Biblio

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.

Botnets are responsible for many large scale attacks happening on the Internet. Their weak point, which is usually targeted to take down a botnet, is the command and control infrastructure: the foundation for the diffusion of the botmaster's instructions. Hence, botmasters employ stealthy communication methods to remain hidden and retain control of the botnet. Recent research has shown that blockchains can be leveraged for under the radar communication with bots, however these methods incur fees for transaction broadcasting. This paper discusses the use of a novel technology, Whisper, for command and control instruction dissemination. Whisper allows a botmaster to control bots at virtually zero cost, while providing a peer-to-peer communication infrastructure, as well as privacy and encryption as part of its dark communication strategy. It is therefore well suited for bidirectional botnet command and control operations, and creating a botnet that is very difficult to take down.

Botnets have been a major area of concern in the field of cybersecurity. There have been a lot of research works for detection of botnets. However, everyday cybercriminals are coming up with new ideas to counter the well-known detection methods. One such popular method is domain flux-based botnets in which a large number of domain names are produced using domain generation algorithm. In this paper, we have proposed a robust way of detecting DGA-based botnets using few novel features covering both syntactic and semantic viewpoints. We have used Area under ROC curve as our performance metric since it provides comprehensive information about the performance of binary classifiers at various thresholds. Results show that our approach performs significantly better than the baseline approach. Our proposed method can help in detecting established DGA bots (equipped with extensive features) as well as prospective advanced DGA bots imitating real-world domain names.

This article focuses on problems related to detection and prevention of botnet threats in private Internet of Things (IoT) networks. Actual data about IoT botnets activity on the Internet is provided in the paper. Results of analysis of widespread botnets, as well as key characteristics of botnet behavior and activity on IoT devices are also provided. Features of private IoT networks are determined. The paper provides architectural features as well as functioning principles of software systems for botnet prevention in private networks. Recommendations for process of interaction between such system and a user are suggested. Suggestions for future development of the approach are formulated.

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.

Most serious occurrence of modern malware is Botnet. Botnet is a rapidly evolving problem that is still not well understood and studied. One of the main goals for modern network security is to create adequate techniques for the detection and eventual termination of Botnet threats. The article presents an approach for implementing a host-based Intrusion Detection System for Botnet attack detection. The approach is based on a variation of a genetic algorithm to detect anomalies in a case of attacks. An implementation of the approach and experimental results are presented.

Vehicular ad hoc networks (VANETs) are designed to provide traffic safety by enabling vehicles to broadcast information-such as speed, location and heading-through inter-vehicular communications to proactively avoid collisions. However, the attacks targeting these networks might overshadow their advantages if not protected against. One powerful threat against VANETs is vehicular botnets. In our earlier work, we demonstrated several vehicular botnet attacks that can have damaging impacts on the security and privacy of VANETs. In this paper, we present SHIELDNET, the first detection mechanism against vehicular botnets. Similar to the detection approaches against Internet botnets, we target the vehicular botnet communication and use several machine learning techniques to identify vehicular bots. We show via simulation that SHIELDNET can identify 77 percent of the vehicular bots. We propose several improvements on the VANET standards and show that their existing vulnerabilities make an effective defense against vehicular botnets infeasible.

The threats posed by botnets in the cyber-space continues to grow each day and it has become very hard to detect or infiltrate bots given that the botnet developers each day keep changing the propagation and attack techniques. Currently, most of these attacks have been centered on stealing computing energy, theft of personal information and Distributed Denial of Service (DDoS attacks). In this paper, the authors propose a novel technique that uses the Non-Deterministic Polynomial-Time Hardness (NP-Hard Problem) based on the Traveling Salesperson Person (TSP) that depicts that a given bot, bj, is able to visit each host on a network environment, NE, and then it returns to the botmaster in form of instruction(command) through optimal minimization of the hosts that are or may be attacked. Given that bj represents a piece of malicious code and based on TSP-NP Hard Problem which forms part of combinatorial optimization, the authors present an effective approach for the detection of the botnet. It is worth noting that the concentration of this study is basically on the centralized botnet architecture. This holistic approach shows that botnet detection accuracy can be increased with a degree of certainty and potentially decrease the chances of false positives. Nevertheless, a discussion on the possible applicability and implementation has also been given in this paper.

The prevalence of social botnets has increased public distrust of social media networks. Current methods exist for detecting bot activity on Twitter, Reddit, Facebook, and other social media platforms. Most of these detection methods rely upon observing user behavior for a period of time. Unfortunately, the behavior observation period allows time for a botnet to successfully propagate one or many posts before removal. In this paper, we model the post propagation patterns of normal users and social botnets. We prove that a botnet may exploit deterministic propagation actions to elevate a post even with a small botnet population. We propose a probabilistic model which can limit the impact of social media botnets until they can be detected and removed. While our approach maintains expected results for non-coordinated activity, coordinated botnets will be detected before propagation with high probability.

We present a security analysis of several gesture CAPTCHA challenges designed to operate on mobiles. Mobile gesture CAPTCHA challenges utilize the accelerometer and the gyroscope inputs from a mobile to allow a human to solve a simple test by physically manipulating the device. We have evaluated the security of gesture CAPTCHA in mobile devices and found them resistant to a range of common automated attacks. Our study has shown that using an accelerometer and the gyroscope readings as an input to solve the CAPTCHA is difficult for malware, but easy for a real user. Gesture CAPTCHA is effective in differentiating between humans and machines.

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.

IoT devices introduce unprecedented threats into home and professional networks. As they fail to adhere to security best practices, they are broadly exploited by malicious actors to build botnets or steal sensitive information. Their adoption challenges established security standard as classic security measures are often inappropriate to secure them. This is even more problematic in sensitive environments where the presence of insecure IoTs can be exploited to bypass strict security policies. In this paper, we demonstrate an attack against a highly secured network using a Bluetooth smart bulb. This attack allows a malicious actor to take advantage of a smart bulb to exfiltrate data from an air gapped network.

Twitter being the most popular content sharing platform is giving rise to automated accounts called “bots”. Majority of the users on Twitter are bots. Various machine learning (ML) algorithms are designed to detect bots avoiding the vulnerability constraints of ML-based models. This paper contributes to exploit vulnerabilities of machine learning (ML) algorithms through black-box attack. An adversarial text sequence misclassifies the results of deep learning (DL) classifiers for bot detection. Literature shows that ML models are vulnerable to attacks. The aim of this paper is to compromise the accuracy of ML-based bot detection algorithms by replacing original words in tweets with their synonyms. Our results show 7.2% decrease in the accuracy for bot tweets, therefore classifying bot tweets as legitimate tweets.

Consider the problem of guessing a random vector X by submitting queries (guesses) of the form "Is X equal to x?" until an affirmative answer is obtained. A key figure of merit is the number of queries required until the right vector is guessed, termed the guesswork. The goal is to devise a guessing strategy which minimizes a certain guesswork moment. We study a universal, decentralized scenario where the guesser does not know the distribution of X, and is not allowed to prepare a list of words to be guessed in advance, or to remember its past guesses. Such a scenario is useful, for example, if bots within a Botnet carry out a brute-force attack to guess a password or decrypt a message, yet cannot coordinate the guesses or even know how many bots actually participate in the attack. We devise universal decentralized guessing strategies, first, for memoryless sources, and then generalize them to finite-state sources. For both, we derive the guessing exponent and prove its asymptotic optimality by deriving a matching converse. The strategies are based on randomized guessing using a universal distribution. We also extend the results to guessing with side information (SI). Finally, we design simple algorithms for sampling from the universal distributions.

Logical locking is the most popular countermeasure against the hardware attacks like intellectual property (IP) piracy, Trojan insertion and illegal integrated circuit (IC) overproduction. The functionality of the design is locked by the added logics into the design. Thus, the design is accessible only to the authorized users by applying the valid keys. However, extracting the secret key of the logically locked design have become an extensive effort and it is commonly known as key guessing attacks. Thus, the main objective of the proposed technique is to build a secured hardware against attacks like Brute force attack, Hill climbing attack and path sensitization attacks. Furthermore, the gates with low observability are chosen for encryption, this is to obtain an optimal output corruption of 50% Hamming distance with minimal design overhead and implementation complexity. The experimental results are validated on ISCAS'85 benchmark circuits, with a highly secured locking mechanism.

Nowadays, computers are used everywhere to carry out daily routine tasks. The input devices i.e. keyboard or mouse are used to feed input to computers. The surveillance of input devices is much important as monitoring the users logging activity. A keylogger also referred as a keystroke logger, is a software or hardware device which monitors every keystroke typed by a user. Keylogger runs in the background that user cannot identify its presence. It can be used as monitoring software for parents to keep an eye on children activity on computers and for the owner to monitor their employees. A keylogger (which can be either spyware or software) is a kind of surveillance software that has the ability to store every keystroke in a log file. It is very dangerous for those systems which use their system for daily transaction purpose i.e. Online Banking Systems. A keylogger is a tool, made to save all the keystroke generated through the machine which sanctions hackers to steal sensitive information without user's intention. Privileged also relies on the access for both implementation and placement by Kernel keylogger, the entire message transmitted from the keyboard drivers, while the programmer simply relies on kernel level facilities that interrupt. This certainly needs a large power and expertise for real and error-free execution. However, it has been observed that 90% of the current keyloggers are running in userspace so they do not need any permission for execution. Our aim is focused on detecting userspace keylogger. Our intention is to forbid userspace keylogger from stealing confidential data and information. For this purpose, we use a strategy which is clearly based on detection manner techniques for userspace keyloggers, an essential category of malware packages. We intend to achieve this goal by matching I/O of all processes with some simulated activity of the user, and we assert detection in case the two are highly correlated. The rationale behind this is that the more powerful stream of keystrokes, the more I/O operations are required by the keylogger to log the keystrokes into the file.

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 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.