Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Signature-based malware detection systems have been a much used response to the pervasive problem of malware. Identification of malware variants is essential to a detection system and is made possible by identifying invariant characteristics in related samples. To classify the packed and polymorphic malware, this paper proposes a novel system, named Malwise, for malware classification using a fast application-level emulator to reverse the code packing transformation, and two flowgraph matching algorithms to perform classification. An exact flowgraph matching algorithm is employed that uses string-based signatures, and is able to detect malware with near real-time performance. Additionally, a more effective approximate flowgraph matching algorithm is proposed that uses the decompilation technique of structuring to generate string-based signatures amenable to the string edit distance. We use real and synthetic malware to demonstrate the effectiveness and efficiency of Malwise. Using more than 15,000 real malware, collected from honeypots, the effectiveness is validated by showing that there is an 88 percent probability that new malware is detected as a variant of existing malware. The efficiency is demonstrated from a smaller sample set of malware where 86 percent of the samples can be classified in under 1.3 seconds.

Wire is a intermediate language to enable static program analysis on low level objects such as native executables. It has practical benefit in analysing the structure and semantics of malware, or for identifying software defects in closed source software. In this paper we describe how an executable program is disassembled and translated to the Wire intermediate language. We define the formal syntax and operational semantics of Wire and discuss our justifications for its language features. We use Wire in our previous work Malwise, a malware variant detection system. We also examine applications for when a formally defined intermediate language is given. Our results include showing the semantic equivalence between obfuscated and non obfuscated code samples. These examples stem from the obfuscations commonly used by malware.

SUMMARY The accuracy of feedback presentation, the sensitivity, and the robustness of reputation evaluation are critical issues to be addressed in a reputation system under the grid environment. This paper proposes a QoS‐based fine‐grained grid reputation system, where economic elements are considered to make the reputation system more sensitive in the commercial grid environments. A novel fine‐grained feedback presentation model based on aggregation of objective QoS attributes and subjective opinions of evaluators is proposed to enable a semi‐automatic, personalized and accurate feedback presentation. Through the introduction of a punishment factor and the adaptive reference of a previous trust value, the proposed reputation system effectively improves the sensitivity of the reputation evaluation. Moreover, the weighted combination of interorganizational trust, direct trust and recommended trust makes the reputation system more robust against collusion attacks. Simulation results show that the proposed reputation system can practically predict feedback and effectively resist malicious attacks such as fake transaction attacks and badmouthing attacks. This provides a clear advantage in the applications of grid service selection. Copyright © 2011 John Wiley & Sons, Ltd.

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Each year, large amounts of money and labor are spent on patching the vulnerabilities in operating systems and various popular software to prevent exploitation by worms. Modeling the propagation process can help us to devise effective strategies against those worms’ spreading. This paper presents a microcosmic analysis of worm propagation procedures. Our proposed model is different from traditional methods and examines deep inside the propagation procedure among nodes in the network by concentrating on the propagation probability and time delay described by a complex matrix. Moreover, since the analysis gives a microcosmic insight into a worm's propagation, the proposed model can avoid errors that are usually concealed in the traditional macroscopic analytical models. The objectives of this paper are to address three practical aspects of preventing worm propagation: (i) where do we patch? (ii) how many nodes do we need to patch? (iii) when do we patch? We implement a series of experiments to evaluate the effects of each major component in our microcosmic model. Based on the results drawn from the experiments, for high-risk vulnerabilities, it is critical that networks reduce the number of vulnerable nodes to below 80%. We believe our microcosmic model can benefit the security industry by allowing them to save significant money in the deployment of their security patching schemes.

Identifying malicious software provides great benefit for distributed and networked systems. Traditional real-time malware detection has relied on using signatures and string matching. However, string signatures ineffectively deal with polymorphic malware variants. Control flow has been proposed as an alternative signature that can be identified across such variants. This paper proposes a novel classification system to detect polymorphic variants using flowgraphs. We propose using an existing heuristic flowgraph matching algorithm to estimate graph isomorphisms. Moreover, we can determine similarity between programs by identifying the underlying isomorphic flowgraphs. A high similarity between the query program and known malware identifies a variant. To demonstrate the effectiveness and efficiency of our flowgraph based classification, we compare it to alternate algorithms, and evaluate the system using real and synthetic malware. The evaluation shows our system accurately detects real malware, performs efficiently, and is scalable. These performance characteristics enable real-time use on an intermediary node such as an Email gateway, or on the endhost.