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Siadati, Hossein, Memon, Nasir.  2017.  Detecting Structurally Anomalous Logins Within Enterprise Networks. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1273–1284.

Many network intrusion detection systems use byte sequences to detect lateral movements that exploit remote vulnerabilities. Attackers bypass such detection by stealing valid credentials and using them to transmit from one computer to another without creating abnormal network traffic. We call this method Credential-based Lateral Movement. To detect this type of lateral movement, we develop the concept of a Network Login Structure that specifies normal logins within a given network. Our method models a network login structure by automatically extracting a collection of login patterns by using a variation of the market-basket algorithm. We then employ an anomaly detection approach to detect malicious logins that are inconsistent with the enterprise network's login structure. Evaluations show that the proposed method is able to detect malicious logins in a real setting. In a simulated attack, our system was able to detect 82% of malicious logins, with a 0.3% false positive rate. We used a real dataset of millions of logins over the course of five months within a global financial company for evaluation of this work.

Halevi, Tzipora, Memon, Nasir, Lewis, James, Kumaraguru, Ponnurangam, Arora, Sumit, Dagar, Nikita, Aloul, Fadi, Chen, Jay.  2016.  Cultural and Psychological Factors in Cyber-security. Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services. :318–324.

Increasing cyber-security presents an ongoing challenge to security professionals. Research continuously suggests that online users are a weak link in information security. This research explores the relationship between cyber-security and cultural, personality and demographic variables. This study was conducted in four different countries and presents a multi-cultural view of cyber-security. In particular, it looks at how behavior, self-efficacy and privacy attitude are affected by culture compared to other psychological and demographics variables (such as gender and computer expertise). It also examines what kind of data people tend to share online and how culture affects these choices. This work supports the idea of developing personality based UI design to increase users' cyber-security. Its results show that certain personality traits affect the user cyber-security related behavior across different cultures, which further reinforces their contribution compared to cultural effects.

Gallagher, Kevin, Patil, Sameer, Dolan-Gavitt, Brendan, McCoy, Damon, Memon, Nasir.  2018.  Peeling the Onion's User Experience Layer: Examining Naturalistic Use of the Tor Browser. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1290–1305.

The strength of an anonymity system depends on the number of users. Therefore, User eXperience (UX) and usability of these systems is of critical importance for boosting adoption and use. To this end, we carried out a study with 19 non-expert participants to investigate how users experience routine Web browsing via the Tor Browser, focusing particularly on encountered problems and frustrations. Using a mixed-methods quantitative and qualitative approach to study one week of naturalistic use of the Tor Browser, we uncovered a variety of UX issues, such as broken Web sites, latency, lack of common browsing conveniences, differential treatment of Tor traffic, incorrect geolocation, operational opacity, etc. We applied this insight to suggest a number of UX improvements that could mitigate the issues and reduce user frustration when using the Tor Browser.

Di Crescenzo, Giovanni, Rajendran, Jeyavijayan, Karri, Ramesh, Memon, Nasir.  2017.  Boolean Circuit Camouflage: Cryptographic Models, Limitations, Provable Results and a Random Oracle Realization. Proceedings of the 2017 Workshop on Attacks and Solutions in Hardware Security. :7–16.

Recent hardware advances, called gate camouflaging, have opened the possibility of protecting integrated circuits against reverse-engineering attacks. In this paper, we investigate the possibility of provably boosting the capability of physical camouflaging of a single Boolean gate into physical camouflaging of a larger Boolean circuit. We first propose rigorous definitions, borrowing approaches from modern cryptography and program obfuscation areas, for circuit camouflage. Informally speaking, gate camouflaging is defined as a transformation of a physical gate that appears to mask the gate to an attacker evaluating the circuit containing this gate. Under this assumption, we formally prove two results: a limitation and a construction. Our limitation result says that there are circuits for which, no matter how many gates we camouflaged, an adversary capable of evaluating the circuit will correctly guess all the camouflaged gates. Our construction result says that if pseudo-random functions exist (a common assumptions in cryptography), a small number of camouflaged gates suffices to: (a) leak no additional information about the camouflaged gates to an adversary evaluating the pseudo-random function circuit; and (b) turn these functions into random oracles. These latter results are the first results on circuit camouflaging provable in a cryptographic model (previously, construction were given under no formal model, and were eventually reverse-engineered, or were argued secure under specific classes of attacks). Our results imply a concrete and provable realization of random oracles, which, even if under a hardware-based assumption, is applicable in many scenarios, including public-key infrastructures. Finding special conditions under which provable realizations of random oracles has been an open problem for many years, since a software only provable implementation of random oracles was proved to be (almost certainly) impossible.