Malware "Ecology" Viewed as Ecological Succession: Historical Trends and Future Prospects

The development and evolution of malware including computer viruses, worms, and trojan horses, is shown to be closely analogous to the process of community succession long recognized in ecology. In particular, both changes in the overall environment by external disturbances, as well as, feedback effects from malware competition and antivirus coevolution have driven community succession and the development of different types of malware with varying modes of transmission and adaptability.Comment: 13 pages, 3 figure

Malware: the never-ending arm race

"Antivirus is death"' and probably every detection system that focuses on a single strategy for indicators of compromise. This famous quote that Brian Dye --Symantec's senior vice president-- stated in 2014 is the best representation of the current situation with malware detection and mitigation. Concealment strategies evolved significantly during the last years, not just like the classical ones based on polimorphic and metamorphic methodologies, which killed the signature-based detection that antiviruses use, but also the capabilities to fileless malware, i.e. malware only resident in volatile memory that makes every disk analysis senseless. This review provides a historical background of different concealment strategies introduced to protect malicious --and not necessarily malicious-- software from different detection or analysis techniques. It will cover binary, static and dynamic analysis, and also new strategies based on machine learning from both perspectives, the attackers and the defenders

Selecting Root Exploit Features Using Flying Animal-Inspired Decision

Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection

Crystal Ball: From Innovative Attacks to Attack Effectiveness Classifier

Android OS is one of the most popular operating systems worldwide, making it a desirable target for malware attacks. Some of the latest and most important defensive systems are based on machine learning (ML) and cybercriminals continuously search for ways to overcome the barriers posed by these systems. Thus, the focus of this work is on evasion attacks in the attempt to show the weaknesses of state of the art research and how more resilient systems can be built. Evasion attacks consist of manipulating either the actual malicious application (problem-based) or its extracted feature vector (feature-based), to avoid being detected by ML systems. This study presents a set of innovative problem-based evasion attacks against well-known Android malware detection systems, which decrease their detection rate by up to 97%. Moreover, an analysis of the effectiveness of these attacks against VirusTotal (VT) scanners was conducted, empirically showing their efficiency against well-known scanners (e.g., McAfee and Comodo) as well. The VT system proved to be a great candidate for the attacks, as in 98% of the apps, less scanners detected the manipulated apps than the original malicious apps. As not all the attacks are effective in the same manner against the VT scanners, the attack efficiency classifiers are advised. Each classifier predicts the applicability of one of the attacks. The set of classifiers creates an ensemble, which shows high success rates, allowing the attacker to decide which attack is best to use for each malicious app and defense system

Hijacking User Uploads to Online Persistent Data Repositories for Covert Data Exfiltration

As malware has evolved over the years, it has gone from harmless programs that copy themselves into other executables to modern day botnets that perform bank fraud and identity theft. Modern malware often has a need to communicate back to the author, or other machines that are also infected. Several techniques for transmitting this data covertly have been developed over the years which vary significantly in their level of sophistication. This research creates a new covert channel technique for stealing information from a network by piggybacking on user-generated network traffic. Specifically, steganography drop boxes and passive covert channels are merged to create a novel covert data exfiltration technique. This technique revolves around altering user supplied data being uploaded to online repositories such as image hosting websites. It specifically targets devices that are often used to generate and upload content to the Internet, such as smartphones. The reliability of this technique is tested by creating a simulated version of Flickr as well as simulating how smartphone users interact with the service. Two different algorithms for recovering the exfiltrated data are compared. The results show a clear improvement for algorithms that are user-aware. The results continue on to compare performance for varying rates of infection of mobile devices and show that performance is proportional to the infection rate

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have