本研究利用人工智能和机器学习进行内存特征工程，提出基于Classic Random Forest的多分类模型检测三种混淆恶意软件，准确率达89.07%，优于现有模型。
摘要：
在配备智能技术的设备时代，为信息提供安全保障至关重要，几乎无法想象没有互联网的一天。廉价的高速互联网不仅使合法用户的通信变得容易，也使网络犯罪分子能够从多个维度发起攻击，破坏隐私和安全。网络犯罪分子获取非法访问权限并侵犯用户隐私，以多种方式对用户造成伤害。恶意软件就是黑客用来执行其恶意意图的工具之一。恶意软件开发者利用AI技术的发展来造成社会危害。在这项工作中，我们旨在展示如何利用人工智能和机器学习来检测和缓解由恶意软件（特别是混淆恶意软件）引发的网络攻击。我们对恶意软件样本的内存分析进行了内存特征工程实验。二分类虽然能识别给定样本是否为恶意软件，但只有识别出恶意软件的类型，才能指导针对该恶意软件采取何种下一步措施，以阻止其继续执行后续行动。因此，我们提出了一种多分类模型，使用Classic Random Forest算法检测三种类型的混淆恶意软件，准确率达到89.07%。据我们所知，目前使用单一模型对多种混淆恶意软件进行分类的研究工作非常少。我们还将我们的模型与几种最先进的模型进行了比较，发现其表现相对更好。
英文摘要：
Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made communication easy for legitimate users but also for cybercriminals to induce attacks in various dimensions to breach privacy and security. Cybercriminals gain illegal access and breach the privacy of users to harm them in multiple ways. Malware is one such tool used by hackers to execute their malicious intent. Development in AI technology is utilized by malware developers to cause social harm. In this work, we intend to show how Artificial Intelligence and Machine learning can be used to detect and mitigate these cyber-attacks induced by malware in specific obfuscated malware. We conducted experiments with memory feature engineering on memory analysis of malware samples. Binary classification can identify whether a given sample is malware or not, but identifying the type of malware will only guide what next step to be taken for that malware, to stop it from proceeding with its further action. Hence, we propose a multi-class classification model to detect the three types of obfuscated malware with an accuracy of 89.07% using the Classic Random Forest algorithm. To the best of our knowledge, there is very little amount of work done in classifying multiple obfuscated malware by a single model. We also compared our model with a few state-of-the-art models and found it comparatively better.
With the advent of new technologies, using various formats of digital gadgets is becoming widespread. In today's world, where everyday tasks are inevitable without technology, this extensive use of computers paves the way for malicious activity. As a result, it is important to provide solutions to defend against these threats. Malware is one of the well-known and widely used means utilized for doing destructive activities by malicious attackers. Producing malware from scratch is somewhat difficult, so attackers tend to obfuscate existing malware and prepare it to become an unrecognizable progr…
In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep Reinforcement Learning based De-Obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system to date that uses advanced DR…
With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to protect these devices as they become more prevalent in commercial and personal networks. However, due to their limited computational power and storage space, especially in the case of battery-powered devices, it is infeasible to deploy state-of-the-art malware detectors onto these systems. In this work, we propose using and extracting features from Markov matrices constructed from opcode traces as a low cost feature for …
With the number of new mobile malware instances increasing by over 50\% annually since 2012 [24], malware embedding in mobile apps is arguably one of the most serious security issues mobile platforms are exposed to. While obfuscation techniques are successfully used to protect the intellectual property of apps' developers, they are unfortunately also often used by cybercriminals to hide malicious content inside mobile apps and to deceive malware detection tools. As a consequence, most of mobile malware detection approaches fail in differentiating between benign and obfuscated malicious apps. W…
We present a novel malware detection approach based on metrics over quantitative data flow graphs. Quantitative data flow graphs (QDFGs) model process behavior by interpreting issued system calls as aggregations of quantifiable data flows.Due to the high abstraction level we consider QDFG metric based detection more robust against typical behavior obfuscation like bogus call injection or call reordering than other common behavioral models that base on raw system calls. We support this claim with experiments on obfuscated malware logs and demonstrate the superior obfuscation robustness in compa…
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Department of Computer Applications, Cochin University of Science and Technology, India（计算机应用系，科钦大学科学与技术学院，印度）；Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy（电气、计算机和生物医学工程系，帕维亚大学，意大利）
New computing paradigms, modern feature-rich programming languages and off-the-shelf software libraries enabled the development of new sophisticated malware families. Evidence of this phenomena is the recent growth of fileless malware attacks. Fileless malware or memory resident malware is an example of an Advanced Volatile Threat (AVT). In a fileless malware attack, the malware writes itself directly onto the main memory (RAM) of the compromised device without leaving any trace on the compromised device's file system. For this reason, fileless malware presents a difficult challenge for tradit…
The web is experiencing an explosive growth in the last years. New technologies are introduced at a very fast-pace with the aim of narrowing the gap between web-based applications and traditional desktop applications. The results are web applications that look and feel almost like desktop applications while retaining the advantages of being originated from the web. However, these advancements come at a price. The same technologies used to build responsive, pleasant and fully-featured web applications, can also be used to write web malware able to escape detection systems. In this article we pr…
Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properti…
Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become more sophisticated and complex. Researchers have therefore turned to deep learning to construct better performing model. In this paper, we create four different long-short term memory (LSTM) based models and train each to classify malware samples from 20 families. Our features consist of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP), including word embeddin…
This paper focuses on the anticipatory enhancement of methods of detecting stealth software. Cyber security detection tools are insufficiently powerful to reveal the most recent cyber-attacks which use malware. In this paper, we will present first an idea of the highest stealth malware, as this is the most complicated scenario for detection because it combines both existing anti-forensic techniques together with their potential improvements. Second, we present new detection methods, which are resilient to this hidden prototype. To help solve this detection challenge, we have analyzed Windows m…
To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable systems is desirable. Threat intelligence is crucial for guiding Security Operations Center (SOC) analysts' focus toward specific system activity and provides the primary contextual foundation for interpreting security alerts. This paper explores novel approaches for improving incident response triage operations, including dealing with attacks and zero-day malw…
Malware writers frequently try to hide the activities of their agents within tunnelled traffic. Within the Kill Chain model the infection time is often measured in seconds, and if the infection is not detected and blocked, the malware agent, such as a bot, will often then set up a secret channel to communicate with its controller. In the case of ransomware the communicated payload may include the encryption key used for the infected host to register its infection. As a malware infection can spread across a network in seconds, it is often important to detect its activities on the air, in memory…
Android malware have been growing at an exponential pace and becomes a serious threat to mobile users. It appears that most of the anti-malware still relies on the signature-based detection system which is generally slow and often not able to detect advanced obfuscated malware. Hence time-to-time various authors have proposed different machine learning solutions to identify sophisticated malware. However, it appears that detection accuracy can be improved by using the clustering method. Therefore in this paper, we propose a novel scalable and effective clustering method to improve the detectio…
In recent years malware has become increasingly sophisticated and difficult to detect prior to exploitation. While there are plenty of approaches to malware detection, they all have shortcomings when it comes to identifying malware correctly prior to exploitation. The trade-off is usually between false positives, causing overhead, preventing normal usage and the risk of letting the malware execute and cause damage to the target. We present a novel end-to-end solution for in-memory malicious activity detection done prior to exploitation by leveraging machine learning capabilities based on data …
MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation
London Metropolitan University（伦敦 Metropolitan 大学）；Cyber Security Research Centre（网络安全研究中心）；Cybersecurity Institute（网络安全研究所）；Department of Computer Science（计算机科学系）
Modern malware and spyware platforms attack existing antivirus solutions and even Microsoft PatchGuard. To protect users and business systems new technologies developed by Intel and AMD CPUs may be applied. To deal with the new malware we propose monitoring and controlling access to the memory in real time using Intel VT-x with EPT. We have checked this concept by developing MemoryMonRWX, which is a bare-metal hypervisor. MemoryMonRWX is able to track and trap all types of memory access: read, write, and execute. MemoryMonRWX also has the following competitive advantages: fine-grained analysis…
In order to find a specific type of malware which is related to GSM frequency, we propose an algorithm according to the most essential characteristics of this malware. At first, detect whether or not there exists a specific thread in the memory. And then, the generated binary strings will be tried to be matched with the one in the target computer. At last, determine whether this threat occurs or not. Furthermore, we study the effective of the new method via some simulations.
This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program executio…
Advances in reverse engineering make it challenging to deploy any on-chip information in a way that is hidden from a determined attacker. A variety of techniques have been proposed for design obfuscation including look-alike cells in which functionality is determined by hard to observe mechanisms including dummy vias or transistor threshold voltages. Threshold-based obfuscation is especially promising because threshold voltages cannot be observed optically and require more sophisticated measurements by the attacker. In this work, we demonstrate the effectiveness of a methodology that applies t…
Embedded systems permeate through nearly all aspects of modern society. From cars to refrigerators to nuclear refineries, securing these systems has never been more important. Intrusions, such as the Stuxnet malware which broke the centrifuges in Iran's Natanz refinery, can be catastrophic to not only the infected systems, but even to the wellbeing of the surrounding population. Modern day protection mechanisms for these embedded systems generally look only at protecting the network layer, and those that try to discover malware already existing on a system typically aren't efficient enough to …
Since modern anti-virus software mainly depends on a signature-based static analysis, they are not suitable for coping with the rapid increase in malware variants. Moreover, even worse, many vulnerabilities of operating systems enable attackers to evade such protection mechanisms. We, therefore, developed a thin and lightweight live-forensic hypervisor to create an additional protection layer under a conventional protection layer of operating systems with supporting ransomware detection using dynamic behavioral features. The developed live-forensic hypervisor collects low-level memory access p…
We offer an embedding of CPython that runs entirely in memory without "touching" the disk. This in-memory embedding can load Python scripts directly from memory instead these scripts having to be loaded from files on disk. Malware that resides only in memory is harder to detect or mitigate against. We intend for our work to be used by security researchers to rapidly develop and deploy offensive techniques that is difficult for security products to analyze given these instructions are in bytecode and only translated to machine-code by the interpreter immediately prior to execution. Our work hel…
Network intrusion detection systems and antivirus software are essential in detecting malicious network traffic and attacks such as denial-of-service and malwares. Each attack, worm or virus has its own distinctive signature. Signature-based intrusion detection and antivirus systems depend on pattern matching to look for possible attack signatures. Pattern matching is a very complex task, which requires a lot of time, memory and computing resources. Software-based intrusion detection is not fast enough to match high network speeds and the increasing number of attacks. In this paper, we propose…
University of California at Santa Cruz（加州大学圣克鲁兹分校）；Microsoft Research（微软研究院）；Microsoft Corp.（微软公司）
Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their ability to run malicious command sequences. Malware authors even use polymorphism to reorder these commands and create several malicious variations. However, if executed in a secure environment, one can perform early malware detection on emulated command sequences. The models presented in this paper leverage this sequential data derived via emulation in order to …
Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each …
Effectively protecting the Windows OS is a challenging task, since most implementation details are not publicly known. Windows has always been the main target of malwares that have exploited numerous bugs and vulnerabilities. Recent trusted boot and additional integrity checks have rendered the Windows OS less vulnerable to kernel-level rootkits. Nevertheless, guest Windows Virtual Machines are becoming an increasingly interesting attack target. In this work we introduce and analyze a novel Hypervisor-Based Introspection System (HyBIS) we developed for protecting Windows OSes from malware and …
ContractShield: Bridging Semantic-Structural Gaps via Hierarchical Cross-Modal Fusion for Multi-Label Vulnerability Detection in Obfuscated Smart Contracts
University of Information Technology（信息技术大学）；Vietnam National University Ho Chi Minh City（越南国家大学胡志明市分校）；VNU-HCM Information Security Center（胡志明市越南国家大学信息安全中心）；Singapore Institute of Technology（新加坡理工学院）
University of Manchester（曼彻斯特大学）；University of Bristol（布里斯托尔大学）
In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest amount of personal information on their mobile devices, such as their contacts, social media profiles, emails, and bank accounts. Both dynamic and static malware analysis is necessary to prevent and detect malware, as both techniques have their benefits and shortcomings. In this paper, we propose a deep learning technique that relies on LSTM and encoder-decoder n…
