Biblio

Malware is one of the biggest threats in cloud computing. Malware running inside virtual machines or containers could steal critical information or continue to attack other cloud nodes. To detect malware in cloud, especially zero-day malware, signature-and machine-learning-based approaches are proposed to analyze the execution binary. However, malicious binary files may not permanently be stored in the file system of virtual machine or container, periodically scanner may not find the target files. Dynamic analysis approach usually introduce run-time overhead to virtual machines, which is not widely used in cloud. To solve these problems, we propose a memory analysis approach to detect malware, employing the deep learning technology. The system analyzes the memory image periodically during malware execution, which will not introduce run-time overhead. We first extract the memory snapshot from running virtual machines or containers. Then, the snapshot is converted to a grayscale image. Finally, we employ CNN to detect malware. In the learning phase, malicious and benign software are trained. In the testing phase, we test our system with real-world malwares.

Research in genetic improvement (GI) conventionally focuses on the improvement of software, including the automated repair of bugs and vulnerabilities as well as the refinement of software to increase performance. Eliminating or reducing vulnerabilities using GI has improved the security of benign software, but the growing volume and complexity of malicious software necessitates better analysis techniques that may benefit from a GI-based approach. Rather than focus on the use of GI to improve individual software artifacts, we believe GI can be applied to the tools used to analyze malicious code for its behavior. First, malware analysis is critical to understanding the damage caused by an attacker, which GI-based bug repair does not currently address. Second, modern malware samples leverage complex vectors for infection that cannot currently be addressed by GI. In this paper, we discuss an application of genetic improvement to the realm of automated malware analysis through the use of variable-strength covering arrays.

As time progresses, new and complex malware types are being generated which causes a serious threat to computer systems. Due to this drastic increase in the number of malware samples, the signature-based malware detection techniques cannot provide accurate results. Different studies have demonstrated the proficiency of machine learning for the detection and classification of malware files. Further, the accuracy of these machine learning models can be improved by using feature selection algorithms to select the most essential features and reducing the size of the dataset which leads to lesser computations. In this paper, we have developed a machine learning based malware analysis framework for efficient and accurate malware detection and classification. We used Cuckoo sandbox for dynamic analysis which executes malware in an isolated environment and generates an analysis report based on the system activities during execution. Further, we propose a feature extraction and selection module which extracts features from the report and selects the most important features for ensuring high accuracy at minimum computation cost. Then, we employ different machine learning algorithms for accurate detection and fine-grained classification. Experimental results show that we got high detection and classification accuracy in comparison to the state-of-the-art approaches.

IoT devices can be used to fulfil many of our daily tasks. IoT could be wearable devices, home appliances, or even light bulbs. With the introduction of this new technology, however, vulnerabilities are being introduced and can be leveraged or exploited by malicious users. One common vehicle of exploitation is malicious software, or malware. Malware can be extremely harmful and compromise the confidentiality, integrity and availability (CIA triad) of information systems. This paper analyzes the types of malware attacks, introduce some mitigation approaches and discusses future challenges.

Malware analysis and detection based on it is very important factor in the computer security. Despite of the enormous effort of companies making anti-malware solutions, it is usually not possible to respond to new malware in time and some computers will get infected. This shortcoming could be partially mitigated through using behavioral malware analysis. This work is aimed towards machine learning algorithms comparison for the behavioral malware analysis purposes.

The most common defenses against malware threats involves the use of signatures derived from instances of known malware. However, the constant evolution of the malware threat landscape necessitates defense against unknown malware, making a signature catalog of known threats insufficient to prevent zero-day vulnerabilities from being exploited. Recent research has applied machine learning approaches to identify malware through artifacts of malicious activity as observed through dynamic behavioral analysis. We have seen that these approaches mimic common malware defenses by simply offering a method of detecting known malware. We contribute a new method of identifying software as malicious or benign through analysis of the frequency of Windows API system function calls. We show that this is a powerful technique for malware detection because it generates learning models which understand the difference between malicious and benign software, rather than producing a malware signature classifier. We contribute a method of systematically comparing machine learning models against different datasets to determine their efficacy in accurately distinguishing the difference between malicious and benign software.

Not only the number of deployed IoT devices increases but also that of IoT malware increases. We eager to understand the threat made by IoT malware but we lack tools to observe, analyze and detect them. We design and implement an automatic, virtual machine-based profiling system to collect valuable IoT malware behavior, such as API call invocation, system call execution, etc. In addition to conventional profiling methods (e.g., strace and packet capture), the proposed profiling system adapts virtual machine introspection based API hooking technique to intercept API call invocation by malware, so that our introspection would not be detected by IoT malware. We then propose a method to convert the multiple sequential data (API calls) to a family behavior graph for further analysis.

Most of the malware detection techniques use malware signatures for detection. It is easy to detect known malicious program in a system but the problem arises when the malware is unknown. Because, unknown malware cannot be detected by using available known malware signatures. Signature based detection techniques fails to detect unknown and zero-day attacks. A novel approach is required to represent malware features effectively to detect obfuscated, unknown, and mutated malware. This paper emphasizes malware behavior, characteristics and properties extracted by different analytic techniques and to decide whether to include them to create behavioral based malware signature. We have made an attempt to understand the malware behavior using a few openly available tools for malware analysis.

Linux is used in a large variety of situations, from private homes on personal machines to businesses storing personal data on servers. This operating system is often seen as more secure than Windows or Mac OS X, but this does not mean that there are no security concerns to be had when running it. Attackers can crack simple passwords over a network, vulnerabilities can be exploited if firewalls do not close enough ports, and malware can be downloaded and run on a Linux system. In addition, sensitive information can be accessed through physical or network access if proper permissions are not set on the files or directories containing it. However, most of these attacks can be prevented by keeping a system up to date, maintaining a secure firewall, using an antivirus, making complex passwords, and setting strong file permissions. This paper presents a list of methods for securing a Linux system from both external and internal threats.

This study was conducted in order to assess the E-security behavior of students in a large higher educational institutions in the United Arab Emirates (UAE). Specifically, it sought to determine the current state of students' E-security behavior in the aspects of malware, password usage, data handling, phishing, social engineering, and online scam. An E- Security Behavior Survey Instrument (EBSI) was used to determine the status of security behavior of the participants in doing their computing activities. To complement the survey tool, focus group discussions were conducted to elicit specific experiences and insights of the participants relative to E-security. The results of the study shows that the overall E-security behavior among students in higher education in the United Arab Emirates (UAE) is moderately favorable. Specifically, the investigation reveals that the students favorably behave when it comes to phishing, social engineering, and online scam. However, they uncertainly behave on malware issues, password usage, and data handling.

Over the past few years, botnets have emerged as one of the most serious cybersecurity threats faced by individuals and organizations. After infecting millions of servers and workstations worldwide, botmasters have started to develop botnets for mobile devices. Mobile botnets use different mediums to communicate with their botmasters. Although significant research has been done to detect mobile botnets that use the Internet as their command and control (C&C) channel, little research has investigated SMS botnets per se. In order to fill this gap, in this paper, we first divide SMS botnets based on their characteristics into three families, namely, info stealer, SMS stealer, and SMS spammer. Then, we propose SMSBotHunter, a novel anomaly detection technique that detects SMS botnets using textual and behavioral features and one-class classification. We experimentally evaluate the detection performance of SMSBotHunter by simulating the behavior of human users and SMS botnets. The experimental results demonstrate that most of the SMS messages sent or received by info stealer and SMS spammer botnets can be detected using textual features exclusively. It is also revealed that behavioral features are crucial for the detection of SMS stealer botnets and will improve the overall detection performance.

Automatic test systems designed to validate the performance of military and aerospace products have always been held to a higher standard; moreover, emerging threats to data security and instrumentation integrity continue to raise this bar. Engineers are faced with growing pressure to not only ensure that the unit under test (UUT) meets all design criteria, but that it remains safe from malicious attacks aimed at gaining access to test parameters or results, controlling of test sequences and functionality, downloading malware, or impacting functionality by way of counterfeit parts installed in instrumentation. This paper will delve into the cybersecurity issue from the perspective of the test development environment, including the use of test executives, and the challenges associated with minimizing impact to data integrity and access to control. An undetected data breach on military / aerospace automated test equipment (ATE) holds significance beyond just the test system, since mission critical electronics associated with avionics, radar, electronic warfare and missile assemblies must also be protected. One topic discussed will be the impact of adopting methods and procedures detailed in the Department of Defense's (DoD) Application Security Technical Implementation Guide, which is based on NIST documents and details how to manage and maintain a secure software-based system such as an ATE system. Another aspect of cybersecurity that is often overlooked in the world of commercial-off-the-shelf (COTS) instrumentation and switching systems is the potential impact on the UUT from substandard counterfeit parts and those embedded with malware. Concerns with counterfeit material can encompass a range of threats including the re-purposing of used parts and new knockoff parts with substandard operating characteristics represented and sold as new hardware. One of the most concerning aspects, parts intentionally infected with malware, can pose a significant risk to personnel and national security. We will discuss various strategies aimed at countering these threats, including the adoption of policies and procedures outlined in AS9100D and AS5553, which can mitigate these risks.

The sheer size of IoT networks being deployed today presents an "attack surface" and poses significant security risks at a scale never before encountered. In other words, a single device/node in a network that becomes infected with malware has the potential to spread malware across the network, eventually ceasing the network functionality. Simply detecting and quarantining the malware in IoT networks does not guarantee to prevent malware propagation. On the other hand, use of traditional control theory for malware confinement is not effective, as most of the existing works do not consider real-time malware control strategies that can be implemented using uncertain infection information of the nodes in the network or have the containment problem decoupled from network performance. In this work, we propose a two-pronged approach, where a runtime malware detector (HaRM) that employs Hardware Performance Counter (HPC) values to detect the malware and benign applications is devised. This information is fed during runtime to a stochastic model predictive controller to confine the malware propagation without hampering the network performance. With the proposed solution, a runtime malware detection accuracy of 92.21% with a runtime of 10ns is achieved, which is an order of magnitude faster than existing malware detection solutions. Synthesizing this output with the model predictive containment strategy lead to achieving an average network throughput of nearly 200% of that of IoT networks without any embedded defense.

The growing number of attacks against cyber-physical systems in recent years elevates the concern for cybersecurity of industrial control systems (ICSs). The current efforts of ICS cybersecurity are mainly based on firewalls, data diodes, and other methods of intrusion prevention, which may not be sufficient for growing cyber threats from motivated attackers. To enhance the cybersecurity of ICS, a cyber-attack detection system built on the concept of defense-in-depth is developed utilizing network traffic data, host system data, and measured process parameters. This attack detection system provides multiple-layer defense in order to gain the defenders precious time before unrecoverable consequences occur in the physical system. The data used for demonstrating the proposed detection system are from a real-time ICS testbed. Five attacks, including man in the middle (MITM), denial of service (DoS), data exfiltration, data tampering, and false data injection, are carried out to simulate the consequences of cyber attack and generate data for building data-driven detection models. Four classical classification models based on network data and host system data are studied, including k-nearest neighbor (KNN), decision tree, bootstrap aggregating (bagging), and random forest (RF), to provide a secondary line of defense of cyber-attack detection in the event that the intrusion prevention layer fails. Intrusion detection results suggest that KNN, bagging, and RF have low missed alarm and false alarm rates for MITM and DoS attacks, providing accurate and reliable detection of these cyber attacks. Cyber attacks that may not be detectable by monitoring network and host system data, such as command tampering and false data injection attacks by an insider, are monitored for by traditional process monitoring protocols. In the proposed detection system, an auto-associative kernel regression model is studied to strengthen early attack detection. The result shows that this approach detects physically impactful cyber attacks before significant consequences occur. The proposed multiple-layer data-driven cyber-attack detection system utilizing network, system, and process data is a promising solution for safeguarding an ICS.

The rise in the number of devices joining the Internet of Things (IoT) has created a huge potential for distributed denial of service (DDoS) attacks, especially due to the lack of security in these computationally limited devices. Malicious actors have realized that and managed to turn large sets of IoT devices into botnets under their control. Given this scenario, this work studies botnet architectures identified so far and assesses how they are considered in the few recent defense proposals that consider botnet architectures.

Users regularly enter sensitive data, such as passwords, credit card numbers, or tax information, into the browser window. While modern browsers provide powerful client-side privacy measures to protect this data, none of these defenses prevent a browser compromised by malware from stealing it. In this work, we present Fidelius, a new architecture that uses trusted hardware enclaves integrated into the browser to enable protection of user secrets during web browsing sessions, even if the entire underlying browser and OS are fully controlled by a malicious attacker. Fidelius solves many challenges involved in providing protection for browsers in a fully malicious environment, offering support for integrity and privacy for form data, JavaScript execution, XMLHttpRequests, and protected web storage, while minimizing the TCB. Moreover, interactions between the enclave and the browser, the keyboard, and the display all require new protocols, each with their own security considerations. Finally, Fidelius takes into account UI considerations to ensure a consistent and simple interface for both developers and users. As part of this project, we develop the first open source system that provides a trusted path from input and output peripherals to a hardware enclave with no reliance on additional hypervisor security assumptions. These components may be of independent interest and useful to future projects. We implement and evaluate Fidelius to measure its performance overhead, finding that Fidelius imposes acceptable overhead on page load and user interaction for secured pages and has no impact on pages and page components that do not use its enhanced security features.

We present novel CAPTCHA challenges based on user gestures designed for mobile. A gesture CAPTCHA challenge is a security mechanism to prevent malware from gaining access to network resources from mobile. Mobile devices contain a number of sensors that record the physical movement of the device. We utilized the accelerometer and gyroscope data as inputs to our novel CAPTCHAs to capture the physical manipulation of the device. We conducted an experimental study on a group of people. We discovered that younger people are able to solve this type of CAPTCHA challenges successfully in a short amount of time. We found that using accelerometer readings produces issues for some older people.

Modern operating systems for personal computers (including Linux, MAC, and Windows) provide user-level APIs for an application to access the I/O paths of another application. This design facilitates information sharing between applications, enabling applications such as screenshots. However, it also enables user-level malware to log a user's keystrokes or scrape a user's screen output. In this work, we explore a design called SwitchMan to protect a user's I/O paths against user-level malware attacks. SwitchMan assigns each user with two accounts: a regular one for normal operations and a protected one for inputting and outputting sensitive data. Each user account runs under a separate virtual terminal. Malware running under a user's regular account cannot access sensitive input/output under a user's protected account. At the heart of SwitchMan lies a secure protocol that enables automatic account switching when an application requires sensitive input/output from a user. Our performance evaluation shows that SwitchMan adds acceptable performance overhead. Our security and usability analysis suggests that SwitchMan achieves a better tradeoff between security and usability than existing solutions.

The privacy of people on internet is getting reduced day by day. Data records of many prestigious organizations are getting corrupted due to computer malwares. Computer viruses are becoming more advanced. Hackers are able penetrate into a network and able to manipulate data. In this paper, describes the types of malwares like Trojans, boot sector virus, polymorphic virus, etc., and some of the hacking techniques which include DOS attack, DDoS attack, brute forcing, man in the middle attack, social engineering, information gathering tools, spoofing, sniffing. Counter measures for cyber attacks include VPN, proxy, tor (browser), firewall, antivirus etc., to understand the need of cyber security.

The concept of a decoy process is a new development of defensive deception beyond traditional honeypots. Decoy processes can be exceptionally effective in detecting malware, directly upon contact or by redirecting malware to decoy I/O. A key requirement is that they resemble their real counterparts very closely to withstand adversarial probes by threat actors. To be usable, decoy processes need to consume only a small fraction of the resources consumed by their real counterparts. Our contribution in this paper is twofold. We attack the resource utilization consistency of decoy processes provided by a neural network with a heatmap training mechanism, which we find to be insufficiently trained. We then devise machine learning over control flow graphs that improves the heatmap training mechanism. A neural network retrained by our work shows higher accuracy and defeats our attacks without a significant increase in its own resource utilization.

This work proposes a scheme to detect, isolate and mitigate malicious disruption of electro-mechanical processes in legacy PLCs where each PLC works as a finite state machine (FSM) and goes through predefined states depending on the control flow of the programs and input-output mechanism. The scheme generates a group-signature for a particular state combining the signature shares from each of these PLCs using \$(k,\textbackslashtextbackslash l)\$-threshold signature scheme.If some of them are affected by the malicious code, signature can be verified by k out of l uncorrupted PLCs and can be used to detect the corrupted PLCs and the compromised state. We use OpenPLC software to simulate Legacy PLC system on Raspberry Pi and show İ/O\$ pin configuration attack on digital and pulse width modulation (PWM) pins. We describe the protocol using a small prototype of five instances of legacy PLCs simultaneously running on OpenPLC software. We show that when our proposed protocol is deployed, the aforementioned attacks get successfully detected and the controller takes corrective measures. This work has been developed as a part of the problem statement given in the Cyber Security Awareness Week-2017 competition.

The task of attack attribution, i.e., identifying the entity responsible for an attack, is complicated and usually requires the involvement of an experienced security expert. Prior attempts to automate attack attribution apply various machine learning techniques on features extracted from the malware's code and behavior in order to identify other similar malware whose authors are known. However, the same malware can be reused by multiple actors, and the actor who performed an attack using a malware might differ from the malware's author. Moreover, information collected during an incident may contain many clues about the identity of the attacker in addition to the malware used. In this paper, we propose a method of attack attribution based on textual analysis of threat intelligence reports, using state of the art algorithms and models from the fields of machine learning and natural language processing (NLP). We have developed a new text representation algorithm which captures the context of the words and requires minimal feature engineering. Our approach relies on vector space representation of incident reports derived from a small collection of labeled reports and a large corpus of general security literature. Both datasets have been made available to the research community. Experimental results show that the proposed representation can attribute attacks more accurately than the baselines' representations. In addition, we show how the proposed approach can be used to identify novel previously unseen threat actors and identify similarities between known threat actors.

The quantity of Internet of Things (IoT) devices in the marketplace and lack of security is staggering. The interconnectedness of IoT devices has increased the attack surface for hackers. "White Worm" technology has the potential to combat infiltrating malware. Before white worm technology becomes viable, its capabilities must be constrained to specific devices and limited to non-harmful actions. This paper addresses the current problem, international research, and the conflicting interest of individuals, businesses, and governments regarding white worm technology. Proposed is a new perspective on utilizing white worm technology to protect the vulnerability of IoT devices, while overcoming its challenges.

Vehicles are becoming increasingly connected to the outside world. We can connect our devices to the vehicle's infotainment system and internet is being added as a functionality. Therefore, security is a major concern as the attack surface has become much larger than before. Consequently, attackers are creating malware that can infect vehicles and perform life-threatening activities. For example, a malware can compromise vehicle ECUs and cause unexpected consequences. Hence, ensuring the security of connected vehicle software and networks is extremely important to gain consumer confidence and foster the growth of this emerging market. In this paper, we propose a characterization of vehicle malware and a security architecture to protect vehicle from these malware. The architecture uses multiple computational platforms and makes use of the virtualization technique to limit the attack surface. There is a real-time operating system to control critical vehicle functionalities and multiple other operating systems for non-critical functionalities (infotainment, telematics, etc.). The security architecture also describes groups of components for the operating systems to prevent malicious activities and perform policing (monitor, detect, and control). We believe this work will help automakers guard their systems against malware and provide a clear guideline for future research.

Law enforcement agencies regularly take down botnets as the ultimate defense against global malware operations. By arresting malware authors, and simultaneously infiltrating or shutting down a botnet's network infrastructures (such as C2 servers), defenders stop global threats and mitigate pending infections. In this paper, we propose malware tarpits, an orthogonal defense that does not require seizing botnet infrastructures, and at the same time can also be used to slow down malware spreading and infiltrate its monetization techniques. A tarpit is a network service that causes a client to stay busy with a network operation. Our work aims to automatically identify network operations used by malware that will block the malware either forever or for a significant amount of time. We describe how to non-intrusively exploit such tarpit vulnerabilities in malware to slow down or, ideally, even stop malware. Using dynamic malware analysis, we monitor how malware interacts with the POSIX and Winsock socket APIs. From this, we infer network operations that would have blocked when provided certain network inputs. We augment this vulnerability search with an automated generation of tarpits that exploit the identified vulnerabilities. We apply our prototype MALPITY on six popular malware families and discover 12 previously-unknown tarpit vulnerabilities, revealing that all families are susceptible to our defense. We demonstrate how to, e.g., halt Pushdo's DGA-based C2 communication, hinder SalityP2P peers from receiving commands or updates, and stop Bashlite's spreading engine.