No description available.

No description available.

No description available.

No description available.

No description available.

No / Developing advanced and efficient malware detection systems is becoming significant in light of the growing threat landscape in cybersecurity. This work aims to tackle the enduring problem of identifying malware and protecting digital assets from cyber-attacks. Conventional methods frequently prove ineffective in adjusting to the ever-evolving field of harmful activity. As such, novel approaches that improve precision while simultaneously taking into account the ever-changing landscape of modern cybersecurity problems are needed. To address this problem this research focuses on the detection of malware in network traffic. This work proposes a machine-learning-based approach for malware detection, with particular attention to the Random Forest (RF), Support Vector Machine (SVM), and Adaboost algorithms. In this paper, the model’s performance was evaluated using an assessment matrix. Included the Accuracy (AC) for overall performance, Precision (PC) for positive predicted values, Recall Score (RS) for genuine positives, and the F1 Score (SC) for a balanced viewpoint. A performance comparison has been performed and the results reveal that the built model utilizing Adaboost has the best performance. The TPR for the three classifiers performs over 97% and the FPR performs < 4% for each of the classifiers. The created model in this paper has the potential to help organizations or experts anticipate and handle malware. The proposed model can be used to make forecasts and provide management solutions in the network’s everyday operational activities.

In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware analysis, static binary analysis techniques have become increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In prior art, a number of clustering and classification techniques have been used to classify the malwares into families and to also identify new malware families, from the behavior reports. In this thesis, we have analysed in detail about the use of Profile Hidden Markov models for the problem of malware classification and clustering. The advantage of building accurate models with limited examples is very helpful in early detection and modeling of malware families. The thesis also revisits the learning setting of an Intrusion Detection System that employs machine learning for identifying attacks and normal traffic. It substantiates the suitability of incremental learning setting(or stream based learning setting) for the problem of learning attack patterns in IDS, when large volume of data arrive in a stream. Related to the above problem, an elaborate survey of the IDS that use data mining and machine learning was done. Experimental evaluation and comparison show that in terms of speed and accuracy, the stream based algorithms perform very well as large volumes of data are presented for classification as attack or non-attack patterns. The possibilities for using stream algorithms in different problems in security is elucidated in conclusion.

Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking in people's computers with 99.61% true positive rate at 0.01% false positive rate; our application of ADAGE on malware detection (patent-pending) enables to detect malware in a streaming setting; and EDOCS (patent-pending) flags comment spammers among 197 thousand users on a social media platform accurately and preemptively. (2) Graph-induced Behavior Characterization: We derive new insights and knowledge that characterize certain behavior from graphs using statistical and algorithmic techniques. We present two examples: a study on identifying attributes of smoking and drinking abstinence and relapse from an addiction cessation social media community; and an exploratory analysis of how company insiders trade. Our work has already made impact to society: deployed by Symantec, AESOP is protecting over 120 million people worldwide from malware; EDOCS has been deployed by Yahoo and it guards multiple online communities from comment spammers.

No description available.

Worldwide computer systems continue to execute malicious software that degrades the systemsâ performance and consumes network capacity by generating high volumes of unwanted traffic. Network-based detectors can effectively identify machines participating in the ongoing attacks by monitoring the traffic to and from the systems. But, network detection alone is not enough; it does not improve the operation of the Internet or the health of other machines connected to the network. We must identify malicious code running on infected systems, participating in global attack networks. This dissertation describes a robust and secure approach that identifies malware present on infected systems based on its undesirable use of network. Our approach, using virtualization, attributes malicious traffic to host-level processes responsible for the traffic. The attribution identifies on-host processes, but malware instances often exhibit parasitic behaviors to subvert the execution of benign processes. We then augment the attribution software with a host-level monitor that detects parasitic behaviors occurring at the user- and kernel-level. User-level parasitic attack detection happens via the system-call interface because it is a non-bypassable interface for user-level processes. Due to the unavailability of one such interface inside the kernel for drivers, we create a new driver monitoring interface inside the kernel to detect parasitic attacks occurring through this interface. Our attribution software relies on a guest kernelâ s data to identify on-host processes. To allow secure attribution, we prevent illegal modifications of critical kernel data from kernel-level malware. Together, our contributions produce a unified research outcome --an improved malicious code identification system for user- and kernel-level malware.