lifecycle. With such adaptability, the model can maintain up-to-date knowledge about the attack behavior and identify the polymorphic ransomware that continuously changes its behavior.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

The data-driven period produces more and more security-related challenges that even experts can hardly deal with. One of the most complex threats is ransomware, which is very taxing and devastating to detect and mainly prevent. Our research methods showed significant results in identifying ransomware processes using the honeypot concept augmented with symbolic linking to reduce damage made to the file system. The CIA (confidentiality, integrity, availability) metrics have been adhered to. We propose to optimize the malware process termination procedure and introduce an artificial intelligence-human collaboration to enhance ransomware classification and detection.