Blocking API calls for security

The typical user feels comfortable with just having anti-virus software running on their computers. This solution works well if viruses are known and the virus databases are updated frequently. The virus writing community has responded by writing software that mutates the viruses so that these viruses are undetectable to the anti-virus software. Attackers know that anti-virus solutions rely on virus signatures and if the attacker mutates the virus, it will not be detected. Virus writers mutate their product by using software called packers, compressors, and binders. Take an older virus, pass it through one of these mutating programs and now the attacker has a new variant of the old virus that will pass through anti-virus software. A simple example of this type of problem in anti-virus software is the MiniZip worm. A packer called NeoLite was used to create the MiniZip worm, which was a compressed version of the ExploreZip worm. This new variant of the ExploreZip worm spread rapidly even though the original virus was well known. This new variant went completely undetected from the existing anti-virus software. In response to these new threats, a more proactive strategy to counter malicious code must be developed. This thesis focuses on using proactive monitoring techniques to identify code that has malicious intent and to block these operations. To achieve this, the thesis explores digital DNA, system resource monitoring and API monitoring. API monitoring was selected as the method of choice for determining malicious intent. This work discusses different API monitoring technologies, to include: proxy DLL, patching, and binary rewriting. With binary rewriting technology, the author was able to develop a software solution to counter malicious code. As a proof of concept, the author demonstrates his security product monitoring and blocking one of the most costly viruses to date, the I love you Virus

A new approach to malware detection

Malware is a type of malicious programs, and is one of the most common and serious types of attacks on the Internet. Obfuscating transformations have been widely applied by attackers to malware, which makes malware detection become a more challenging issue. There has been extensive research to detect obfuscated malware. A promising research direction uses both control-flow graph and instruction classes of basic blocks as the signature of malware. This research direction is robust against certain obfuscation, such as variable substitution, instruction reordering. But only using instruction classes to detect obfuscated basic blocks will cause high false positives and false negatives. In this thesis, based on the same research direction, we proposed an improved approach to detect obfuscated malware. In addition to using CFG, our approach also uses functionalities of basic block as the signature of malware. Specifically, our contributions are presented as follows: 1) we design "signature calculation algorithm" to extract the signature of a malicious code fragment. "Signature calculation algorithm" is based on compiler optimization algorithm, but add and integrate memory sub-variable optimization, expression formalization and cross basic block propagation into it. 2) we formalize the expressions of assignment statements to facilitate comparing the functionalities of two expressions. 3) we design a detection algorithm to detect whether a program is an obfuscated malware instance. Our detection algorithm compares two aspects: CFG and the functionalities of basic blocks. 4) we implement the proposed approach, and perform experiments to compare our approach and the previous approach

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

Improved Detection for Advanced Polymorphic Malware

Malicious Software (malware) attacks across the internet are increasing at an alarming rate. Cyber-attacks have become increasingly more sophisticated and targeted. These targeted attacks are aimed at compromising networks, stealing personal financial information and removing sensitive data or disrupting operations. Current malware detection approaches work well for previously known signatures. However, malware developers utilize techniques to mutate and change software properties (signatures) to avoid and evade detection. Polymorphic malware is practically undetectable with signature-based defensive technologies. Today’s effective detection rate for polymorphic malware detection ranges from 68.75% to 81.25%. New techniques are needed to improve malware detection rates. Improved detection of polymorphic malware can only be accomplished by extracting features beyond the signature realm. Targeted detection for polymorphic malware must rely upon extracting key features and characteristics for advanced analysis. Traditionally, malware researchers have relied on limited dimensional features such as behavior (dynamic) or source/execution code analysis (static). This study’s focus was to extract and evaluate a limited set of multidimensional topological data in order to improve detection for polymorphic malware. This study used multidimensional analysis (file properties, static and dynamic analysis) with machine learning algorithms to improve malware detection. This research demonstrated improved polymorphic malware detection can be achieved with machine learning. This study conducted a number of experiments using a standard experimental testing protocol. This study utilized three advanced algorithms (Metabagging (MB), Instance Based k-Means (IBk) and Deep Learning Multi-Layer Perceptron) with a limited set of multidimensional data. Experimental results delivered detection results above 99.43%. In addition, the experiments delivered near zero false positives. The study’s approach was based on single case experimental design, a well-accepted protocol for progressive testing. The study constructed a prototype to automate feature extraction, assemble files for analysis, and analyze results through multiple clustering algorithms. The study performed an evaluation of large malware sample datasets to understand effectiveness across a wide range of malware. The study developed an integrated framework which automated feature extraction for multidimensional analysis. The feature extraction framework consisted of four modules: 1) a pre-process module that extracts and generates topological features based on static analysis of machine code and file characteristics, 2) a behavioral analysis module that extracts behavioral characteristics based on file execution (dynamic analysis), 3) an input file construction and submission module, and 4) a machine learning module that employs various advanced algorithms. As with most studies, careful attention was paid to false positive and false negative rates which reduce their overall detection accuracy and effectiveness. This study provided a novel approach to expand the malware body of knowledge and improve the detection for polymorphic malware targeting Microsoft operating systems

Developing a Qualia-Based Multi-Agent Architecture for Use in Malware Detection

Detecting network intruders and malicious software is a significant problem for network administrators and security experts. New threats are emerging at an increasing rate, and current signature and statistics-based techniques are not keeping pace. Intelligent systems that can adapt to new threats are needed to mitigate these new strains of malware as they are released. This research detects malware based on its qualia, or essence rather than its low-level implementation details. By looking for the underlying concepts that make a piece of software malicious, this research avoids the pitfalls of static solutions that focus on predefined bit sequence signatures or anomaly thresholds. This research develops a novel, hierarchical modeling method to represent a computing system and demonstrates the representation’s effectiveness by modeling the Blaster worm. Using Latent Dirichlet Allocation and Support Vector Machines abstract concepts are automatically generated that can be used in the hierarchical model for malware detection. Finally, the research outlines a novel system that uses multiple levels of individual software agents that sharing contextual relationships and information across different levels of abstraction to make decisions. This qualia-based system provides a framework for developing intelligent classification and decision-making systems for a number of application areas

Overcoming the Intuition Wall: Measurement and Analysis in Computer Architecture

These are exciting times for computer architecture research. Today there is significant demand to improve the performance and energy-efficiency of emerging, transformative applications which are being hammered out by the hundreds for new computing platforms and usage models. This booming growth of applications and the variety of programming languages used to create them is challenging our ability as architects to rapidly and rigorously characterize these applications. Concurrently, hardware has become more complex with the emergence of accelerators, multicore systems, and heterogeneity caused by further divergence between processor market segments. No one architect can now understand all the complexities of many systems and reason about the full impact of changes or new applications. To that end, this dissertation presents four case studies in quantitative methods. Each case study attacks a different application and proposes a new measurement or analytical technique. In each case study we find at least one surprising or unintuitive result which would likely not have been found without the application of our method

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

Darwin Turing Dawkins: Building a General Theory of Evolution

Living things, computers, societies, and even books are part of a grand evolutionary struggle to survive. That struggle shapes nature, nations, religions, art, science, and you. What you think, feel, and do is determined by it. Darwinian evolution does not apply solely to the genes that are stored in DNA. Using the insights of Alan Turing and Richard Dawkins, we will see that it also applies to the memes we store in our brains and the information we store in our computers. The next time you run for president, fight a war, or just deal with the ordinary problems humans are heir to, perhaps this book will be of use. If you want to understand why and when you will die, or if you want to achieve greatness this book may help. If you are concerned about where the computer revolution is headed, this book may provide some answers.Comment: 247 page